esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works on DL Methods of Small Object Detection
This work used three channels of Red, Green, and Blue (RGB) SAR images and achieved precise results. Recently, a target signature-based ship indicator was developed for complex signal kurtosis alongside a SAR image dataset [28]. This work introduced signals for complex image detection. A model was used to identify the ships on a high-resolution SAR image dataset tested with the DL algorithm [29]. This method was equipped with adaptive hierarchical detection. A lightweight neural network (NN)-based solution was recently introduced to detect ships over SAR image datasets [30]. This work tested a multi-scale ship identification mechanism for space-borne SAR images. Yet another work was developed for SAR-image-based ship detection, and it was technically named U-net [31]. In this work, the efficacy was achieved and tested for a low-cost ship detection model. A few more works have introduced ship detection techniques with neural-network-based models [32; 33; 34; 35]. A work discussed multiple downsampling for small objects with weaker features of small objects. Here, YOLOv5 was introduced to overcome irrelevant context. Feature enhancement was achieved with YOLOv5 with discriminative features of smaller objects [36]. Recently, GAN was used with ResNet 50 architecture with a feature pyramid network (FPN) to detect multi-class objects alongside the image enhancement process for a SAR dataset [37]. GAN was used to detect small, as well as medium-sized, objects. Yet another work used FPN with CNN for scale sequence feature detection over small objects. Scale-sequence-based FPN introduced considerable accuracy in detecting small objects [38]. In the bottom-up pathway, spatial resolution is half-reduced when it raises one level. It can be achieved by doubling the stride at each level. Each convolutional module produces the output that the top-down pathway will use. Let us take this output as Bi. The output of each module in the bottom-up pathway will be fed to 1 \(\times\) 1 filters to reduce the depth of the Bi channel and to generate Ti. In the top-down path, which contrasts the bottom-up pathway, each layer will be upsampled by 2 when it goes down. The output of each module will be added element-wise before going to the next level. A problem arises when it is combined with an upsampled layer which is called aliasing. To recover ship images from the aliasing problem, 3 \(\times\) 3 convolutions images are applied after merging [17]. The output of this 3 \(\times\) 3 convolution layer is the final extracted feature that can be given to the object detector. If we take as a set of the production of the bottom-up pathway [B1, B2, B3, B4] and [T1, T2, T3, T4] as input to the top-down pathway, then [F1, F2, F3, F4] is a set of extracted features. The sample feature pyramid of a SAR image is given in Figure 1a,b, a top-down and bottom-up path. Figure 1: _Cont._
# An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar
## 2 Materials and Methods
### UAV Velocity Reference Calculation and Accuracy Analysis
#### 2.4.2 Trace Validation of the UAV Actual Route  
Using the radar geodetic coordinates and the route information obtained by the RTK, the theoretical elevation of the main beam center corresponding to the UAV at each position point was calculated. By comparing the actual elevation of each position recorded by the UAV, it was verified whether the UAV was flying in the main beam center of the radar system. According to the horizontal distance \(R_{n}\) between the UAV and the radar system, the radar elevation angle \(\alpha\) and the antenna feeds elevation \(H_{R}\), the theoretical elevation of each position is \(H_{n}\) and the beam broadening \(H_{w}\) at each point is respectively:  
\[H_{n}=R_{n}\tan\alpha+H_{R} \tag{13}\]  
\[H_{w}=R_{n}\tan(\alpha+\theta/2)-R_{n}\tan(\alpha-\theta/2) \tag{14}\]  
where \(\theta\) is the beam width (unit: \({}^{\circ}\)). By comparing the actual and theoretical elevations, the height deviation of each point and its percentage of the beam width were calculated.
# Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks
## 4 Lessons Learned
### Explicit Feedback  
Most of the explicit feedback approaches rely on physical layer metrics, mainly the SINR, as we have seen in the related work section. Several conditions should be met for this approach to be used on real devices such as a hardware that provides SINR values, or a method for sending the feedback to the transmitter, which does not modify the standard.  
Additionally, as simulation results show, only relying on SINR values for decision making may not be enough. In some cases, the absence of the feedback may result in many frame losses until the channel quality is suitable for the current rate. A possible solution to this problem may be implemented on the transmitter side.  
Although explicit feedback algorithms, such as Ideal RAA, gave better performances than the other algorithms in a dense environment, in both ad hoc and infrastructure modes, a lot of work needs to be done in order to minimize the FLR, which will result in better overall performances. One can consider different approaches to minimize the FLR by combining the current explicit approach with implicit approaches, such as changing the A-MPDU size, which results in a lower channel occupancy and collecting statistical information at the transmitter that will help in the decision making of future transmissions.
## 7 IoMT Healthcare Services and Applications
### Smartphones Services  
Smartphone services enable healthcare systems to provide better patient care, remotely monitor health status, and increase patient engagement. Mobile apps allow patients to easily access health records, schedule appointments, and communicate with doctors. Smartphone-controlled sensor services support patient monitoring, telemedicine, and remote doctor visits. They provide quick and secure access to medical records, promoting seamless information sharing among healthcare providers and enhancing patient care while reducing costs. Versatile healthcare functionalities are achieved on smartphones with compatible hardware and software products. Diagnostic apps offer access to information on diagnostics and treatments, while medical education apps provide tutorials, training materials, surgical demonstrations, and medical illustrations. In ref. [97] presents image analysis algorithms for non-contact measurements in healthcare applications. However, implementing IoT smartphones in healthcare systems presents challenges that need to be addressed. Data security and privacy are major concerns in light of increasing data breaches and cyber-attacks, requiring robust security measures to protect patient information. Standardization among IoT devices is another challenge, hindering interoperability and data exchange between different devices and systems. Additionally, not all individuals are interested in fitness and health tracking, and some may find constant monitoring invasive or uncomfortable. Moreover, there are alternative devices such as Fitbit's Zip or One that cater to fitness tracking without relying on a smartphone.
## 5 Laboratory Verification for PMSM Drive  
### The Control Structure  
The appropriate PMSM model, after necessary transformations, was used in simulation studies, incorporating the typical _Field-Oriented Control_ (FOC) concept for a PMSM drive, as shown in Figure 11. A standard \(i_{d-ref}=0\) control strategy was assumed. This attempt assumes that the current controllers in the \(d\)- and \(q\)- axes will not be modified. The tests involved only changing the structure and parameters of the speed controller.  
As previously mentioned, part of the experimental verification of the presented signal-delayed control concept involved selecting the speed control system of the PMSM motor drive as the test object. For this application, the controlled PMSM can be modeled using equations for a rotating coordinate system synchronous with the rotor--denoted as \(dq\). Traditional simplifying assumptions were applied in the analyzed model: the rotor is magnetically symmetrical, has no windings, and the rotor flux remains constant. Phenomena such as eddy currents, saturation, and temperature effects are ignored. In such cases, the following form is obtained:  
\[u_{d}=R_{i}\dot{q}_{d}+L_{d}\frac{\dot{q}_{d}}{dt}-p\omega_{r}L_{q}\dot{q}_{q}\,, \tag{11}\]  
Figure 11: General structure of control system with PMSM.  
Figure 10: Simulation: The current waveforms of the reference \(q\)-axis for the response of the system to a sloped change in the speed setpoint. Corresponds to the waveforms in Figure 8. (**a**) Using an MPI speed controller. (**b**) Using a PID speed controller; the current is filtered for better clarity.  
\[u_{q}=R_{s}i_{q}+L_{q}\frac{di_{q}}{dt}+p\omega_{r}L_{d}i_{d}+p\omega_{r}\Psi_{m}\,, \tag{12}\]  
\[T_{e}=\frac{3}{2}\cdot p\left[\Psi_{m}-\big{(}L_{q}-L_{d}\big{)}i_{d}\right] \cdot i_{q}\,, \tag{13}\]  
\[J\frac{d\omega_{r}}{dt}=T_{e}-T_{l}, \tag{14}\]  
\[\frac{d\Theta}{dt}=p\cdot\omega_{r}\,. \tag{15}\]  
where:  
\(u_{d}\), \(u_{q}\)\(d\)- and \(q\)- axis stator voltages, respectively;  
\(i_{d}\), \(i_{q}\)\(d\)- and \(q\)- axis stator currents, respectively;  
\(L_{d}\), \(L_{q}\)\(d\)- and \(q\)- axis inductances, respectively;  
\(R_{s}\) stator resistance;  
\(\omega_{r}\)rotating speed;  
\(\Psi_{m}\)magnetic flux excited by permanent magnets placed on the rotor;  
\(p\)number of pole pairs;  
\(T_{e}\)produced electromagnetic torque;  
\(T_{l}\)load torque;  
\(J\)total moment of inertia;  
\(\Theta\)rotor position.  
In this case, it is assumed that \(L_{d}=L_{q}\).
## 2 Analytical Description of the Proposed Algorithm
### Doppler Checker, Phase Corrector, and Bi-Phase Signal Recovery  
In each iteration, the estimated Doppler value, \(\hat{f_{d}}\), is passed to the Doppler checker to decide whether to continue the iterations or proceed to range and velocity calculations, as shown in Figure 2. If \(\hat{f_{d}}\
eq 0\), the Doppler checker passes \(\hat{f_{d}}\) to the accumulator, which aggregates the Doppler estimates from the previous iterations.  
Since the Doppler estimate can exceed the actual value, the aggregation is not always obvious. In fact, the Doppler checker has to be novel enough to compute the proper sign of \(\hat{f_{d}}\) before passing the signed \(\hat{f_{d}}\) to the accumulator.  
The accumulated Doppler value \(\hat{f_{d}}=\sum\hat{f_{d}}\) is delivered to the conjugate of the Euler form and Hadamard product blocks to remove the estimated Doppler effect from the signal. Mathematically, the conjugate of the Euler form block computes \(e^{2\pi i\hat{f_{d}}t}\), while the Hadamard product, an element-wise multiplication between two signals, outputs the following:  
\[B(t)=R_{1}(t)e^{2\pi i\hat{f_{d}}t}=e^{i\phi}X(t-\tau)e^{-2\pi i(\hat{f_{d}}- \hat{f_{d}})t}+n(t)e^{2\pi i\hat{f_{d}}t}, \tag{12}\]  
where \(\phi=2\pi f_{d}\tau\) is the phase effect of the target Doppler delay. Note that \(e^{i\phi}\) is a constant, as the Doppler effect and range of targets are unchanged from scan to scan. Therefore, this phase does not affect the local minmax detection.  
If \(\hat{f_{d}}\) is 0, the Doppler checker knows that the current Doppler residue is small enough to terminate the iterations and deliver the signal for range and velocity calculations, as shown in Figure 2 with yellow arrows. The ultimate effect of each iteration is, therefore, to reduce the overall Doppler effect \(f_{d}\) by an amount equal to the accumulated Doppler values \(\hat{f_{d}}\).  
Interestingly enough, the expression of \(B(t)\) in Equation (12) is analogous to that of \(R_{1}(t)\) in Equation (2) but with a reduced Doppler effect. Therefore, we can recursively repeat the same processing that we used for \(R_{1}(t)\) on \(B(t)\) until we obtain a Doppler estimate value \(\hat{f_{d}}\approx 0\).
## 3 Results  
### Water Environments and Seagrass Bed  
Figure 2 illustrates the changes in water temperature and salinity at the surveying points during the survey period. The surface water temperature at Stn. 1 in Akkeshi-ko Estuary showed the lowest value of 3.9 \({}^{\circ}\)C on 21 April, the first day of this study, and it then gradually rose and reached the maximum of 15.7 \({}^{\circ}\)C on 23 June (Figure 2a). The salinity had a relatively high value 29.8 on 21 April, but the lowest value of 23.0 was recorded the next week (Figure 2b). After that, the salinity gradually ascended and remained stable in the range of 28.6 to 30.0 after 25 May.  
The surface water temperature at Stn. 2 fluctuated in the range of 3.1 \({}^{\circ}\)C (21 April) to 14.8 \({}^{\circ}\)C (23 June, Figure 2c). The salinity varied from 27.0 on 27 April to 30.9 on 23 June (Figure 2d). Regarding the water temperature at the 5 m depth, the lowest value (3.0 \({}^{\circ}\)C) was recorded on May 6 and the highest value (8.7 \({}^{\circ}\)C) was on June 8 (Figure 2c). The salinity at the 5 m depth fluctuated between 29.8 on 23 June and 32.4 on 30 June (Figure 2d). The differences in water temperature between the surface layer (0 m) and the 5 m depth were 2.7-8.6 \({}^{\circ}\)C, and the differences in salinity were more pronounced with a maximum difference of 8.4 (27 April, Figure 2d).  
The water temperature of the surface layer (0 m) at Stn. 3 (at the mouth of Akkeshi Bay) was in the range between 2.4 \({}^{\circ}\)C (21 April) and 12.1 \({}^{\circ}\)C (29 June, Figure 2e). The surface salinity showed values between 30.6 (12 May) and 32.2 (8 June, Figure 2f). At the 20 m depth of this station, the water temperature showed the lowest temperature of 1.7 \({}^{\circ}\)C on 27 April and the highest at 5.9 \({}^{\circ}\)C on 15 June (Figure 2e). The salinity of the 20 m depth tended to be stable and was within the range from 32.3 (21 April) to 32.7 (29 June, Figure 2f). The differences in water temperature between the surface layer (0 m) and the 20 m depth were 2.4-7.1 \({}^{\circ}\)C. The differences in salinity between the surface and the bottom were very small at Stn. 3 compared to Stn. 2.  
Concerning the seagrass _Z. marina_ at Stn. 1 in Akkeshi-ko Estuary, the lengths of the leaves were <1 m on 21 April, and the growth tips of the leaves did not reach the sea surface. The leaves had become longer and reached the sea surface on the last observation day (29 June). Biofilms developed especially on the surface of the leaves that reached the sea surface, and large amounts of microorganisms including diatoms were confirmed in the biofilms.  
Figure 2: Temporal changes of water temperature (**a,c,e**) and salinity (**b,d,f**) monitored at the three stations in Akkeshi-ko Estuary (Stn. 1) and Akkeshi Bay (Stn. 2 and Stn. 3).
# Exploring Residual Clays for Low-Impact Ceramics: Insights from a Portuguese Ceramic Region
## 2 Materials and Methods  
### Sampling and Study Area Context  
The village of Sao Pedro do Corval (southern Portugal; Figure 1) has a long tradition of handmade pottery. This artisanal activity is closely linked to the geological characteristics of the region, particularly the presence of residual clays derived from the intense weathering of local metamorphic rocks. Corval is located within the Ossa-Morena Zone (OMZ) of the Iberian Massif, a complex geotectonic unit characterized by ancient metamorphic and igneous rocks affected by multiple tectonothermal events [8]. The regional bedrock is mainly composed of phyllites, schists, quartzites, and minor carbonate units, which belong to Precambrian to Paleozoic sequences deformed during the Variscan orogeny [9]. Over time, the chemical weathering of these fine-grained metasedimentary rocks led to the formation of residual clay deposits, particularly in topographically stable areas. These residual clays are in situ weathering products, rich in illite, kaolinite, and some smectitic phases, depending on the original rock composition and local hydrothermal influences [10].  
The residual clays of Corval are derived from the weathering of politic rocks (schists and hornfels). Field observations showed that these residual clays are directly influenced by the outcrops of metabasites, occurring in association with the schists of the Barrancos Formation, which crop out in the vicinity of the Reguengos Eruptive Massif [11]. [PERSON] et al. [12] identified five distinct groups in Corval based on the regional geology and the types of rocks associated with these residual clay deposits.  
For this study, 9 representative residual clay samples were collected at a depth of 40-50 cm to minimize the influence of surface alterations caused by weathering, biological activity, and atmospheric contamination. This sampling depth ensured that the materials reflected in situ characteristics of the clay deposit, avoiding the effects of prolonged exposure to air, moisture fluctuations, and organic matter accumulation. The collected samples were the following (Figure 1): A1 and A2 near the schists of the Barrancos Formation contact, B1 and B2 at the metabasite outcrops, C1 and C2 near a microgranite intrusion within the metabasites, D1 and D2 near the contact with Reguengos Eruptive Massif granites, and one sample (E1) from a small crop within the granites of the Reguengos Massif.  
Figure 1: Study area on the map of Portugal (red square), sample locations (blue dots), and a detail of one sampling site (bottom right). Adapt. IGE [13] and Novo [14].
## 6 Experiment and Result Analysis
### Comparative Experimental Analysis
#### 6.7.2 Experiments Across Datasets  
In real-world scenarios, suspicious images or videos are often generated from unknown source videos using unknown algorithms. Therefore, cross-dataset transfer is crucial. To better demonstrate the effectiveness of our method, we adopted the widely used CelebDF dataset for a comprehensive evaluation. Representative algorithms in the field were trained on the FaceForensics++ dataset, and their transferability was tested on CelebDF. The test results are presented in Table 13.  
From Table 13, it is obvious that the FG-TEFusionNet algorithm achieved an AUC of 72.15% when trained on the FF++ dataset and tested on the CelebDF dataset. While the validation results are not the absolute best obtained, our method still exhibits the highest AUC performance on the FF++ dataset. We evaluated the transferability of our framework in this context and conducted tests on Celeb-DF. Given the substantial differences between datasets, this remains a challenging task for most detection methods. All methods show varying degrees of performance decline in cross-dataset tasks. Furthermore,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **FaceForensics++ (\%)** & **CelebDF (\%)** & **Date** \\ \hline Two-Stream [46] & 70.10 & 53.90 & 2017 \\ MesoNet [20] & 84.70 & 54.80 & 2018 \\ Capsule [43] & 96.60 & 57.50 & 2019 \\ XceptionNet [13] & 98.97 & 65.30 & 2019 \\ Two-Branch [47] & 93.18 & 73.41 & 2020 \\ F3-Net [45] & 97.97 & 65.17 & 2020 \\ SPSL [22] & 96.94 & 76.88 & 2021 \\ Multi-attentional [26] & 99.80 & 67.64 & 2021 \\ M2 TR [23] & 98.50 & 65.70 & 2022 \\ Long-Distance [24] & 99.79 & 70.33 & 2023 \\ \hline FG-TEFusionNet (Ours) & 99.83 & 72.15 & 2025 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The AUC results of transferability testing on the CelebDF dataset across different methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **FPR (\%)** & **FNR (\%)** & **AUC (\%)** & **ACC (\%)** \\ \hline Deepfakes & 0.32 & 0.51 & 99.87 & 99.49 \\ Face2 Face & 0.89 & 0.92 & 99.42 & 99.18 \\ FaceSwap & 1.12 & 1.05 & 99.29 & 99.11 \\ NeuralTextures & 0.45 & 0.62 & 99.78 & 99.03 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Classification error distribution fpr/fnr of the proposed model across four deepfake datasets.  
experimental results indicate that although our method was not explicitly designed for cross-dataset performance, its AUC metric still outperforms most methods and demonstrates satisfactory cross-dataset performance. SPSL, designed specifically for framework transferability, achieved the best results. The results in the table also affirm the robustness and generalizability of our proposed approach.
# Integrating Inland and Coastal Water Quality Data for Actionable Knowledge
## 3 Overview of Observational Methods and Platforms
### Remote Sensing
#### 3.3.3 Drone and Aircraft Systems
They have the potential to play a significant role in coastal and inland water quality monitoring in years to come. However, listing them here is beyond the scope of this paper. Disadvantages of drones include frequent changes to codes, restrictions, and controlling legislation at the state and national levels [99]. Local and national laws now commonly require specialized training, certifications, and aircraft registration. Drone pilots must be aware of restricted airspaces. Obtaining permissions can sometimes be lengthy and complicated, although a new USA FAA flight approval process (LAANC) can provide dependable and rapid permission for flights in restricted airspaces using computer interactions between a certified operator's flight planning software (for example B4 UFLY) and local control tower operations. Operators should have good training and experience with dynamic weather conditions, natural and human-built obstacles, encounters and avoidance with other aircraft, birds, and protected wildlife species.
###### Abstract  
Transmission tower connection joint is an important connection component of the tower leg member and diagonal member. Its axial stiffness directly affects the stable bearing capacity of a transmission tower. The axial stiffness of the joint is mainly related to the connection form of joint. This paper takes the double-limb double-plate connection joint as the research object. Through the comparative study with the single-limb single-plate connection joint, the influence law of single-limb single-plate and double-limb double-plate joint on stable bearing capacity of quadrilateral transmission tower is studied from three aspects of model test, theoretical analysis and numerical simulation. Through the scale model test, it is found that the elastic stiffness of the double-limb double-plate joint is 3.12 times that of the single-limb single-plate joint, which can increase the bearing capacity of the joint by 26.1%. Through the energy method, the theoretical calculation expression of the stable bearing capacity of the quadrilateral tower considering the influence of the axial stiffness of the joint is derived. Compared with the effect of the single-limb single-plate connection joint, the double-limb double-plate joint can improve the stable bearing capacity of the quadrilateral tower by 15.6%. Considering the influence of geometric nonlinearity of tower and connecting joint, it is found that the double-limb double-plate connecting joint can improve the nonlinear stability bearing capacity of a transmission tower by 14.9%. The results show that the double-limb double-plate connection joint can not only improve the bearing capacity of the joint, but also greatly improve the stable bearing capacity of the tower. The research results can provide reference for the engineering application and design of double-limb double-plate connection joints.  
single-limb single-plate; double-limb double-plate; elastic stiffness; model test; geometric nonlinearity; nonlinear stable bearing capacity +
Footnote †: journal: Applied Sciences