esa-sceva/satcom-chunk-collection huggingface dataset:
# RS-DETR: An Improved Remote Sensing Object Detection Model Based on RT-DETR
## 4 Results and Discussion
### Experiments on the SIMD Dataset  
#### 4.4.1 Experimental Results  
The experimental results of RS-DETR on the SIMD dataset are presented in Table 1 and Figure 8. As shown in Table 1, RS-DETR achieved an mAP50 of 78.2% and an mAP of 63.1%, proving that our model has achieved relatively good results overall on the SIMD dataset. Notably, RS-DETR exceeded 90% on mAP50 for dense small targets such as cars and boats, demonstrating that our model significantly enhances detection performance for small targets. Additionally, our model achieved a recall of 100% for the fighter category, indicating that RS-DETR correctly classified and located all fighter samples, demonstrating its effectiveness in targets of medium size. However, RS-DETR underperformed in detecting categories, such as other, stair truck, pushback truck, and helicopter. This may be attributed to their smaller sizes and limited sample quantities, which resulted in lower detection accuracy for these categories.  
Figure 8 shows the visual results of the RS-DETR algorithm on the SIMD dataset, demonstrating its robust object detection performance. Our proposed method effectively identifies and locates objects of various scales, especially small objects such as cars and boats.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Class** & **P (\%)** & **R (\%)** & **mAP50 (\%)** & **mAP (\%)** \\ \hline all & 77.8 & 78.4 & 78.2 & 63.1 \\ car & 78 & 93.4 & 92.3 & 72.5 \\ truck & 78.5 & 77.4 & 77.2 & 63.1 \\ van & 77.3 & 80.4 & 78.1 & 63.2 \\ longvehicle & 68.6 & 83.3 & 79.4 & 65.6 \\ bus & 79.5 & 87.9 & 88.1 & 72.3 \\ airliner & 90.9 & 95.2 & 96.6 & 87.1 \\ propeller aircraft & 90.8 & 93.9 & 92.4 & 80.2 \\ trainer aircraft & 87.9 & 98.2 & 96.2 & 80.2 \\ chartered aircraft & 81.9 & 96.2 & 93.8 & 81.5 \\ fighter aircraft & 69 & 100 & 96.7 & 86 \\ other & 54 & 29.5 & 30.7 & 24.2 \\ stair truck & 59 & 50.8 & 49.2 & 33.6 \\ pushback truck & 69.5 & 38.6 & 50.1 & 36 \\ helicopter & 95.4 & 53.8 & 54.9 & 23.3 \\ boat & 87.3 & 97.4 & 97.3 & 78.3 \\ \hline \end{tabular}
\end{table}
Table 1: The detection results of RS-DETR for all categories on the SIMD dataset.  
Figure 8: The visual results of RS-DETR on the SIMD dataset.
## 4 Experiments and Results  
This section investigates our research efforts to identify the most suitable deep learning models for S-HAR in categorizing gym exercises. We aim to create models capable of recognizing different gym activities accurately and efficiently. We center our study on the Myogym dataset, designed explicitly for tasks related to gym exercise recognition, containing diverse sensor data collected during various gym sessions. To gauge the effectiveness of the deep learning models, we rely on two widely used metrics in S-HAR for gym applications, namely accuracy and F1-score. These metrics comprehensively assess how well the models perform in accurately classifying gym exercise activities.
# A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
## 4 Methods
### Evaluation and Comparison of Surface Albedo's Methods
#### 4.3.1 Evaluation and Comparison with ABOVE

Table 3: Data obtained from the different estimation methodologies of surface albedo with Landsat 8 (V01, V02, V03, V04) versus surface albedo ABOVE (A). Data sorted by NDVI (horizontal dashed lines). Dark green highlights the methodology with the lowest RMSE, and light green indicates the methodology with the second lowest RMSE. The analysis of the results collected in Table 3 and the RMSE values for each methodology has made possible the determination of the V03 methodology as the one that responds better (RMSE with the lowest value) for NDVI \(\geq\) 0.563 and NDVI \(\leq\) 0.385, while the V01 method shows the best results for NDVI > 0.385 and NDVI < 0.563.
# FlowHaptics: Mid-Air Haptic Representation of Liquid Flow
## 3 Methodology
### Flow Control System  
A flow control valve was used to produce the airflow based on the estimated flow velocity obtained from the fluid simulation. The flow control valve was operated by applying a direct current voltage. On the PC, eleven control signals are transmitted to the Arduino microcontroller using serial communication. Specifically, the high-order bit was used as a control section for stopping and starting the operation. The central nine bits were divided into three signals. Each of the three signals represents PWM values from 0 to 255, which are the minimum and maximum flow velocity values. The signal is sent directly to the right, center, and left air control valves. The low-order bit is a terminating character. The result of the velocity control of the airflow is presented in Figure 6.  
Figure 4: (**a**) Fluid simulation for 3 fingers and (**b**) the velocity measured in the fluid simulation on the left, center, and right parts of the middle finger.  
Figure 5: System configuration.  
so that there was no influence from the visual stimulus. We had 10 participants in total. The experiment conditions were conducted as follows:  
* 1F: the condition in which only the middle finger was presented to the device;
* 3F: the condition in which the index finger, middle finger, and ring finger were presented to the device.  
The shapes of the fingers in 1F and 3F during the experiment are shown in Figure 9c,d, respectively. Furthermore, there were two conditions for the air ejection method as follows:  
* 1T: the condition in which _FlowHaptics_ the air was ejected only from the center nozzle;
* 3T: the condition in which _FlowHaptics_ the air was ejected from all 3 nozzles.  
Hence, we had four conditions, which were 1F/1T, 1F/3T, 3F/1T, and 3F/3T, for each participant. We analyzed the discrimination performance realized by the proposed device with a constant method. The standard stimulus was set to 95.58 m/s, and we had five comparison stimuli with estimated wind speed values of 54.3, 74.94, 95.58, 114.92, and 136.86 m/s. In each condition, a pair of standard stimuli and one comparison stimulus were presented in a random order, and the participants reported whether the former or latter stimulus felt stronger. A total of 15 comparisons, that is 3 times for the 5 compared stimuli, were made for each condition. In total, 1 participant performed 60 trials of the discrimination sensitivity experiment, that is 15 comparisons for the 4 conditions, i.e., 1F/1T, 1F/3T, 3F/1T, and 3F/3T.
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 5 Conclusions  
To our knowledge, this is the first study demonstrating the significant influence of the different UV wavelengths and levels received by grapes on the phenolic and VOC composition of the resulting wines, despite the changes taking place during winemaking. In particular, flavonoids were higher in wines whose grapes had been exposed to a combination of UV-A and UV-B radiation, while esters responded to UV-A alone. Overall, our results open up new possibilities for the artificial manipulation of UV radiation in grapevine cultivation, given the contribution of these compounds to wine quality, and the healthy properties of flavonoids. Nevertheless, several years of experimentation would be needed to unequivocally confirm our results, thus avoiding the possible influence of the specific environmental conditions of the year when the experiment was performed. Future research should include experiments using different UV doses as well as high UV peak irradiances, to establish the treatments that most efficiently change grape and wine composition. In addition, other interacting factors could contribute to the further improvement of grape and wine quality [11; 13; 43; 44].  
Conceptualization, E.N.-O., J.M.-A. and M.-A.D.-C.-A.; analysis, M.-A.D.-C.-A., L.M., and R.T.-L.-H.; data curation, E.N.-O., J.M.-A., and M.-A.D.-C.-A.; writing--original draft preparation, J.M.-A., E.N.-O., and M.-A.D.-C.-A.; writing--review and editing, all authors; funding acquisition, E.N.-O. and J.M.-A. All authors have read and agreed to the published version of the manuscript.  
This research is a result of the Project PGC2018-093824-B-C42, funded by the European Regional Development Fund (ERDF/FEDER), the Spanish Ministerio de Ciencia e Innovacion, and the Spanish Agencia Estatal de Investigacion. The University de La Rioja contributed with a PhD grant of Plan Propio 2014 to M.-A.D.-C.-A.  
Data supporting reported results is contained within the article.  
We thank [PERSON], [PERSON], [PERSON], and [PERSON] (Universidad de La Rioja) for technical assistance.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Geophysical Coupling Before Three Earthquake Doublets Around the Arabian Plate
###### Abstract  
In this study, we analysed lithospheric, atmospheric, and top-side ionospheric magnetic field data six months before the three earthquake doublets occurred in the last ten years around the Arabian tectonic plate. They occurred in 2014, close to Dehloran (Iran), in 2018, offshore Kilmia (Yemen) and in 2022, close to Bandar-e Lengeh (Iran). For all the cases, we considered the equivalent event in terms of total released energy and mean epicentral coordinates. The lithosphere was investigated by calculating the cumulative Benioff strain with the USGS earthquake catalogue. Several atmospheric parameters (aerosol, SO\({}_{2}\), CO, surface air temperature, surface latent heat flux humidity, and dimethyl sulphide) have been monitored using the homogeneous data from the MERRA-2 climatological archive. We used the three-satellite Swarm constellation for magnetic data, analysing the residuals after removing a geomagnetic model. The analysis of the three geo-layers depicted an interesting chain of lithosphere, atmosphere, and ionosphere anomalies, suggesting a geophysical coupling before the Dehloran (Iran) 2014 earthquake. In addition, we identified interesting seismic accelerations that preceded the last 20 days, the Kilmia (Yemen) 2018 and Bandar-e Lengeh (Iran) 2022 earthquake doublets. Other possible interactions between the geolayers have been observed, and this underlines the importance of a multiparametric approach to properly understand a geophysical complex topic as the preparation phase of an earthquake.  
lithosphere; atmosphere; ionosphere; coupling; Swarm; Saudi Arabian Peninsula; doublet earthquakes 2024 15 1318. [[https://doi.org/10.3390/atmos15111318](https://doi.org/10.3390/atmos15111318)]([https://doi.org/10.3390/atmos15111318](https://doi.org/10.3390/atmos15111318))
## 2 Introduction and Application of Modeling
### Framework of the Proposed Model  
The flow data used in this study were obtained from 07/01/21 to 12/15/21 considering the learning time for training. Missing data from 12/16/21 to12/31/21 were excluded for precise prediction.  
There were several minor instances of missing data, which were replaced with the data from the same time on the previous day. However, this did not significantly affect the training results. Additionally, flow data during any national holidays were included for training. Flow data acquired from the tele metric and tele control systems were consistent because they were acquired using serial communication. During this period, 70% of the data were used as the training set and the remaining 30% were used as the test set. A predictive model for 42 days (11/04/21-12/15/21) was used as the test set. The observation periods were related to the same dates.  
For the flow prediction model applied in this paper, as shown in Table 2 and Figure 5, hourly flow data for the previous 7 days were used as input variables with LSTM/MLP and the hourly flow data of the next day were used output variables.  
The method for extracting the MNF was finding the smallest flow data between 0 am and 5 am in terms of the hourly flow from the previous predictions, and the smallest flow measured in a 1-h interval was used as the minimum night flow [11; 12; 13].
# Reliability-Aware Cooperative Node Sleeping and Clustering in Duty-Cycled Sensors Networks
## 6 Performance Evaluation  
We implemented the proposed RCR in three schemes, namely BLR, GPSR and REER, using the OPNET modeler and performed extensive simulations [30]. We built a wireless sensor network with a 1000 m \(\times\) 500 m field where sensors were randomly deployed. In order to verify the scaling property of RCR, we set up 800 sensor nodes in this large-scale network. By default, we set \(f\) to 0.3, \(\mu\) to 1, \(k\) to 5, \(R\) to 75 m and \(\tau\) to 0.025 s. We used the same energy consumption model as in [31].
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## 1 Introduction  
The lidar ceilometer is a remote observation device used to measure cloud height at the location in which it is installed. Many studies [1; 2; 3; 4; 5; 6; 7] have obtained planetary boundary layer height (PBLH) by analyzing backscatter data: the raw data obtained using a lidar ceilometer. However, there is considerable room for improvement due to the limited accuracy of past methodologies. In the past, backscatter data from a lidar ceilometer were used primarily for PBLH measurements, but recently they have been used for radiation fog alerts [8], optical aerosol characterization [9], aerosol dispersion simulation [10], and studies of the relationship between cloud occurrence and precipitation [11].  
Machine learning techniques have been actively applied to the meteorology field in recent years. For example, they are used for forecasting very short-range heavy precipitation [12; 13], quality control [14; 15] and correction [16; 17; 18; 15] of observed weather data, and predicting winter precipitation types [19].  
This study attempts to conduct an unprecedented analysis of backscatter data obtained using a lidar ceilometer. Beyond the conventional use of backscatter data for the analysis of PBLH measurement, their correlations with weather phenomena are analyzed and weather detectability is examined. The weather phenomena of precipitation and fog, which are expected to affect backscatter data, were examined. Cloud occurrence is related to precipitation [11], and backscatter measurements can be used to predict fog [8]. Machine-learning techniques are applied in weather detection. To detect the aforementioned weather phenomena (precipitation and fog) from the backscatter data obtained from the lidar ceilometer, three machine learning models: random forest, support vector machine, and artificial neural network were applied.  
This paper is organized as follows: Section 2 describes the three machine learning models used in this study. Section 3 introduces backscatter data obtained from the lidar ceilometer, and observational data of precipitation and fog. Section 4 presents machine learning methods for detecting the weather phenomena. Finally, conclusions are drawn in Section 5.
## 3 Results and Discussions
#### 3.1.3 Linear Polarization Method  
After three months of curing, Tafel curves were determined for the reinforcing steel inside the concrete specimens containing the corrosion inhibitor and the control group. Data fitting was performed based on these Tafel curves. Figure 10 shows the polarization curves of reinforcing steel under the influence of 0.5% chloride ion content and the corrosion inhibitor. Table 6 presents various data obtained by fitting calculations using fitting program on the Tafel polarization curves of the reinforcing steel.  
Figure 10: Tafel curve of steel bars when inhibitor is added.  
Figure 9: Reinforcement under the effect of the inhibitor: (**a**) corrosion area rate of steel bars; (**b**) steel bar weight loss rate.  
From Figure 10, it can be observed that at a fixed chloride ion content, as the amount of _Xanthium sibiricum_ extract increases, the corrosion potential of the reinforcing steel shifts in a positive direction. Compared to the control group, the changes in Sa-1 and Sa-2 are particularly notable. The relevant literature [25; 26] indicates that the magnitude of reinforcing steel's corrosion potential can reflect its corrosion tendency. A positive shift in corrosion potential represents a slowing of the steel corrosion process, suggesting that the _Xanthium sibiricum_ corrosion inhibitor can achieve a good inhibition effect. Table 6 shows that the corrosion rate of reinforcing steel in the D-1 group without the inhibitor is approximately 47.5% faster than that of the experimental group Sa-3, with the steel loss rate being about 40% more severe. As the ratio of chloride ion content to _Xanthium sibiricum_ content increases, the self-corrosion current density of the reinforcing steel gradually decreases, implying a reduction in the electrochemical reaction rate at the test electrode surface. It is preliminarily speculated that active compounds in high concentrations of _Xanthium sibiricum_, due to their strong oxidizing properties, chemically react with the metal surface, promoting the formation of a dense protective passivation layer. This effectively reduces contact between the reinforcing steel and chloride ions, inhibiting the rate of corrosion reactions and enhancing the corrosion resistance of the reinforcing steel.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 5 Setting up the PrECast Infrastructure
### The PrECast Algorithm
#### 5.1.3 The PrECast ARP Request Process  
Our approach is similar to the conventional ARP request process. Host A needing to know Host B's MAC address will broadcast an ARP request packet. This packet will be received by Switch S through the switch port \(S_{A}\). Instead of forwarding to every host in the network, PrECast will modify the ARP process as follows:  
* Switch S will check its MCAM table for a possible <MAC, IP> binding for Host B. If the required ARP-mapping information is available, S will send a unicast ARP-reply to Host A.
* If there is no binding information available at Switch S, S will securely multicast the ARP-request to all other switches in the network. The switch to which Host B is connected (say Switch T) will send a multicast reply containing the <MAC, IP> binding of Host B to every switch in the network. Switches receiving this multicast reply will add Switch T's ID, <MAC, IP> in their MCAM table for possible use in the sequel. Eventually, Switch S will send an ARP-reply back to Host A based on the information obtained from Switch T.
* If no switch knew about Host B, like the conventional ARP process, the ARP request will go for time-out. This will be known to all switches in the network, through the absence of any multicast reply.  
Figure 3: MCAM table build-up process. G-ARP, Gratuitous ARP; MCAM, Modified CAM.  
As can be seen from the above steps, the PrECast ARP algorithm is transparent to end-hosts. Hosts neither need a modified protocol stack, nor to be aware of the existence of the PrECast system running in the network.  
The state-transition diagram of an ARP-lookup process for the proposed PrECast algorithm is presented in Figure 4.
# On the Modeling and Analysis of Brittle Failure in Existing R/C Structures Due to Seismic Loads
## 2 Procedures for Simulating Strength Mechanisms in Substandard R/C Buildings
### Simulation of Beam-Column Joint Behavior  
Shear failure of beam-column joints in R/C buildings during earthquakes is a pronounced brittle mode of failure that may cause excessive flexibility of the overall frame and a consequent loss of vertical load-carrying capacity [3]. The integrity of R/C beam-column joints is secured through the compressive stress field acting within the joint which enables the concrete to participate in the joint shear action and allows (through the development of high bond stresses) for steep force gradients along with the beam and column primary reinforcements. In R/C buildings designed and constructed under contemporary standards (ASCE/SEI 41-17 [1], EN1998-1 [2], etc.), beam-column joints are dimensioned so as to sustain the development of the flexural strengths of the adjacent frame elements at the joint faces without significant degradation of the bond along with beam and column primary reinforcement. In substandard R/C buildings, the absence of a limited number of stirrups within the beam-column joints does not provide sufficient confinement to support the formation of the diagonal strut mechanism that is essential for force transfer between adjacent members during earthquake excitations.  
Simulation of R/C beam-column joint behavior in FE models of existing R/C buildings can be achieved through the use of zero-length elements placed above and below the intersection of the beam and column elements used for simulating the mechanical behavior within the joint (see Figure 1). The joint response in each of the building principal plan dimensions is defined by the multilinear \(M\)-\(\theta\) relationship of Figure 5. In this figure, \(M_{j}=V_{j}\cdot(H_{\mathrm{cl}}/2)\) is the bending moment associated with the shear capacity \(V_{j}\) of an R/C beam-column joint according to the RSA (see Equations (A11) and (A12) in the Appendix A, for unreinforced and well-reinforced joints, respectively), whereas \(r_{j}\) (\(=V_{j}/V_{flex}\)) is the resistance ratio to shear failure in the beam-column joints.
# An Adaptive 24 GHz PSO-Based Optimized VCO in Next-Generation Wireless Sensor Networks
## 2 Problem Identification in PSO  
Below are five key points that identify problems with the traditional PSO and how our proposed adaptive PSO addresses them:  
1. A traditional PSO employs a fixed velocity limit (\(S_{max}\)) throughout all iterations, which can lead to slow convergence. Adaptive PSOs introduce an adaptive scaling term that gradually reduces Vmax as the algorithm progresses, thereby speeding up the convergence and reducing the total number of objective function evaluations.
2. In traditional PSOs, the constant search scale may initially allow for ample exploration but does not adequately shift towards exploitation (fine-tuning) in later stages. The adaptive PSO strategically decreases the search scale over time, ensuring a broad initial search for global optima and a focused, refined search as the algorithm nears convergence.
3. Evaluating candidate solutions is computationally expensive; therefore, traditional PSOs may require many evaluations due to their slower convergence rate. Adaptive PSOs minimize the computational cost by reducing the number of evaluations needed, making them more efficient in scenarios where each function evaluation is resource-intensive.
4. The fixed parameter settings in traditional PSOs do not adapt to different phases of the search process, potentially limiting their performance across diverse optimization problems. In contrast, adaptive PSOs incorporate a tunable scaling factor (controlled by the positive constant h) that adapts based on current generation, providing greater flexibility and robustness across various stages of the optimization process.  
The comparison between Traditional PSO and Proposed adaptive PSO is shown in Figure 3.