esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 3 Results
### Sensitivity of Noise Subtraction for Targets with Low SNR
A hypothetical noise shift of 0.1 dB almost vanishes the \(\beta^{0}\) difference in this case if S-1A noise increases or S-1B noise decreases. In this case the black line is almost reached. In the opposite case the discrepancy rises up to about 1.2 dB. The high noise dependency on the S1A/S1B difference is only visible for low SNR. If higher SNRs or lower absolute noise values are considered, the circle will move towards the lower left corner of the figure. In this region the colored lines are more separated from each other, i.e., a noise variation has a much lower impact on the resulting S1A/S1B difference. However, for comparing noise subtracted backscatter for low SNR, the noise estimation has to be very accurate. In the presented case, an error within the noise estimation of 0.1 dB explains a difference of noise subtracted backscatter between S-1A and S-1B of 0.65 dB.
## 1 Introduction
[24] conducted studies in which ceramic inserts made from alumina (Al-Oxide), mixed oxide, and silicon nitride were used for turning Inconel 718 alloys with three different hardness levels (26 HRC, 35 HRC, and 45 HRC). They observed that the hardness of the material being machined affects the surface quality. The surface of the sample with the highest hardness characterized lower roughness values, while machining the material with the lowest hardness resulted in the lowest abrasive wear on the flank face. The same team also investigated the effect of cutting tool temperature on crater wear [25]. The studies were conducted using ceramic inserts made from alumina (Al-Oxide), mixed oxide, and silicon nitride without using any coolant. They observed a monotonic increase in cutting temperature with increasing cutting speed. The authors recorded that the ceramic insert made from alumina exhibited the lowest crater wear and the best cutting performance, regardless of the hardness of the material being machined or the cutting speed. [PERSON] et al. [26] studied the impact of cutting inserts on the machined surface layer. Based on their findings, they concluded that the type and size of damage to the machined surface depend on the cutting parameters, tool wear, and cooling conditions. They observed damage in the form of micro-cracking, plastic flow, cavities, side flow, and buildup on the machined surface. The reviewed research shows that there is a significant number of studies focused on the application of cutting inserts made of sintered carbides and ceramics for machining nickel-based alloys. Sintered carbide inserts are mostly studied for their use in finish machining, while ceramic inserts are considered useful for rough machining, particularly for high-rigidity components where the surface quality requirements are not as high. A research gap exists in the application of cutting inserts made from these materials under the same machining conditions (cooling and lubrication) and for the same cutting parameters. The conducted research aims to fill this gap. The manufacturing of ceramic cutting inserts with the same cutting edge geometry as sintered carbide inserts creates an opportunity to carry out finish turning experiments with these inserts. The experiments were carried out for both types of tool materials using the same cutting parameters, as well as cutting speeds recommended by tool manufacturers for each specific tool material.
# Numerical Simulation of the Force Acting on the Riser by Two Internal Solitary Waves
## 2 Materials and Methods
### Geometric Model and Mesh Parameters  
In internal solitary wave research, the commonly used simplification method involves simplifying seawater into two layers of fluid. The ocean, with density a thermocline, is regarded as two layers with uniform density, and a sudden density change can be observed between layers [48; 49]. The two-layer density model assumes that the two-phase fluids are impermeable, non-interfering, and incompatible with each other. For this study, the numerical simulation domain of internal solitary waves was 6 m long, 0.35 m wide, and 0.57 m high. According to the two-layer density model, the calculation domain is divided into upper and lower layers. The height of the upper water body was \(h_{1}=0.095\) m and the density was \(\rho_{1}=999\) kg/m\({}^{3}\); for the lower water body, \(h_{2}=0.475\) m and \(\rho_{2}=1024\) kg/m\({}^{3}\). The dynamic viscosity coefficient for the two water layers was 1.003 \(\times\) 10\({}^{-6}\) pa-s, while the riser diameter was 8 cm. The center of the riser was 3 m from the left end of the calculation domain and 0.175 m from the front and back of the calculation domain. Figure 1 shows the specific structure.  
To avoid the interference of grid size to the numerical simulation results, the grid independence needs to be verified first. Figure 2 shows the accuracy of locally encrypted mesh in the computational domain under three different conditions. The mesh generation uses cutting volume mesh and surface reconstruction. Table 1 shows the grid size, and the overall grid number under three conditions, where \(\Delta\)x, \(\Delta\)y, and \(\Delta\)z represent the grid size in x, y, and z directions, respectively, and N represents the total number of flow field element grids. As shown in Figure 3, there was considerable difference in waveform change under sparse precision, while in the medium and dense precisions, the differences were small. Therefore, the medium grid precision was selected in this study.  
Figure 1: Schematic diagram of numerical water tank.
# SNR Enhancement of an Electrically Small Antenna Using a Non-Foster Matching Circuit
## 3 Fabrication and Measurement of Non-Foster Circuit
### Measurement of Reflection Coefficient  
Figure 8a is a picture of the small monopole and NFC assembly. A network analyzer (Anritsu MCS2038C) was connected to the input port of the NFC to measure the S\({}_{11}\). The measured S\({}_{11}\) is shown in Figure 8b. The comparison made here is with and without the NFC. The latter means the monopole was directly connected to the network analyzer. As observed, the measured S\({}_{11}\) without the NFC is higher than -2 dB over the whole frequency range, 50-500 MHz. This is due to the high capacitance preventing the monopole from reaching the resonance as described in Section 2.1. Contrarily, the S\({}_{11}<-10\) dB bandwidth is 60-400 MHz when the NFC is connected to the monopole. This proves that the NFC successfully removes the positive capacitance of the monopole by generating a sufficient amount of negative capacitance.  
Figure 6: The transient responses of non-Foster circuits: (**a**) Examples of stable and unstable transient responses. (**b**) The transient response of the proposed non-Foster circuit.  
Figure 7: The fabricated non-Foster circuit prototype.
# Fault Diagnosis via Neural Ordinary Differential Equations
## 6 Discussion  
Training methodology of DL structures is vital to reach models with high performance. A review of the literature demonstrates that training can be very demanding for DL models. In [43], a common procedure for fault detection using DL models is proposed, feature extraction and neural network type are regarded as two key elements of the process. In [44] an autoencoder is used with 4 different and selectable behaviors. In [21], the training is divided into many steps in accordance with the aging of the system. In [20], the training is performed using ensemble learning, which uses multiple learners. In [25], a feature extraction and a dimensionality reduction are performed previously over the data-set.  
Figure 14: Phase diagram during a fault in sensor.  
Figure 13: Phase diagram during fault in actuator.  
In [24], a group of 14 features is extracted and then selected using a criterion before the training. All these approaches shown the complexity that features selection requires.  
Our approach aims to alleviate these complicated procedures with the inclusion of some characteristics of the model dynamics to be learned by the Neural ODE. We incorporated in the training an approximation of the system using features in form of the measurement equations and inputs of the process. Those characteristics allow us to reduce the epochs and complexity and furthermore to work with data-series in a natural manner, as was demonstrated in Section 4.  
Some final tests were performed using classical SVM, ANN, and DL models with the data-set of actuator faults, Table 2 shows the results for each model tested, we include weighted accuracy as a measure for accuracy considering weights for each class, class 0 for no fault is weighted 0.08 and other classes are weighted 0.95, 0.96 and 0.99, respectively. Additionally, in all the deep learning methods, except for the Neural ODE method, data augmentation is previously performed using noise injection, dropout, pooling, and time-warping [45]; and batch normalization is included for the fully connected layers. It is noticeable from the results that the Neural ODE model allows improving the efficiency of the network for this case.  
From Table 2, the SVM method reaches a high accuracy for this case, but it is clearly due to false negatives, as is appreciated in its corresponding weighted accuracy. The ANN and DL models had a lower accuracy; hence, it is likely required a more advanced pre-processing of the data-set to reach better results. Meanwhile, the use of neural ODE and dense network, proposed in this work, shows that, even excluding pre-processing and data augmentation, the model can achieve higher accuracies.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 6 Conclusions  
A new forest fire detection approach based on STM has been described in this paper. The major novelties of this method lie in considering the correlations between the inspection pixel and its neighboring pixels and updating the spatial relationship defined in the model over time. STM can mitigate the adverse impacts of spatial heterogeneity on the prediction of background intensities by using the strong correlations between pixels. What is more, the integration of spatial contextual information and temporal information makes it a more robust model for anomaly detection.  
To evaluate the performance of the proposed method, a case study was conducted to detect fire pixels in the Yinanhe forest. The results indicated that the STM method was capable of detecting forest fire pixels with a producer's accuracy of 90.09% and a user's accuracy of 94.45% when applied to HJ-IRS images. The STM method was also compared with the contextual algorithm. The comparison results suggested that the STM method performed better for background BT prediction and achieved comparable detection accuracies. In addition, the STM method was also more robust in terms of the selection of the window size.  
However, the STM method was only tested during the fire season. Its performance throughout the year has not been verified. To improve the validation process, future work should be dedicated to conducting careful and quantitative validations of the STM method in different regions and seasons.  
This work is jointly supported by the National Science and Technology Major Project (30-Y20A03-9003-15/16), the National Natural Science Foundation of China (Grant No. 41401474 and No. 41301393) and Young Scholars of Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (Y5 SJ1400 CX).  
[PERSON] had the original idea for the study and wrote the original manuscript. [PERSON] and [PERSON] supervised the research and reviewed and edited the manuscript. [PERSON] and [PERSON] were responsible for the manuscript revisions. [PERSON] and [PERSON] offered valuable comments on the manuscript. [PERSON] was responsible for the validation data collection and examination. All the authors read and approved the final manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} STM & Spatio-temporal model \\ MWIR & midwave infrared \\ LWIR & longwave infrared \\ NIR & near infrared \\ SWIR & shortwave infrared \\ BT & brightness temperature \\ SEVIR & Spinning Enhanced Visible and Infrared Imager \\ DDM & Dynamic Detection Model \\ RST & robust satellite technique \\ ALICE & Absolute Local Index of Change of the Environment \\ MODIS & Moderate Resolution Imaging Spectroradiometer \\ CCD & charge-coupled device \\ IRS & infrared camera sensor \\ HJ-1 & Environment and Disaster Monitoring and Forecasting with a Small Satellite Constellation \\ \end{tabular}
# Improving RF Fingerprinting Methods by Means of D2D Communication Protocol
## 3 Material and Methods
### RF Fingerprint Positioning  
RF fingerprint positioning refers to database matching or correlation method where UE's position can be estimated by comparing UE's RF fingerprint with a database previously recorded. Each reference (or training) fingerprint in the database is associated to a known location, which commonly corresponds to a local coordinate (\(X\), \(Y\)), or a global coordinate (latitude, longitude), or a label (e.g., _Main Corridor_). Generally, the fingerprint database is built by site surveying, crowdsourcing or by other means. Different distance measurement metrics, e.g., Jaccard, Manhattan, Minkowski, etc., can be used to determine the signal distance between training and testing fingerprint [37; 38]. Figure 1 depicts the general concept of RF fingerprint positioning method.  
We have used Euclidean distance throughout the research since it is the most commonly used distance measure used in the current literature. If \(P\) and \(Q\) are two RSS vectors of test and training fingerprint respectively, the signal distance between them can be calculated as follows:  
\[distance_{euclidean}(P,Q)=\sqrt[2]{\sum\limits_{i=1}^{n}{{{({P_{i}}-{Q_{i}})}^ {2}}}} \tag{1}\]  
Here \(n\) is the length of the RSS vectors. Generally, the signal distance between the test fingerprint and all the training fingerprints are calculated. The test fingerprint's coordinate is estimated by computing the coordinates of the \(k\) reference/training fingerprints reporting the shortest signal distance. In the most simple case, the coordinate corresponding to the reference fingerprint reporting the lowest signal distance.
# A Comprehensive Evaluation Algorithm of Multi-Point Relay Based on Link-State Awareness for UANETs
## 2 Related Works
### Analysis of MPR Selection Issues  
The measurement for selecting MPR nodes is single, and even when considering multiple factors, the probability of all factors being good is very low. Therefore, it is necessary to integrate multiple factors.  
In the process of selecting MPR nodes, there are nodes with the same initial coverage and current coverage. In this case, a node is randomly selected as the MPR node. If MPRs are selected based on coverage, as shown in Figure 3, there will be two results: {1, 2, 4} and {1, 3, 4}, from which a selection will be made randomly. When the mobility of node 3 is too fast, it is not within the communication range of node 0 at a certain moment, causing link breakage between them. Meanwhile, compared with node 3, if the link duration of node  
Figure 1: The comparison of data transmission links under the flooding mechanism and MPR mechanism. (**a**) Diagram of data transmission link under the flooding mechanism; (**b**) schematic diagram of data transmission link under MPR mechanism.  
Figure 2: The traditional process of selecting MPR set for node S.  
2 is longer and the link stability is better, then the former reflects a better effect. If a large amount of packets are received at a certain moment resulting in the buffer queue to be full, node 2 can be unable to continue receiving and only discard the packets arriving at the next moment, which will cause packets loss. Considering the load situation of node 2, the latter has a better effect.  
Relying solely on single indicators such as node coverage, node mobility, and node load to select MPR nodes is unreasonable [31]. The above examples illustrate that if multiple factors are not comprehensively considered, it will not only affect the selection of MPR nodes, but also disrupt normal communication and data transmission in the network, leading to poor network robustness and decreased overall network performance.
## 2 Materials and Methods
### Assessments of Fruit Quality Characteristics
#### 2.4.3 Phenolic Compound Concentration and Antioxidant Capacity Determination  
Approximately 2.0 g of the frozen apricot pulp was extracted with 5 mL 100% methanol for 30 min at 40 \({}^{\circ}\)C under periodical stirring and centrifuged at 4000\(\times\)\(g\) for 6 min. The supernatant was transferred to a new tube, and the pellet was re-extracted with methanol under the same conditions. After the second centrifugation, the two supernatants were combined and used to measure the concentration of total phenols, total o-diphenyl, total flavonoids, and total flavonoids, according to [PERSON] et al. [22], and the results were expressed as mg equivalent gallic acid (GAE) (total phenols), caffe acid (CAE) (total o-diphenyl), catechin (CtE) (total flavonoids), and catechin (CtE) (total flavonoids). Briefly, for the total phenols determination, 50 mL of the supernatant was added to 3.95 mL distilled water, and the solution was vortexed. Afterward, 250 mL of Folin-Ciocalteu reagent was added and vortexed, and then 750 mL of saturated Na\({}_{2}\)CO\({}_{3}\) solution and the solution remained at room temperature for 2 h, after which time the absorbance was measured at 760 nm. For the determination of o-diphenyl, 100 mL of the supernatant was added to 900 mL water, vortexed, and then 1 mL potassium phosphate buffer (0.1 M pH 5.8) was added, followed by 2 mL of a 5% (\(w/v\)) solution of Na\({}_{2}\)MoO\({}_{4}\) 5H\({}_{2}\)O. The absorbance was measured after 15 min at 370 nm. For the total flavonoids determination, 500 mL of the supernatant was added to 2 mL water, followed by 0.15 mL NaNO\({}_{2}\) (5% \(w/v\) in water), and after 5 min, 0.15 mL of AlCl\({}_{3}\) (10% \(w/v\) in water) were added. After six minutes, 1 mL of 1 N NaOH was added, followed by 1.2 mL of water, and the absorbance was measured at 510 nm. For the total flavanols determination, 200 mL supernatant was added to 1 mL chromogen reagent (0.1% (\(w/v\)) of 4-dimethylaminocinnamaldehyde in 1 N HCl in methanol), the solution was vortexed, and the absorbance was measured after 10 min at 640 nm.  
The antioxidant capacity of the pulp was assessed in the same methanolic extract of phenolic compound extraction, based on the DPPH (2,2-diphenyl-1-picryl hydrazyl) and FRAP (ferric-reducing antioxidant power) assays as described by [PERSON] et al. [22], and are both expressed in umol Trolox equivalents (TE) per g fresh weight. Briefly, 0.1 mL of the supernatant was added to 2 mL of 0.1 mM DPPH solution (freshly prepared each time), and after 15 min in the dark, the absorbance was measured at 517 nm. For the FRAP assay, 50 mL of supernatant was added to 1.5 mL of FRAP reagent previously equilibrated at 37 \({}^{\circ}\)C. The solution remained for 30 min at 37 \({}^{\circ}\)C in a water bath, and then the absorbance was measured at 593 nm.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 5 Feature Extraction  
This section proposes a novel feature extraction method for classifying malicious MQTT traffic. The features are the source port index, TCP length, MQTT message type, keep alive, and connection ACK.
## 3 Neighbor Verification through Low-Power Beacons  
The core idea of our proposal builds on the physical communication constraint that messages from neighbor vehicles transmitted at a small transmit (Tx) power reach only the immediate proximity of the sender [29]. It contrasts with existing communication-based approaches that exploit the maximum communication range (e.g., 300 m) of the attacker as the constraint to be checked against [6; 17]. In this section, we discuss our proposal to use low-power beaconing for proximity proving purpose, in order to defend against position data forging attack from stationary roadside attackers. The proposed scheme has a few desirable properties. First, it does not require a new hardware component beyond existing wireless access in vehicular environment (WAVE) on-board unit (OBU). Second, it does not excessively increase channel utilization as to hamper normal beaconing activities using BSMs [3]. Third, it works where sensor fusion is not applicable, and against a stationary attacker to which the behavior-based checking is not applicable. Below, we sketch the solution approach.
## 4 Conclusions  
Our data show that in unilateral posterior crossbite, the contralateral Masseter muscle exhibits an increased expression of collagen IV, laminin, and MMP9 if compared to the ipsilateral one, although the ipsilateral muscle shows the expression of all tested proteins. That led us to hypothesise that the observed increased expression in contralateral side could be correlated to a hypertrophic response of this muscle in accordance with our previous results. The ECM behaviour of ipsilateral side, instead, is not representative of a hypotrophic/atrophic response. This suggest that during unilateral posterior crossbite, the first response is represented by hypertrophy of contralateral muscle, probably in order to balance the low activity of the affected side. However, we cannot exclude the possibility that the ipsilateral side may experience hypotrophy/atrophy if the crossbite is not corrected.  
G.V.: Data curation-Writing original draft; A.C.: Methodology-Validation; M.G.P.: Project Administration-Validation; M.C.M.: Investigation-Resources; M.R.A.: Methodology-Visualization; G.P.: Visualization-Software. G.C.: Validation and Writing, Review, Editing. All authors have read and agreed to the published version of the manuscript.  
No Funding.  
**Institutional Review Board Statement:** Ethics committee approval: \"Morphological and molecular evaluation of the masseter muscle in orthognathic surgical patients\" approved on 18 June 2018 protocol n.0063261, document number CS2/372 of \"Comitato Etico Interaziondale A.O.U. Citta della Salute e della Scienza di Torino-A.O. Ordine Mauriziano-A.S.L. Citta di Torino\".  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: The graphic shows the means of fluorescence intensity for each tested proteins both in the contralateral and the ipsilateral muscles. The means were obtained by the function “Histo” of confocal laser microscope. Laminin, Collagen IV, and MMP-9 are more expressed in the contralateral muscle than in the ipsilateral one. For the MMP-2 the values for contralateral and ipsilateral muscles are close to each other.
# An RF-MEMS-Based Digitally Tunable SIW Filter in X-Band for Communication Satellite Applications
## 3 Tunable Filter Design  
### Specifications and SIW Filter Design  
In the context of communication satellite application, one possible targeted digitally tunable two-state filter should be able to discretely control a bandwidth of 250 MHz, with a central frequency ranging from 10.825 GHz to 11.075 GHz, through 250 MHz steps.  
The design here of a two-state tunable filter electrically controlled using RF-MEMS fits the above specifications.  
First, this required the design of a simple fourth-order Chebyshev SIW bandpass filter. SIW band-pass filters have been broadly studied in the literature [10] and the design procedure is quite similar to that of a conventional rectangular waveguide filter. The circuit was realized on a 635 \(\upmu\)m thick alumina substrate (\(\varepsilon\)r = 9.9, \(\text{tg}\delta=3\times 10^{-4}\)) and was fed by a tapered microstrip line.