esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Errors from Different Environments  
To evaluate model robustness in complex environments, the obstacles composed of various materials were randomly placed. Figure 12 illustrates the positioning errors under different environmental conditions. Compared to a single material environment, the model demonstrated stable adaptability in environments with randomly placed obstacles, with a maximum error of 0.92 cm, a minimum error of 0.85 cm, and an average error of 0.89 cm. Notably, the lowest positioning error was observed when paper was used as the obstacle, with an average error of 0.8557 cm and a standard deviation of 0.0114, reflecting excellent stability. This result highlights that the low reflectivity of paper minimally impacts positioning accuracy.  
In contrast, wood as an obstacle resulted in the highest average error of 0.91 cm among all conditions. This may be attributed to the wood surface's complex texture and moderate reflectivity, which interfere with laser signals. Although error magnitude is high, the standard deviation of 0.0188 indicates moderate variability. In the absence of obstacles, the system achieved high-precision performance with an average error of 0.8757 cm, a standard deviation of 0.0188, and an error range from 0.84 cm to 0.90 cm. When metal was used as an obstacle, the average error increased slightly to 0.9043 cm, and the standard deviation of 0.0238 suggests greater error variability. This increased variability is likely due to the uneven reflective properties of metal surfaces, which affect the stability of laser detection.  
Figure 11: Errors at different distances from the target object.  
To be closer to the actual usage scenario, we randomly set up obstacles in a two-meter-wide corridor and conducted the experiment in a completely dark environment. Figure 13 presents the memory usage and inference latency of the proposed LBP-FFT-CNNs model over a 2-h period, sampled every 5 min under simulated high-load and variable system conditions. Compared to baseline conditions, both metrics exhibit more pronounced fluctuations. The memory usage varies within the range of approximately 1280 MB to 1320 MB, reflecting periodic increases in background memory consumption or model memory reallocation. Despite these variations, the model maintains stability without exceeding the critical 1.4 GB threshold, ensuring continued operation within the constraints of the Raspberry Pi 4B.  
Inference latency fluctuates between 145 ms and 160 ms, which corresponds to a significant increase relative to nominal performance (~45 ms). These elevated values simulate worst-case scenarios, such as concurrent sensor data processing or intermittent I/O activity. Importantly, the latency remains within acceptable bounds for applications with moderate real-time constraints.  
Overall, the system demonstrates robust behavior under resource-constrained conditions, confirming the model's viability for deployment in dynamic embedded environments.  
Figure 12: Errors under different environmental conditions.  
Figure 13: Trends of memory usage and inference latency over a 2-h period.
# Performance Analysis of a WPCN-Based Underwater Acoustic Communication System
## 2 System Model  
We consider a point-to-point WPCN-based UWAC system consisting of one H-AP and one terminal node. Both H-AP and terminal nodes are equipped with one single hydrophone and loudspeaker pair.
## 5 DDoS Attacks Emulation on SDN-Based IoT Architecture  
### Effect of SECOD in the Simulation  
Before validating SECOD in the real testbed, we first verify its performance using Mininet (Network emulator, available at: [[http://mininet.org/](http://mininet.org/)]([http://mininet.org/](http://mininet.org/))), and the simulation is running over Ubuntu 16.04 OS with Intel i5 CPU and 8 G RAM. The setup of the virtual network is shown in Figure 3. There are 30 switches (s1-s30) attached to the same controller, and each switch has two subordinate hosts, which means there are 60 hosts (h1-h60) in this network. The bandwidth of each link between switches and hosts is limited to 1 GB. iPerf tool (Traffic generator, available at: [[https://iperf.fr](https://iperf.fr)]([https://iperf.fr](https://iperf.fr))) is used to measure the available bandwidth from h1 to h60, so that we can compare the output when the network is normal, under attack and protected by SECOD. Here, h1 behaves as a benign user and h60 acts as a server. Among the rest 58 hosts, there are 10 IoT attackers, these compromised hosts generate malicious traffic to consume the network resources.  
We compare the available bandwidth during normal state and DDoS attack state to certify the performance of SECOD, each scenario has 10 runs and results are illustrated in Figure 4. When the network is in the normal state, the average available bandwidth is 953 Mbps, and this value drops to 631 Mbps under DDoS attacks. If SECOD is activated under DDoS attack, the average bandwidth is 939 Mbps. From the result, it can be seen that SECOD can protect the network against DDoS attacks under multiple attackers.  
In order to emulate IoT traffic in an SDN-based network, we replicate as close to a realistic IoT scenario as possible, described as follows.
## 4 Discussion
### Red Tide Extent  
The red tide extent associated with red tide intensity can affect the performance of predicting a red tide map. Both the RTI and U-Net models utilize the spectral characteristics of red tide water. If the real intensity of red tide blooms is weak, the unique spectral characteristics of the red tide are not well represented in the satellite images. Red tide attention was issued during the PlanetScope image acquisition period. However, no red tide blooms with high densities were found in any of the images. Red tide intensity generally showed a positive relationship with the extent of red tides. Red tide attention is usually issued by the criteria of red tide cell density (>10 cells mL\({}^{-1}\)), and the criteria have a scale of red tide extent. Red tide blooms occur over a radius of 2-5 km (12-79 km\({}^{2}\)). These criteria imply that the red tide intensity and the extent of the red tide are related. The red tide extent for the individual images was calculated based on the ground-truth red tide map. Among the 36 images, only three showed a red tide extent of 10 km\({}^{2}\) or more. Other images showed a red tide extent of less than 5 km\({}^{2}\). Some images even showed a red tide extent of less than 1 km\({}^{2}\). In fact, the relationship between the extent of red tides and the F-measure level for each image showed a positive correlation. In the case of RTI and U-Net #4, the R\({}^{2}\) values between red tide extent and F-measure are 0.67 and 0.71, respectively. As shown in Table 6, the F-measure of RTI and U-Net #4 in the LE group was low, while that in the HE group was at a reasonable level. This means that the wider the red tide extent in the image, the higher the performance of the predicted red tide map.
# Multi-Switching Combination Synchronization of Three Fractional-Order Delayed Systems
## 1 Introduction
In multi-switching combination-combination synchronization scheme, the state variables of two drive systems synchronize with different state variables of two response systems simultaneously, which makes the security of this scheme higher than that in [32]. Since time-delay is a frequently encountered phenomenon in real applications, and the time-delay can be used as an additional parameter in synchronization to increase security in secure communication, we consider the work in [32] to investigate multi-switching combination synchronization scheme for non-identical fractional-order delayed systems by introducing time-delays in fractional-order systems. The rest of this paper is organized as follows. In Section 2, the concept of fractional calculus and the stability theory of linear fractional-order systems with multiple time-delays are briefly introduced. The multi-switching combination synchronization scheme of three non-identical fractional-order delayed systems is analyzed in Section 3. In Section 4, numerical simulations performed using MATLAB are presented. Finally, conclusions are drawn in Section 5.
###### Abstract  
The inertial navigation system (INS) is a robust and reliable navigation strategy to provide position, attitude and velocity information of a carrier with signal acquired from inertial sensors without external assistant. However, the lack of external correction information leads to the accumulation of navigation errors, thereby limiting the reliability and applicable range of INS. In a typical INS, the accuracy and robustness of INS are mainly hindered by sensor's measuring accuracy, installation misalignments and navigation algorithm effectiveness. To address the limitations of navigation accuracy degradation caused by sensor measurement errors, a calibration and compensation method of the gyro bias was proposed to improve the navigation accuracy. Through analyzing the influence of individual navigation errors, we found that the bias noise of gyroscope is the dominant factor in degrading the navigation accuracy. Aiming to improve the performance of navigation, a rotational modulation method is employed to eliminate the influence of gyro bias drift. Specifically, the rotational modulation could average the gyro bias to zero through the periodic rotational mechanism. Furthermore, the rotational turntable output angle can be used to correct navigation-resolved attitude results, which has a highly precise angle and can be used to calibrate the gyro drift. By compensating for gyro bias in a navigation algorithm, the performance of the navigation results is improved by a matter of one order from 7 km to less than 1 km over a period of 6 h. Several individual navigation experiments were also conducted, and the results prove the effectiveness of our method. The theoretical and experimental results show that the proposed error analysis and the compensation method are feasible and can been applied to the practical navigation system.  
rotational inertial navigation system (RINS); navigation error model; inertial measurement unit; gyro bias +
Footnote †: journal: Applied sciences  
Article
## 3 System Modelling and Analysis  
To analyze the protocol accurately, a frameless slotted-ALOHA system with \(M\) active users is establish based on the elaboration of the protocol principle and the parameter setting in Section 2. Set the length of each slot as \(T_{s}\). At the beginning of a slot, \(M\) active users try to send their packets to a common receiver. It is assumed that whether each user has a packet to send in a slot is independent. According to the slotted-ALOHA protocol, active users can only send their packets synchronously at the beginning of a slot. All active users in the system are either free or backlogged.
# Regeneration Performance of Activated Carbon for Desulfurization
## Results and Discussion  
### Characterization of AC  
#### 3.1.1 SEM Analysis  
The SEM images of the six AC samples after five times regeneration are shown in Figure 2a-f. From the aspects of pore structure, the surface of the samples appeared to have a structure with more micropores and mesoporous (Figure 2b) after thermal regeneration. Additionally, some micropore structures were observed within the macropores as in Figure 2e,f. After the 8 th cycle, the pores were irregularly arranged and partly collapsed. Comparing the surface morphology of the AC before and after desulfurization, some white particles with irregular shape were deposited on the surface of the samples after regeneration, which could have been sulfate. Some of the micropores became clogged with these particles. The results indicated that new micropores are generated after regeneration, which is conducive to the adsorption of SO2 by AC, while the deposition of sulfate lead to a decline in desulfurization activity with continued cycles of regeneration.
# Uniform Linear Antenna Array Beamsteering Based on Phase-Locked Loops
## 2 Antenna Basics  
The example problem in this paper is to design an ULA antenna array at 2.4 GHz which has a gain of 10 dB and can be steered through \(\pm 60^{\circ}\) in azimuth. The full design criteria are given in Table 1.  
The basic building block of an array is a single antenna, which is referred to as an element in array terminology. For simplicity, in this paper, we use a half-wave dipole positioned a quarter-wave over a ground plane (Ref. [1] gives details on dipole design and fabrication), other element types such as patches or slots are often used in printed arrays. The performance of this dipole will be stated by its frequency, return loss, and gain/radiation pattern. The frequency of the antenna is the frequency at which it has the highest return loss. (The return loss of the antenna is the dB ratio of the power applied to the antenna and the power reflected back from it.) The gain of an antenna is \"the ratio of the intensity, in a given direction, to the radiation intensity that would be obtained if the power accepted by the antenna was radiated isotropically\" [2]. The 3 dB beamwidth is the angle over which the power does not drop below 3 dB of peak power. Typically, antenna directivity is shown in the radiation pattern, often directivity is quoted as a single number in which case it is the peak directivity, which is defined as the ratio of the radiation intensity in a given direction from the antenna to the radiation intensity averaged over all directions [2]. The 10 dB bandwidth is the frequency span over which the return loss is greater than 10 dB.
## 6 Relationship Between RF Input Power and EVM for Different Modulation Formats
Within this scenario, the significance of equalization algorithms is of utmost importance since it is obvious from Figure 8 that the signal in higher modulation formats such as 16-QAM, 64-QAM, or 128-QAM can only reach the corresponding 3 GPP thresholds with equalization. The proposed algorithms, maximum likelihood detection (MLD) and decision feedback equalizer (DFE), are specifically developed to more efficiently reduce the impact of ISI and noise. By implementing these techniques, it is feasible to attain reduced EVM values and enhanced signal quality, which is crucial for maximizing the performance of optical communication systems.
## 4 Data Processing and Features
### Deep Learning
#### 5.2.4 Convolutional Neural Networks (CNNs)  
CNNs are biologically inspired such as ANNs. Their design is inspired by the brain's visual cortex, which alternates basic and sophisticated cells. CNNs are feed-forward networks that exclusively pass information from inputs to outputs. CNN designs include modules of convolutional and pooling layers. CNN has Input Layer, Convolution Layer, Pooling Layer, Dense Layer, and Output Layer [22; 36; 64].  
**Advantages of using CNN**[64]  
* Compared to conventional classification algorithms, they have lower pre-processing overhead and can teach themselves new filters and attributes.
* Another important benefit offered by CNNs is the distribution of weight.  
**Disadvantages of using CNN**[64]  
* For the CNN to function properly, a significant amount of training data are required.
* Because of procedures such as maxpool, CNNs often have substantially higher latency.
# Position Control of a Mobile Robot through Deep Reinforcement Learning
## 3 Position Control  
The problem of controlling the position of a mobile robot consists of maneuvering the vehicle from its current position, \(C(x_{c},y_{c})\), to a target point, \(\text{TP}=\big{(}x_{p},\ y_{p}\big{)}\), manipulating the angular velocity, \(\omega\), and the linear velocity, \(v\), of the mobile robot. To achieve this maneuver, we define the distance \(d\) (Equation (2)), the angle \(\alpha\) (Equation (3)) between the robot and the TP and the angular error \(O_{c}\) (Equation (4)) from the scheme in Figure 2. A graphical scheme of the control of the position of a differential robot is presented in this figure.  
\[d=\sqrt{\big{(}y_{p}-y_{c}\big{)}^{2}+\big{(}x_{p}-x_{c}\big{)}^{2}} \tag{2}\]  
\[\alpha=\tan^{-1}\bigg{(}\frac{y_{p}-y_{c}}{x_{p}-x_{c}}\bigg{)} \tag{3}\]  
\[O_{c}=\tan^{-1}\bigg{(}\frac{\sin(\alpha-\theta)}{\cos(\alpha-\theta)}\bigg{)} \tag{4}\]The angular error \(O_{c}\) lies between the \(\pi\) and \(-\pi\) values and is equivalent to the subtraction between \(\alpha\) and \(\theta\) within that interval. To get the robot to reach the TP, we seek to reduce the distance and the angular error between \(\alpha\) and \(\theta\).  
By using control algorithms, such as the Villela algorithm and the IPC algorithm, a set of rules to be followed for the control law can be defined. Thus, Figure 3 shows the block diagram of the implementation of the control law for the control algorithms; in this work, the Villela and IPC algorithms are used, which have their control laws defined by Equations (5) and (6) for Villela and by Equations (7) and (8) for IPC.  
\[v=\begin{cases}v_{max}&if\ |d|>k_{r}\\ d\left(\frac{v_{max}}{k_{r}}\right)&if\ |d|\leq k_{r}\end{cases} \tag{5}\]  
\[\omega=\omega_{max}\sin(\alpha-\theta) \tag{6}\]  
where \(v_{max}\) and \(\omega_{max}\) are the maximum values of the lineal and angular velocities, respectively, and \(k_{r}\) is a radio of a docking area of the TP.  
\[v=\min\{k_{v}\ p(O_{c})\ d,\ v_{max}\} \tag{7}\]  
\[\omega=k_{p}\sin(O_{c})+k_{i}\int_{0}^{t}O_{c}\ dt \tag{8}\]  
where \(k_{v}\), and \(k_{i}\) are the tuning parameters of the IPC algorithm obtained empirically and \(p(O_{c})=1-|O_{c}|/\pi\), for \(O_{c}\in[-\pi,\pi]\)[4].  
Figure 2: Position control of a differential robot [4].  
Once the problem of position control has been solved, we can add obstacles to the trajectory of the robot and avoid them. This can be archived by adding a new block of obstacle avoidance, such as the Braitenberg algorithm [46]. This method builds a weighted matrix that converts the measurements of obstacle sensors (eight infrared sensors for the Khepera) into new lienal and angular velocities, just as how its shown in Figure 4. For more details, see [46].
###### Abstract  
The traditional approach of considering the probability distribution of rain attenuation leads to provide very large power margin (overdesign) in data channels. We have extended a method which, with a small power margin, bandwidth expansion and variable symbol rate, avoids overdesign and can transfer the same data volume as if the link were in clear-sky conditions. It is characterized only by the link mean efficiency, suitably defined. It is useful only if: (a) data must be up- and downloaded when it is raining; (b) real-time communication is not required. We have applied it to the links of GeoSurf satellite constellations (in which, at any latitude of ground stations, propagation paths are at the local zenith) by simulating rain attenuation time series at 80 GHz (mm-wave)-the new frontier of satellite frequencies-with the Synthetic Storm Technique, from rain-rate time series recorded on-site, at sites located in different climatic regions. The power margin to be implemented at 80 GHz ranges from 2.0 dB to 7.4 dB-well within the current technology-regardless the instantaneous rain attenuation. The clear-sky bandwidth is expanded 1.75 to 2.80 times, a factor not large per se, but it may challenge current technology if the clear-sky bandwidth is already large.  
s 2024/13/1639/10/399/10/1399  
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, 20133 Milan, Italy; [EMAIL_ADDRESS]  
bandwidth expansion; GeoSurf constellations; link efficiency; power margin; rain attenuation; synthetic storm technique
## 3 Results and Discussion
### Quantitative Analysis Confirms Variations in Amino Acids, Catechins, and Caffeine  
Amino acids, catechins, and caffeine have been reported to be the main active ingredients in _C. sinensis_ green tea and the main substances affecting the taste of _C. sinensis_ green tea [14; 33]. To precisely determine the content differences of three tea samples, including amino acids, catechins, and caffeine, were quantified using a typical HPLC analysis. As shown in Table 1, the average total amino acid concentration in GT, QLC, and QDC was 28.72 \(\pm\) 1.27, 3.59 \(\pm\) 0.06, and 4.59 \(\pm\) 0.36 mg\(\cdot\)g\({}^{-1}\), respectively. Theanine is one of the most abundant free amino acids in _C. sinensis_ green tea and has many benefits for human health [36]. In this study, the concentration of theanine was 15.97 \(\pm\) 0.99 mg\(\cdot\)g\({}^{-1}\) in GT similar to previous studies [33], whereas the concentration was only 0.01 \(\pm\) 0.00 mg\(\cdot\)g\({}^{-1}\) in QLC, and not detected in QDC. Moreover, the concentration of total amino acids was significantly lower in QLC and QDC than in GT, and the concentration of almost all of the amino acids exhibited similar variations. Total catechin concentrations in QLC and QDC were 19.95 \(\pm\) 0.99 and 22.04 \(\pm\) 3.07 mg\(\cdot\)g\({}^{-1}\), which were significantly lower than the concentration in GT (130.27 \(\pm\) 5.87 mg\(\cdot\)g\({}^{-1}\)). Gallocatechin, gallocatechin gallate, epicatechin-3-gallate, and catechin gallate were not detected in QLC and QDC. Epigallocatechin gallate, catechin, and epicatechin were detected in all the three tea samples, but considerable variations in concentrations were observed.
# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
###### Abstract  
In large-scale distributed sensor networks, sensed data is required to be relayed around the network so that one or few sensors can gather adequate relative data to produce high quality information for decision-making. In regards to very high energy-constraint sensor nodes, data transmission should be extremely economical. However, traditional data delivery protocols are potentially inefficient relaying unpredictable sensor readings for data fusion in large distributed networks for either overwhelming query transmissions or unnecessary data coverage. By building sensors' local model from their previously transmitted data in three matrixes, we have developed a novel energy-saving data relay algorithm, which allows sensors to proactively make broadcast decisions by using a neat matrix computation to provide balance between transmission and energy-saving. In addition, we designed a heuristic maintenance algorithm to efficiently update these three matrices. This can easily be deployed to large-scale mobile networks in which decisions of sensors are based on their local matrix models no matter how large the network is, and the local models of these sensors are updated constantly. Compared with some traditional approaches based on our simulations, the efficiency of this approach is manifested in uncertain environment. The results show that our approach is scalable and can effectively balance aggregating data with minimizing energy consumption.  
proactive data relay; information fusion; large distributed sensor networks; matrix-based computing +
Footnote †: journal: Sensors