esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results: Predicted GNSS-R Altimetry Performance
### GNSS-R Altimetry Performance Sensitivity to Errors  
Since both, single frequency or dual-frequency altimetry estimates depend on the bandwidth and the SNR, the sensitivity to any error source computed as the derivative of the rms altimetry error at a given frequency band (\(\sigma_{f}\)) with respect to a given parameter (\(P\)), \(\partial\sigma_{f}/\partial P\), reduces to the calculation of the sensitivity to the signal and the noise powers, whose main drivers are the antenna directivity and the receivers' noise figure, respectively. Since the sensitivities exhibit a highly non-linear behavior, it is not possible to provide a single numerical value. Therefore, the sensitivities to the antenna pattern directivity (Figure 15) and the receivers' noise figure (Figure 16) are presented here. Please note that the parameters presented are the derivatives of the RMS error, so when it is negative, it means that the error decreases, and the other way around. It is clear the significant degradation (increase) of the RMSE, especially at the swath edges, because of the antenna directivity drop.  
If the directivity increases, so does the \(SNR\), then the sensitivity to errors is much smaller. A last comment is illustrated in Figure 17. Comparing it with Figure 15, it can be seen that if the transmitted power is just 3 dB more, then the sensitivity to the directivity dramatically decreases, since the SNR for the iGNSS-R does not increase any further.  
Figure 16: RMS height error sensitivity to receivers noise figure [dB]; errors (P) at nominal values (NF\({}_{\rm nom}\) = 3.5 dB), u = [dB].  
Figure 15: RMS height error sensitivity to antenna directivity (P) at nominal values as given by Table 3, u = [dB].  
Other influencing factors are the antenna losses, the beamformer amplitude errors, the array phase center, the number of bits in the phase shifter, the group delay error, the bandwidth rms mismatch, the number of bits of the correlator, the Delay Refresh Rate Update (DeRRU), and the Doppler Refresh Rate Update (DoRRU). The first three have an evident impact in the _SNR_ before correlation. The others as well, but their impact is much more moderate, as compared to the antenna directivity and receivers' noise figure.
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 2 Radar Architecture Design
### SIMO Antenna Layout Design  
The radar's field of view (FOV) \(\mathrm{\theta_{max}}=\mathrm{arcsin}(\lambda/2\mathrm{d})\) and angular resolution \(\Delta\theta=\lambda/\mathrm{Dcos}(\theta)\) depend on the antenna spacing \(\mathrm{d}\) and the total array length \(\mathrm{D}\). To facilitate clearance coverage and radar miniaturization, an FOV range of \(\pm 15^{\circ}\) with an antenna spacing of 7.5 mm is an optimal configuration. Because of the weak SNR caused by the small-RCS blade oblique incidence from more than 70 m, blade clearance detection requires a high-P\({}_{t}\) radar configuration rather than MIMO with a higher azimuth resolution. Therefore, combining three transmitting channels into one, namely SIMO configuration, is a good choice to acquire high \(\mathrm{P_{t}}\). To compensate for the lower angular resolution caused by fewer antenna channels, as shown in Figure 4, the linear array antenna can be altered to a [0 3 4 6] non-linear array to expand the total array length \(\mathrm{D}\), and the rest of the antennas are grounded. Figure 5 describes the normalized directional diagram of the antenna beam synthesis for linear and [0 3 4 6] non-linear arrays at the same spacing \(\mathrm{d}\). In the form of a non-uniform array layout, the receiving antenna aperture of the radar is increased, and the 3 dB beam width is improved from 4.7\({}^{\circ}\) to 3\({}^{\circ}\), indicating that the angular resolution of the radar has been improved by 1.7\({}^{\circ}\).  
Figure 4: [0 3 4 6] Non-linear antenna array.
# Aircraft Target Interpretation Based on SAR Images
## 4 Experimental Results and Analysis  
### Experimental Environment  
The datasets used in this paper are from the 2021 GF challenge on Automated High-Resolution Earth Observation Image Interpretation [35]. These images in our datasets were collected from the GF-3 satellite, which is single polarization (HH) and spotlight imaging mode with 1m spatial resolution. The scene includes multi-temporal images of several common airports around the world. The datasets contain 2000 SAR images ranging in size from 600 \(\times\) 600 pixels to 2048 \(\times\) 2048 pixels and 6556 aircraft instances; there are seven different types of aircraft, as shown in Figure 8 [36]. The datasets are divided into the training set, validation set, and test set according to the proportion of 8:1:1.  
The deep learning program runs on a 64-bit Windows 10 computer system, and Table 2 shows the software environment configuration. The number of training iterations is 200, the batch size is 32, and the initial learning rate is 0.01.
## 4 Extension of Traditional Impedance Tracking Topology
### Analysis of DCM Mode  
By adjusting the numerical parameters according to Equation (17), the relationship between the current iLb in inductor Lb and time t in DCM can be modeled and analyzed. Figure 9 illustrates the relationship between iLb and t in DCM, where iLb represents the current flowing through inductor Lb; ton is the conduction time of the switch transistor; t0 is the starting point of current interruption; and Tb is the end point of one period.  
The mathematical analysis shown in Figure 9 reveals that the average voltage across the inductor within one period is zero, leading to the following equation:  
\[\int_{0}^{t_{\text{on}}}{(\text{Vb}-\text{Vo})\text{dt}}+\int_{t_{\text{on}}}^ {t_{\text{0}}}{(-\text{Vo})\text{dt}}=0 \tag{15}\]  
According to Equation (20), the output voltage of the buck circuit in DCM can be obtained as follows:  
\[\text{Vo}=\frac{t_{\text{on}}}{t_{\text{0}}}\text{Vb} \tag{16}\]  
Furthermore, considering that the average current of the inductor within one period is equal to the current on the load, we can derive the following equation:  
\[t_{\text{0}}=\frac{t_{\text{on}}+\sqrt{t_{\text{on}}^{2}+\frac{8L_{\text{b}}T_ {\text{b}}}{R_{\text{b}}}}}{2} \tag{17}\]  
By combining Equations (21) and (22), we can determine the output voltage of the buck circuit in DCM as follows:  
\[\text{Vo}=\frac{2}{1+\sqrt{1+\frac{8L_{\text{b}}}{R_{\text{b}}T_{\text{b}}D^{ 2}}}}\text{Vb} \tag{18}\]  
Figure 9: DCM Mode.
## 1 Introduction
Compared to the existing methods, the proposed method has advantages as follows:  
(1) The proposed method further considers the influence of MGFs' elevation data and radar elevation angle resolution, so that it can be applied to aircraft at any altitude, better aligning with the advanced aircraft's performance and requirements. (2) The proposed method integrates errors from both the MGFs and the aircraft and employs the Monte Carlo method to obtain \(P_{ident}\), instead of the binary result from the existing method, providing more accurate and intuitive decision-making support for the mission planning of maritime aircraft.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 2 Background
### Routing Protocol for Low-Power and Lossy Networks (RPL)
#### 2.1.1 Sinkhole Attack  
A Sinkhole Attack is an internal assault in which an impacted node attempts to attract network traffic by propagating bogus routing updates. An attacker launches an attack on a network by adding bogus nodes. The objective of this attack is to reroute traffic from a certain region across a malicious node that appears to be highly appealing to the surrounding nodes [10].
## 2 Data and Methods
### Reference Systems and Transformations
Since MERS and PALRS have the same origin, they are only slightly different in terms of rotation in axes, which is a fixed matrix. Meanwhile, DE430 could provide the \(R_{ICRS}\) at a given moment. Now, the coordinates of the Moon-based observatory have been converted into the GCRS. The satellite orbits generated by SDP4 orbit propagators are also in the GCRS. The coordinate of the satellite is \(\left(X_{S},Y_{S},Z_{S}\right)_{ITRS}\). To analyze the geometric properties of MS-BiSAR Earth observation, they are converted into the ITRS by rotation of the axis of the GCRS. \[ \begin{bmatrix}X_{M}\\ Y_{M}\\ Z_{M}\end{bmatrix} _{ITRS}=R_{P}\cdot R_{N}\cdot R_{E}\cdot R_{M} \begin{bmatrix}X_{ M}\\ Y_{M}\\ Z_{M}\end{bmatrix} _{GCRS} \tag{3}\]  
\[ \begin{bmatrix}X_{S}\\ Y_{S}\\ Z_{S}\end{bmatrix} _{ITRS}=R_{P}\cdot R_{N}\cdot R_{E}\cdot R_{M} \begin{bmatrix}X_{ S}\\ Y_{S}\\ Z_{S}\end{bmatrix} _{GCRS} \tag{4}\]  
where \(R_{P}\), \(R_{N}\), \(R_{E}\), and \(R_{M}\) are the rotation matrices that result from the Earth's precession, nutation, rotation, and polar motion, respectively. These rotation matrices are realized using EOP data.
# Industrial Internet of Things for a Wirelessly Controlled Water Distribution Network
## 5 Proposed Network Architecture Design
### Network Architecture Using Trusted Wireless Network  
Trusted Wireless technology supports the mesh topology, which allows distant nodes to communicate with the PLC by relaying data through intermediate nodes, shortening the distance for data transmission. The parent-listing feature of Trusted Wireless can help implement a hierarchical structure like that of the LEACH protocol, where nodes can serve as CHs, creating multiple paths for communication.  
To maintain an efficient and reliable network topology, the Trusted Wireless network is designed with a three-hop limit, ensuring a balance between coverage, delay, and link reliability. This constraint prevents excessive latency and retransmissions, which can degrade network performance.  
The challenge of optimizing multi-hop constraints has been widely explored in network topology design. Ref. [28] propose a constraint-based parallel local search algorithm to minimize path limits while preserving network robustness and connectivity. While this approach is highly effective for general large-scale networks, industrial automation networks require carefully controlled hop limits to maintain low latency and deterministic communication, as outlined by [11] in their implementation of ISA100 Wireless for industrial applications.  
The three-hop constraint in our design follows a practical engineering trade-off, leveraging the long-range capabilities of Trusted Wireless to minimize hop counts while ensuring wide coverage. Even in future expansions, careful node placement will allow the system to maintain three-hop connectivity, preserving network efficiency without excessive reliance on relays.  
(1) Main Tank: In this network, the PLC, serving as the central node, is directly connected to five repeaters. These repeaters function as CHs, coordinating communications with other remote nodes in the network and suggesting omnidirectional antennas to enable communication with the surrounding wells. However, in a few cases, like in the northern well (T/1/364/0/96), where there are no surrounding wells and it can only connect to the central mode, it is advisable to use a directional antenna.  
Additionally, Figure 5 highlights the integration of a node at the secondary tank into this network. This integration allows the PLC associated with the second tank to send control requests to the PLC in the main tank as necessary, in line with the control algorithm. This setup leverages the mesh networking capabilities serve as repeaters and clients. Hence, it eliminates the need for a direct point-to-point link between the two PLCs.  
Figure 5: Illustration of the main water tank mesh network.  
(2) Secondary Tank: For the second water station, due to the way the wells are distributed, a mesh topology will not be of any use. The best approach is similar to the one proposed in the Wi-Fi network design, which is to have a star that connects each well directly to the central node using directional high-gain antennas.
# An End-to-End Deep Learning Image Compression Framework Based on Semantic Analysis
## 1 Introduction
The specific contributions are divided into the following three points:  
(1) An end-to-end deep learning image compression framework based on semantic analysis is proposed, containing a semantic analysis network based on a convolutional neural network (CNN) and an image compression network based on a recurrent neural network (RNN). (2) We design a compression bit allocation algorithm to calculate the compression iterations of each image block, skillfully using the results of semantic analysis to compress the image hierarchically. (3) The performance and feasibility of the proposed compression framework are verified by comparison with other image compression algorithms in a multi-structure structural similarity (MS-SSIM) index and proposed semantic-important structural similarity (SI-SSIM) index.
## 2 Materials and Methods
### Point-to-Point Registration Method Based on Leave-One-Out Strategy  
To define the optimal rotation matrix and translation vector between a pair of corresponding 3D point sets, we developed a novel point-to-point registration method using a singular value decomposition (SVD) according to the concept of the conventional iterative closest point algorithm [27]. We used six pairs of corresponding points for registration, and the overall registration procedure was as follows. (1) The centroids of the point sets were calculated by averaging the positions of the six points in each set. (2) The centroids of the point sets were moved to the origin for removing the translation components. (3) We generated the accumulating matrix and applied SVD to find the rotation (Equations (1)-(3)). (4) We calculated the translation vector (Equation (4)) and determined the rigid transformation for registration.  
\[\mathrm{H}=(\mathrm{M}-\mathrm{Centroid}_{\mathrm{M}})(\mathrm{F}-\mathrm{ Centroid}_{\mathrm{F}})^{\mathrm{T}} \tag{1}\]  
\[[\mathrm{U},\;\mathrm{S},\;\mathrm{V}]=\mathrm{SVD}(\mathrm{H}) \tag{2}\]  
\[\mathrm{R}=\mathrm{V}\mathrm{U}^{\mathrm{T}} \tag{3}\]  
\[\mathrm{R}=\mathrm{V}\mathrm{U}^{\mathrm{T}} \tag{4}\]  
\[\mathrm{t}=\mathrm{Centroid}_{\mathrm{F}}-\mathrm{R}\;\times\;\mathrm{Centroid }_{\mathrm{M}} \tag{5}\]  
where M (moving points) and F (fixed points) are sets of 3D points with correspondence, R is a 3 \(\times\) 3 rotation matrix, t is a translation vector (technically a 1 \(\times\) 3 matrix), H is an accumulated matrix, U is an unitary matrix, S is a rectangular diagonal matrix with non-negative real numbers on the diagonal, and V is an unitary matrix [28].  
To improve the accuracy of the point-to-point registration, we established a leave-one-out registration strategy. An initial registration was performed using the corresponding point sets using six points, and the fiducial registration error (FRE) was then calculated using the points used for the registration. After the initial registration, we found and excluded the point with the highest error. Then, we performed the fine registration using the remaining five points. As a result, the rigid transformation between the patient's physical and image spaces was determined by the registration.
## 3 Fundamentals of GLI-Split
### Name Resolution
#### 3.3.2 Use of the Mapping System  
The mapping system consists of a local and a global component. The local mapping system stores a set of local GLI-addresses for IDs residing within its local GLI-domain while the global mapping system stores a set of global GLI-addresses for any ID. Sets of addresses are required when routing alternatives exist, e.g., inside a GLI-domain when the ID is connected to several networks in the same GLI-domain, or, in the global mapping system when the ID belongs to a GLI-node in a multihomed domain.  
GLI-hosts with upgraded networking stacks are able to recognize when an IP address returned from the DNS belongs to a GLI-node. In that case, they extract the ID from that address and query the local mapping system for an appropriate GLI-address. If the destination node resides in the same GLI-domain as the requesting node, the local mapping system returns a set of local GLI-addresses, otherwise it notifies the requesting node that the requested ID is not part of the same GLI-domain. Then, the GLI-node requests the global mapping system which returns a set of global GLI-addresses.  
Like the DNS, the mapping system is queried only for the first occurrence of a new ID and the query result is locally cached for later use to avoid that the mapping system becomes a performance bottleneck [25]. We do not specify how the mapping system works in detail but in [10], we propose a scalable, reliable, and secure mapping system called FIRMS which could be used in combination with GLI-Split. In addition in [26], we propose a classification of current mapping systems and compare FIRMS with many others, e.g., [27; 28; 29; 30], in detail.
# Adaptive Pre/Post-Compensation of Cascade Filters in Coherent Optical Transponders
## 7 Discussions of Results and Research Findings
### Comparison of Experiments and Simulations Results  
The lab results are shown in Figure 7a. Unlike how data displayed in presented simulation results, the required-OSNR again was measured per frequency offset and only the difference to post-compensation only is presented. We saw a consistent gain for both pre-compensation and proposed method, with the minimum gain equal to 0.2 dB, compared to Rx doing the full post-compensating using its adaptive filtering. The proposed method has a steady gain over pre-compensation. It does tell that the real-time transponder has many sources of noise on the Tx side. The simulation and lab results agree qualitatively. We will list some of the main differences between the two setups:  
1. Although the Tx/Rx used in the simulation were fixed-point models with inherited implementation noise, other noise sources such as EO thermal noise were not captured. As well, since we are using 16 QAM as modulation format, there is sensitivity of the results to In-Phase/Quadrature-Phase imbalance in power and delay. As well, it was not possible to simulate the same jitter profile generated by local VCO at both the DAC and ADC.
2. The resolution of the waveshaper does not permit the accurate replication of filters shapes (measured with spectrum analyzer with 400 MHz resolution). While in simulation, the channel model was done in floating-point.
3. The EO model in the simulation had no ripples, while it can be seen from Figure 3b, when bypassing the waveshaper that the signal has frequency-dependent ripples.
4. Carrier recovery is activated in a commercial card, although the frequency difference between the Tx and Rx lasers was within 200 MHz from the ITU center frequency, the small dithering and phase noise would impact slightly (less than 0.025 dB compared to using one card in loopback--i.e., same laser) the results.
###### Abstract  
Stable communication technologies in complex waters are a prerequisite for underwater operations. Underwater acoustic communication is susceptible to multipath interference, while underwater optical communication is susceptible to environmental impact. The underwater electric field communication established based on the weak electric fish perception mechanism is not susceptible to environmental interference, and the communication is stable. It is a new type of underwater communication technology. To address issues like short communication distances and high bit error rates in existing underwater electric field communication systems, this study focuses on underwater electric field communication systems based on direct sequence spread spectrum (DSSS) and binary phase shift keying (BPSK) modulation techniques. To verify the feasibility of the established spread spectrum electric field communication system, static communication experiments were carried out in a swimming pool using the DSSS-based system. The experimental results show that in fresh water with a conductivity of 739 \(\mu\)S/cm, the system can achieve underwater current electric field communication within a 11.2 m range with \(10^{-6}\) bit errors. This paper validates the feasibility of DSSS BPSK in short-range underwater communication, and compact communication devices are expected to be deployed on underwater robots for underwater operations.  
Weak electric fish; direct sequence spread spectrum; binary phase shift keying modulation; electric field communication
## Materials and Methods
### ELISA Test Reagents, Equipment and Procedure  
Screening analyses were performed using a Neogen Veratov(r) Histamine kit. All reagents used for the ELISA screening analysis were included in the commercial kit. Sample preparation was carried out according to the kit instructions with minor changes. Briefly, the pre-treated sample was homogenized using a commercial blender. One gram of each aliquot was transferred into a 15 mL centrifuge polypropylene tube and 9 mL of distillate water was added. After shaking at room temperature for 10 min, the mixture was centrifuged at 2112 g and 10 \({}^{\circ}\)C for 10 min; 100 \(\upmu\)L of the aqueous supernatant was diluted with 10 mL of kit diluent buffer. A further 5-fold dilution was necessary to make the concentration compliant to the standards curve. Thus, the sample was ready to be analyzed according to the instructions reported in the manufacturer's kit manual.  
A microtiter plate spectrophotometer (Anthos HT2, GSG ROBOTIX s.r.l. (Cinisello Balsamo, Milano, Italy) was used for reading the ELISA plates at 620 nm. A calibration curve was obtained, plotting absorbance values against the concentration of standard solutions at 0, 20, 50 mg kg\({}^{-1}\). Sample concentration values were multiplied to the dilution factor of five. In each analytical batch, positive and negative quality control samples were included. The negative quality control was a blank fish (i.e., tuna and/or ripened anchovies) depending on the type of samples analyzed in the daily batch, while the positive quality control was the same blank tuna spiked at 100 mg kg\({}^{-1}\) and/or the same blank ripened anchovies spiked at 200 mg kg\({}^{-1}\). Unknown samples, standard solutions and quality control were pipetted twice into the microplate wells. Elisa test, allowing the detection of HIM content at concentrations between 2.5 and 250 mg kg\({}^{-1}\), was fully validated. Details of validation are reported elsewhere [32].  
Samples analyzed by the ELISA method with an HIM content greater than 77 mg kg\({}^{-1}\) (Maximum Limit--2 \(\times\) relative standard deviation of the screening method repeatability) for fresh and canned fish and 177 mg kg\({}^{-1}\) (Maximum Limit--2 \(\times\) relative standard deviation of the screening method repeatability) for processed fish were judged \"suspicious non-compliant\" samples and they were subsequently analyzed by an HPLC/FLD method with online derivatization with _o-phthalaldehyde_.
## 4 Results
### Lithofacies Associations
#### 4.1.3 FA3: Shore-Shallow Lacustrine Deposits  
Description:  
FA3 is characterized by gray silt-fine sandstone (Fl, Fr), silty mudstone, and mudstone (Fm, Figure 5). The silt-fine sandstone forms sheet-like or lens-shaped bodies, typically 0.5 to 2 m thick, interbedded within thick mudstone. It commonly exhibits ripple and horizontal bedding, while the mudstone and silty mudstone are predominantly characterized by massive or horizontal bedding. The lithofacies association has an overall thickness ranging from 10 to 30 m, with the GR curve showing a relatively stable baseline with minor fluctuations (Figure 6).  
Interpretation:  
FA3 is interpreted as shore-shallow lacustrine deposits, dominated by fine-grained sediments. Ripple bedding indicates wave activity, while lens-shaped silt-fine sandstone interbedded within thick mudstone reflects beach-bar deposits influenced by wave action near the lake margin. Horizontal bedding in sandstone suggests low-energy traction currents entering the lacustrine environment [58]. Massive mudstones represent periods of stable lacustrine conditions, with suspended particles settling in low-energy environments [59]. Gray and black sediment predominance, with an absence of red-colored deposits, indicates humid climatic conditions [60].