esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Effect of GoP Size on the Performance of Proposed JD-TV  
In this subsection, the proposed JD-TV is assessed and equated with independent BCS-TV-AL3 using various GoP sizes. Table 3 shows the impact of three different GoP sizes (3, 5, 8) on the performance of the proposed JD at various subrates for several video sequences. The results shown are the average values of the non-key frames obtained for each complete video sequence. The result shows that the proposed scheme performs well when compared with independent BCS-TV-AL3. For low, moderate, and high content types, the improvement over independent BCS-TV-AL3 is 3 dB- 7 dB, 2.5 dB-5 dB and 2 dB-4 dB, respectively, on average for all GoP sizes. Also, it is also noticed that the gain for reconstructed low content video sequence is better than moderate and high content as the association between the frames is higher than in moderate and high content videos. The better correlation results in more precise frame estimation and residual reconstruction. \begin{table}
\begin{tabular}{c l l l} \hline  & **Video Sequence** & **No. Frames** & **Content-Type** \\ \hline \multirow{4}{*}{**CIF**} & Hall-Monitor & 300 & Low \\  & Mother Daughter & 300 & Low \\  & Coast-Guard & 300 & Moderate \\
**(352 × 288)** & Foreman & 300 & Moderate \\  & Mobile Calendar & 300 & High \\  & Stefan & 300 & High \\ \hline \multirow{2}{*}{**HD**} & Love Birds & 300 & Low \\  & News & 300 & Moderate \\ \multirow{2}{*}{**(1024 × 678)**} & Book-Arrival & 300 & Moderate \\ \hline \end{tabular}
\end{table} 
Table 2: Various grayscale CIF and HD standard video sequences. It is also observed that the larger the GoP size, the lower the PSNR gain will be. This is because the principle of the proposed scheme is to reconstruct the non-key frames by utilizing the correlation of the keyframes. Thus, the non-key frames closer to the keyframe are more correlated than those further away. For GoP = 3 the average gain is 3 dB to \(\sim\)6 dB, while for GoP = 8 -2 dB to 4 dB is achieved. Additionally, as the subrate increases, the gain decreases. In other words, as the keyframes, F\({}_{\text{K}}\) is encoded at a subrate higher than that of non-key frames F\({}_{\text{NK}}\), it produces a larger measurement, covering the related smaller set of F\({}_{\text{NK}}\) measurements. This, in result decreases the estimation errors of F\({}_{\text{NK}}\) that arises due to smaller set measurements and generates a better form of F\({}_{\text{NK}}\).
# Secure Data Distribution Architecture in IoT Using MQTT
###### Abstract  
Message Queuing Telemetry Transport (MQTT) is one of the standard application layer protocols for the Internet of Things. It uses a publish/subscribe mechanism which organizes a set of clients around a server called the broker, which delivers published data to its intended recipients. This article proposes an architecture that allows MQTT brokers to cooperate and share their data with other interested MQTT brokers. It is a service-oriented architecture that wraps an MQTT broker with a well defined WebSockets-based interface which allows it to offer its topic space and published data to other MQTT brokers. The wrapped MQTT broker is called a broker service, and it discovers other broker services through a discovery service. Each broker service only connects to services that have data its clients are interested. Furthermore, these services are authenticated by obtaining tokens from an authentication service that registers and issues JSON Web Tokens for them. These tokens contain the identity and claims of their owners and they can be verified without contacting the authentication service. The proposed architecture simplifies data sharing and improves the security in scenarios with multiple MQTT brokers where clients can move between them. In these scenarios, the MQTT brokers need to obtain data based on their clients interests, which are constantly changing. It does so by isolating MQTT brokers into services that can be discovered and consumed over well-defined interfaces. The architecture was implemented in javascript using MQTT 3.1.1 standard complaint library. We demonstrate the performance characteristics of our architecture using our implementation through three scenarios, which are designed to compare the delay from publisher to subscriber when they operate within the same MQTT broker and different MQTT brokers. The results show that the overhead of our architecture is around 50% in two synthetic scenarios (performed on a single machine) and around 27% in a third scenario performed on the cloud with multiple virtual machines hosting the broker services and simulated clients.  
IoT; MQTT; security; brokers +
Footnote †: journal: Applied  
1 Information & Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia 1
# Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage
## 2 Materials and Methods
### Field Surveys
Note that the sensitivity of the SAR backscatter to CC of three different crops has been changed in different height and CC in each crop. For maize, the variation of correlation was determined when the maize CC threshold is 75%. This threshold is 85% and 60% for sunflower and wheat respectively.
# Design of Wideband Decoupling Antenna Array for 5G Smartphones at N77/N78/N79/WLAN 5 GHz Bands
## 3 Performance of the Eight-Element Antenna System
### Eight-Element MIMO Antenna System Prototype
#### 3.1.4. DG  
The diversity gain describes the ability to distinguish independent multipath signals on a MIMO antenna system and can be derived from (2) by ECC.  
\[DG(m,n)=10\sqrt{1-ECC(m,n)^{2}} \tag{2}\]  
Figure 14: (a)Total efficiency. (b) Radiation efficiency. (c) Peak gain.  
Figure 15: Calculated ECC.  
The diversity gain is correlated with the envelope correlation coefficient. In Figure 16, it can be clearly observed that the DG is higher than 9.99 dB at an operating frequency of between 3.30 and 5.83 GHz, indicating that the diversity gain performs well.
# CNT-PUFs: Highly Robust and Heat-Tolerant Carbon-Nanotube-Based Physical Unclonable Functions +
## 4 Fabrication and Implementation of CNT-PUFs
### CNT-PUF Quantisation  
The fabricated CNT-PUFs provide a binary response, which is extracted through quantisation of analogue measurements of the PUF cells. Therefore, for each CNT-PUF, we assigned the measured values of each of the 144 PUF cells to one of two classes, representing logical 0s and logical 1s. Here, non-conducting cells are defined as logical 0 and conducting cells are defined as logical 1, such that a CNT-PUF returns a binary response \(R_{p}\) with \(|R_{p}|\coloneqq 144\). This assignment was based on the quantisation of \(I_{D,\text{max,p}}\) gained from the measurements for each PUF cell using a threshold \(I_{th}\).  
The proposed quantisation procedure accepts an array of measurements \(M_{p}\coloneqq\{I_{D,\text{max,p}}^{0},I_{D,\text{max,p}}^{1},\ldots,I_{D,\text {max,p}}^{143}\}\), where each element is the current \(I_{D,\text{max,p}}\) of each cell of a single CNT-PUF \(p\), and compares each \(I_{D,\text{max,p}}\) with a threshold \(I_{th}\). If \(I_{D,\text{max,p}}<I_{th}\), a logical 0 is added to the binary PUF response \(R_{p}\); otherwise, a logical 1. Finally, a 144-bit binary PUF response is returned.  
In order to reliably assign the measured values to a class under repeated measurements, a suitable value for the threshold \(I_{th}\) must be determined. In Figure 4, the distribution of raw measurements of a single CNT-PUF (consisting of 144 PUF cells) is shown. The light grey dashed lines indicate the window of acceptable thresholds, where conducting and non-conducting cells can be clearly separated with only a few outliers. A threshold \(I_{th}\) can be set within a broad range between \(I_{D}=7\,\text{pA}\) and \(I_{D}=100\,\text{pA}\). By placing the threshold \(I_{th}\) within this window, a stable PUF response can be generated for almost all measurements and PUF cells, as indicated by the dark grey line denoting an exemplary threshold in Figure 4. The selection of the best \(I_{th}\) value to achieve the most-stable responses is discussed in Section 5.2.4.  
Figure 3: Illustration of a transfer curve applying pulsed sweeps across the gate voltage \(V_{GS}\), between \(-4\,\text{V}\) and \(4\,\text{V}\).
## 4 Experiment and Results Analysis
### Computational Performance Analysis  
To evaluate the computational efficiency of MSF-SLAM, we benchmarked resource usage against ORB-SLAM3 on two platforms: a high-performance laptop (Intel i9-14900 HX, NVIDIA RTX 4060, 32 GB RAM) and an edge device (NVIDIA Jetson Xavier NX, 8 GB RAM). Table 8 summarizes the results for the \"walking_xyz\" sequence (1000 frames).  
On the laptop, MSF-SLAM achieves 233.1 FPS with 65% GPU utilization, 8.2 GB VRAM, and 12.5 GB RAM, compared to ORB-SLAM3's 275.1 FPS, 20% GPU utilization, 1.5 GB VRAM, and 6.8 GB RAM. The increased resource demand reflects the integration of YOLOv8+MSFConv, which adds \(\sim\)9.9% computational overhead (8.9 vs. 8.1 GFLOPs). On the Jetson Xavier NX, MSF-SLAM runs at 45.2 FPS with 5.8 GB VRAM, suitable for real-time robotics applications (>30 FPS), while ORB-SLAM3 achieves 78.4 FPS with 1.2 GB VRAM. Per-frame latency for MSF-SLAM is 4.3 ms (laptop) and 22.1 ms (Jetson), compared to ORB-SLAM3's 3.6 ms and 12.8 ms, respectively.  
These results indicate that MSF-SLAM's enhanced accuracy (94.43% ATE reduction) comes with a manageable resource trade-off, enabling deployment on mid-range edge devices for dynamic SLAM tasks, such as autonomous navigation in indoor environments.  
Figure 6: ORB_SLAM3 Operation Trajectory.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metric** & **Platform** & **MSF-SLAM** & **ORB-SLAM3** \\ \hline FPS & Laptop & 233.1 & 275.1 \\ FPS & Jetson Xavier NX & 45.2 & 78.4 \\ GPU Utilization (\%) & Laptop & 65 & 20 \\ VRAM Usage (GB) & Laptop & 8.2 & 1.5 \\ RAM Usage (GB) & Laptop & 12.5 & 6.8 \\ Latency (ms/frame) & Laptop & 4.3 & 3.6 \\ Latency (ms/frame) & Jetson Xavier NX & 22.1 & 12.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Computational Performance Comparison.
# A SAM-Based Detection Method for the Distance Between Air-Craft Fire Detection Lines
## 4 Discussion  
### Model Robustness  
From the visualization results, we notice that the model can segment small sections of the fire detection lines that the annotators miss, such as the small segment in the lower right corner in Figure 5. This segment is missed due to its texture and color being very similar to the surrounding environment, leading to human error in manual annotation. Similarly, in Figure 6, the model identifies a small segment of the fire detection line in the lower left corner that the annotators miss due to its minuscule size, also leading to human error. This qualitatively demonstrates that the model is robust enough to handle similar textures and small objects.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Loss Function** & **Dice** & **Target Task Accuracy** \\ \hline ASAM & Dice Loss & 85.17 & 87.16 \\  & Fusion loss function & 85.28 & 95.45 \\ K-Net & Dice Loss & 72.07 & 79.63 \\  & Fusion loss function & 72.44 & 87.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dice and target task accuracy of ASAM and K-Net when applying different loss functions.
## 6 Case Study
### Process Robustness Analysis
#### 6.3.1 Process Robustness Comparison of Three Substitution Strategies under ID Failure Mode  
As shown in Figure 11, (1) in the ID failure mode, the RoS values under the no-substitution strategy (\"common\") and the three resource substitution strategies (\"internal\", \"external\", and \"inter&exter\") all show a decreasing trend as the number of node failures  
Figure 10: Variation of S under RB failure mode.  
increases (i.e., as the cloud order completion rate decreases). This indicates that from a multi-agent simulation perspective, the robustness of the CMP gradually decreases. (2) The curves of the three resource substitution strategies are all located above the curve of the no-substitution strategy (i.e., their RoS values are larger than the no-substitution strategy's value, which means their order completion rates are higher). This indicates that all three resource substitution strategies can improve the robustness of the CMP in the face of initial node degree failure. (3) The \"internal\" curve is only slightly higher than the \"common\" curve, and when the number of node attacks is high, the RoS values of these two strategies decrease to 0, at which point all cloud orders fail to be processed. In contrast, the \"external\" and \"inter&exter\" curves are significantly higher than the \"common\" curve, and though the RoS values fluctuate when the number of node attacks is high, they still maintain a high level (above 0.6). This indicates that the external replacement strategy and the internal-external integration replacement strategy both offer more significant robustness enhancement than the initial replacement strategy under the ID failure mode. In particular, when the number of node attacks is high, the slight fluctuations of the RoS value indicate that the cloud order completion rate tends to be stable at this time, but different resource substitution strategies will lead to changes in the service time, cost, reliability, and other factors.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 4 Acoustic Emission Source Localization Techniques for Three-Dimensional (3D) Structures
### Triaxial Sensor Approach  
The idea of this method is that if the spatial position of a point is known, then the spatial positioning of any other point can be represented by its relative distance and azimuth to the known point [96]. Source S is to be determined in terms of its relative distance, d, and its azimuth (\(\alpha\), \(\beta\), \(\gamma\)).  
The relative distance can be expressed as:  
\[d=\frac{v_{p}v_{s}}{v_{p}-v_{s}}(t_{s}-t_{p})\] \[\alpha=\left(\cos l\right)^{-1},\ \beta=\left(\cos m\right)^{-1}, \gamma=\left(\cos p\right)^{-1}\] \[l=\frac{A_{x}}{A},\ m=\frac{A_{y}}{A},p=\frac{A_{z}}{A}\] \[A=\sqrt{{A_{x}}^{2}+{A_{y}}^{2}+{A_{z}}^{2}}\]  
where \(d\) is the distance from the source to the sensor, \(v_{p}\) and \(v_{s}\) are the velocities of P-and S-waves, and \(t_{p}\) and \(t_{s}\) are the arrival times of P- and S-waves. The relative azimuth (\(\alpha\), \(\beta\), \(\gamma\)) can be determined by the amplitude of the received signals, assuming that \(A_{x}\), \(A_{y}\) and \(A_{z}\) are the amplitudes of three mutually orthogonal components of the signal, where \(l\), \(m\) and \(p\) are the direction cosines of the signal relative to the sensor.  
The triaxial sensor approach requires fewer sensors, and for deep applications it can reduce the number of boreholes, resulting in significant cost savings. This method has been used in various deep applications, such as rock burst research, the stability of oil reserve sites and geothermal engineering. This method also has two technical advantages. Firstly, as this method uses both P-waves and S-waves to reach, it improves the stability of the source position solutions for these external array events. Secondly, it allows people to analyze the ground motion status of sensor locations, which is crucial for studying event magnitude and source mechanisms.  
The main technical issue with this method is the stability of the azimuth angle, as amplitude is more sensitive to material properties and structure than travel time.
## Materials and Methods
### Testing System of the Micro-Fluxgate Sensor  
Generally, a fluxgate sensor measuring system is based on the second harmonic principle that consists of excitation and sensing circuits. The excitation circuits must ensure the magnetic core is working in a deep saturation state, which results in a noticeable variation of permeability of the core when an external magnetic field is added. The sensing circuits should be able to pick up the second harmonic signal effectively from the output of the pick-up coil. In this work, we established a measuring system that included a signal generator, a power amplifier, a biquadratic bandpass filter, and an oscilloscope. A block diagram of the measuring system is shown in Figure 3a.  
Figure 2: **(a–h)** The detailed fabrication process of the micro-fluxgate sensor; **(i)** the fabricated micro-fluxgate sensor; **(j)** images of the bottom coil and vias; **(k)** the cross-sectional image of the sensor.  
The signal generator (Tektronix AFG 3022, Tektronix, Beaverton, OR, USA) was set to provide a sine wave signal. Because the power of the signal provided by the generator was too small to drive the fluxgate sensor, a power amplifier printed circuit board (PCB) power amplifier circuit, (see Figure S1 in detail) was needed to ensure a powerful enough excitation signal. A biquadratic bandpass filter (Mitron LPFD-3040+, Mitron Interlink, Inc., San Chung, Taiwan) was used to pick up the second harmonic signal from the output signal of the pick-up coil. The measuring results were read with the oscilloscope (Tektronix TDS2014B, Tektronix, Beaverton, OR, USA). In order to guarantee the accuracy of the test results, a cylindrical magnetic shield (10 layers of FeNi thin films) was used to protect of our sensor from the environmental magnetic field. Figure 3b shows a photograph of the test system.
# ndInIoT-FC: IoT Devices as First-Class Traffic in Name Data Networks
## 4 Testbed Platform and Use Cases
### Platform  
In the scope of the IoT paradigm, the communication must allow the seamless integration of any object, allowing new forms of interaction between people and devices, or directly between devices (machine-to-machine). Our software testbed architecture, illustrated in Figure 8, aims to provide a city-wide scenario with heterogeneous elements. The platform can be divided into three main groups. Data producers (edge devices), data consumers (Cloud services and applications), and ICN routers (blue devices) to interconnect producers and consumers.  
On the edge of network we have the data producers, Data Collection Units (DCUs), where sensors sets (temperature, luminosity, sound, etc.) are housed (Such DCUs present a similar configuration as the ones placed in the city of Aveiro, under the scope of Aveiro STEAM City project ([[https://uia-initiative.eu/en/uia-cities/aveiro](https://uia-initiative.eu/en/uia-cities/aveiro)]([https://uia-initiative.eu/en/uia-cities/aveiro](https://uia-initiative.eu/en/uia-cities/aveiro)))). To control and capture the sensor measurements, the DCUs have a Gathering Service (denoted as GS) that periodically collects the data and stores it locally. The DCUs also contain the Producer Service (PS) that serves those interested in such information, i.e., the applications. All DCUs on our platform are configured as ICN nodes and prepared to send and receive packets according to this paradigm. As illustrated in Figure 8, for the use cases to be discussed in this paper, the DCUs will be ICN producers, as they are the source of sensor measurements.  
The Cloud hosts the services and applications that subscribe the edge services to get the sensors information. As show in the Figure 8, we have a Consumer Service (CS) that periodically receives data from GSs and puts them into Cloud databases. Such information can be used for Cloud applications such as user dashboards. In addition, of course, the Cloud has at least one ICN node configured, the ICN consumer node.  
Figure 8: ICN IoT Platform with pub-sub services only.  
As we will demonstrate in our use cases, such platform is flexible, scalable and can operate in a homogeneous or hybrid way. We can easily add services both in the Cloud and on the edge, we can configure routes with different link costs, can configure several types of communication interfaces operating simultaneously on the network nodes and also increase/decrease the number of sensors and the number of DCUs.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 4 Results and Discussion
### Channel Coding and Interleaving in LoRa
#### 4.1.1 Interleaving  
Interleaving is a process that scrambles data bits throughout the packet. It is often combined with forward error correction (FEC) to make the data more resilient to bursts of interference [31]. According to the patent [13], a diagonal interleaver is implemented in LoRa chips.
## 2 Basic Theory and Calculation Method
### [PERSON]' Solution  
Based on the above formulas, [PERSON] proposed to use plane loss rate \(\varepsilon_{x,z}\) to quantify stratum loss with non-equivalent radial movement. The expression is as in Equation (6):  
\[\varepsilon_{x,z}=\frac{4 gR+g^{2}}{4R^{2}}\exp\left\{-\left|\frac{1.38x^{2}}{ \left(H+R\right)^{2}}+\frac{0.69z^{2}}{H^{2}}\right|\right\}. \tag{6}\]  
Combined with [PERSON]'s [71; 72] analytical formula, he put forward a formula for estimating stratum displacement caused by undrained stratum loss in clays, of which the formulas are as in Equation (7):  
\[\left\{\begin{array}{l} S_{z}=R^{2}\left\{-\frac{z-H}{x^{2}+ \left(z-H\right)^{2}}+(3-4\mu)\frac{z+H}{x^{2}+\left(z+H\right)^{2}}-\frac{2z \left[x^{2}-\left(z+H\right)^{2}\right]}{\left[x^{2}+\left(z+H\right)^{2} \right]^{2}}\right\}\times\varepsilon_{x,z}\\ S_{x}=-R^{2}x\left\{\frac{1}{x^{2}+\left(z-H\right)^{2}}+ \frac{(3-4\mu_{u})}{x^{2}+\left(z+H\right)^{2}}-\frac{4z(z+H)}{\left[x^{2}+ \left(z+H\right)^{2}\right]^{2}}\right\}\times\varepsilon_{x,z}\end{array} \right.. \tag{7}\]  
Considering that the displacement solution caused by stratum loss derived by Loganathan is a two-dimensional solution, which is often a three-dimensional problem in actual engineering, it is necessary to extend the two-dimensional displacement solution to three-dimensional. In the above deduction process, we can find that the stratum loss parameter \(\varepsilon_{x,z}\) is the key for extending. If we extend the plane stratum loss parameter \(\varepsilon_{x,z}\) to three-dimensional parameter \(\varepsilon_{x,z,y}\), the displacement solution can be extended to three-dimensional space. Another important method to solve the stratum displacement caused by stratum loss is used here. The concept of stratum loss proposed by [PERSON] proposed the mirror source-sink method and derives the problem of free displacement field by elastic half-space model. Although field monitoring data [40] show that the movement pattern of actual volume loss is not consistent with the assumed equivalent radial movement pattern in this method, its process is a strict mathematical derivation process, so the three-dimensional stratum loss parameter \(\varepsilon_{x,z,y}\) can be extracted from the [PERSON] solution.