esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Mitigation Methods for FSM Using Triple Modular Redundancy
## 1 Introduction
Design constraints imposed by, for example, the availability of FPGA logic resources or performance expectations are driving the development of ever-newer logic synthesis methods: [24; 25; 26] or [27; 28]. Appropriate logic synthesis methods are, therefore, required [29; 30; 31; 32; 33]. In the paper [34], a software-hardware co-design approach for the development of a power-efficient real-time traffic signal classification system tailored for FPGAs utilizing the convolutional neural network algorithm is proposed. Low-power strategies toward FPGA-based wearable medical device for physiological monitoring are described in [35]. Techniques like Clock Enable (CE) inputs utilization [36] or specific register-transfer level code generation [37] may be used to design low-power modules. Naturally, the key from the perspective of a low-power minimization-oriented synthesis strategy is what type of circuits will be synthesized. In general, combinatorial and sequential circuits can be distinguished in digital technology. In the case of combinatorial circuits, the aim is generally to reduce switching activity [30; 31]. In the case of sequential circuits, on the other hand, power reductions can be achieved by appropriate encoding of internal states [33]. Sequential circuits are the dominant group in digital technology. An important group here is the finite state machine (FSM). The question arises as to how TMR-based mitigation can most effectively be fitted into the FSM. There is also the question of which blocks in the FSM should be triplicated and where TMR-based voters should be included in the FSM structure. The main objective of this paper is to present the different possibilities of applying triple modular redundancy in finite state machines. Experiments with different benchmarks have been realized. The different structures have been synthesized and compared in terms of logic utilization, number of logic levels, and power dissipation. Error-correction efficiency and recovery capability have also been analyzed. This article is an extension of the authors' earlier article on voting circuits [38], towards triple modular redundancy for sequential circuits. The article is divided into sections. Section 2 contains a description of the FSMs and the essence of the TMR. Section 3 presents the authors' proposed models for the implementation of TMR in the FSM structure and is followed by a summary in (Section 4). Section 5 presents the results of the experiments obtained with both academic and commercial tools. The article ends with some conclusions in Section 6.
## 3 Proposed Solutions
### Closed-Form Solution by Nullspace Projection
\begin{bmatrix}\mathbf{p}\\ \mathbf{v}\\ \gamma\\ \iota\\ \iota^{2}-\|\mathbf{v}\|^{2}\\ \gamma\iota-\mathbf{p}^{T}\mathbf{v}\end{bmatrix} ,\quad\mathbf{H}= \begin{bmatrix} \mathbf{I}_{N}&\mathbf{O}_{N}&0&0\\ \mathbf{O}_{N}&\mathbf{I}_{N}&0&0\\ \mathbf{O}_{N}&\mathbf{O}_{N}&1&0\\ \mathbf{O}_{N}&\mathbf{O}_{N}&0&1\\ \mathbf{O}_{N}&-2\mathbf{v}^{T}&0&2\iota\\ -\mathbf{v}^{T}&\mathbf{p}^{T}&\iota&\gamma\end{bmatrix} ,\quad\Delta \boldsymbol{\mu}= \begin{bmatrix}\Delta\mathbf{p}\\ \Delta\boldsymbol{\mathrm{v}}\\ \Delta\gamma\\ \Delta\iota\end{bmatrix} . \tag{35}\]  
Inserting (34) into (10) gives a new equation with respect to the estimation errors:  
\[\mathbf{h}-\mathbf{G}\boldsymbol{\phi}+\mathbf{G}\mathbf{H}\Delta\boldsymbol{ \mu}=\mathbf{B}\boldsymbol{\varepsilon}+\mathbf{C}\Delta\mathbf{s}, \tag{36}\]  
Similarly, the estimation of \(\Delta\boldsymbol{\mu}\) can be obtained through the WLS as  
\[\Delta\hat{\boldsymbol{\mu}}=-(\mathbf{H}^{T}\mathbf{G}^{T}\mathbf{W}_{2} \mathbf{G}\mathbf{H})^{-1}\mathbf{H}^{T}\mathbf{G}^{T}\mathbf{W}_{2}(\mathbf{ h}-\mathbf{G}\boldsymbol{\phi}) \tag{37}\]  
where the weighting matrix  
\[\mathbf{W}_{2}=(\mathbf{B}\mathbf{Q}_{\ell}\mathbf{B}^{T}+\mathbf{C}\mathbf{Q} _{s}\mathbf{C}^{T})^{-1}. \tag{38}\]  
Finally, subtracting the estimated error \(\Delta\hat{\boldsymbol{\mu}}\) from \(\hat{\boldsymbol{\mu}}\), we obtain a better solution:  
\[\hat{\boldsymbol{\mu}}=\hat{\boldsymbol{\mu}}-\Delta\hat{\boldsymbol{\mu}}. \tag{39}\]  
The weighting matrices \(\mathbf{W}_{1}\) and \(\mathbf{W}_{2}\) depend on unknown parameters to be estimated. We shall set them as identity matrices to generate a pre-estimation, which will be put back to update \(\mathbf{W}_{1}\) and \(\mathbf{W}_{2}\) to obtain a better result. Repeating this one or two times is sufficient for an accurate solution, as the estimation is not very sensitive to the weighting matrices [28; 29; 30].
# Minimized Triple-Band Eight-Element Antenna Array for 5G Metal-frame Smartphone Applications
###### Abstract  
A multiple-input-multiple-output (MIMO) antenna array for triple-band 5G metal-frame smartphone applications is proposed in this paper. Each single antenna element consists of an S-shaped feeding strip and an L-shaped radiation strip on the metal frame. The dimension of the antenna element is only 6.5 mm \(\times\) 7 mm (0.076 \(\lambda_{0}\)\(\times\) 0.082 \(\lambda_{0}\), \(\lambda_{0}\) is the free-space wavelength at the frequency of 3.5 GHz). The \(-6\) dB impedance bandwidth of the proposed eight-antenna array can cover 3.3-3.8 GHz, 4.8-5 GHz, and 5.15-5.925 GHz. The evolution design and the analysis of the optimal parameters for a single antenna element are derived to investigate the principle of the antenna. The measured total efficiency is larger than 70%. The measured isolation is better than 13 dB. The measurements of the prototype agree well with the simulation results.  
5G new radio (5G NR); sub-6 GHz; MIMO; triple-band antenna; metal-frame smartphone 2022
# Mechanical Behavior of a Mine Tailing Stabilized with a Sustainable Binder
## 4 Mechanical Behavior of Mine Tailing after Stabilization
### Triaxial Compression Tests  
The triaxial compression tests were carried out based on the BS 1377-7 (1990) standard. These tests were conducted to evaluate the shear strength, cohesion, and friction angle development of the mine tailing after stabilization. Only the mechanical behavior of the mixture M3, which revealed better mechanical performance based on the uniaxial compression test results, was evaluated. This mix comprises 55% mine tailing and 45% fly ash. All specimens were manufactured with 600 g of mine tailing, 500 g of fly ash and 200 g of sodium hydroxide in a concentration of 10 molal. All specimens were tested after 7 days of curing in a humid chamber at room temperature and with a relative humidity of 88%. The stabilized tailings specimens were consolidated with confining stresses of 50 kPa, 100 kPa, and 200 kPa. To obtain the ICC, the process adopted was the same as described for the unstabilized samples. This specimen was also submitted to the shearing for a tension of 200 kPa (test piece identified as \(\sigma_{3}=200\) kPa (OC)). The shear phase of all tested specimens was carried out under undrained conditions (UC) at the constant deformation rate of \(2.74\times 10^{-4}\) mm/s.
## 3 Objective of This Study  
The development and increase in the number of biomass power plants, as mentioned before, led to the production of increasing quantities of wood bio-ash in Croatia [3]. The positive experience of different applications of wood ash in the construction industry has inspired a growing number of wood ash application studies for usage in pavement structures. One of these studies was a laboratory evaluation of the influence of wood bio-ash (BA), as part of the filler in an asphalt mixture, on the physical-mechanical properties of the mixture, as described in [23]. Different laboratory tests were carried out on the asphalt mixture of asphalt concrete type AC 16 surf, which is used for the construction of leveling layers and wearing layers. A mineral filler (industrial) was used as the basic filler in the AC 16 surf asphalt mixture. As a supplement to the mineral filler, wood bio-ash (BA) was used in different proportions (0%, 25%, 50%, and 75%). The ash was cyclone ash, i.e., fly ash collected on a cyclone deduster. The Marshall stability (MS) and Marshall quotient (MQ) had the highest values in the case of an asphalt mixture with 50% bio-ash (BA) content in the filler, with the lowest deformation-flow ratio, which indicates increased resistance of the asphalt to rutting. The indirect tensile strength values of water-conditioned (ITSw) samples are the highest in the case of an asphalt mixture with a 25% content of bio-ash (BA) in the filler, while, in the case of the dry (ITSd) samples, they are highest in the asphalt mixture with 50% bio-ash (BA) content in the filler, which has an impact on the increased resistance of the asphalt to the appearance of cracks. As the results of the laboratory evaluation of [23] showed, wood ash can replace part of the stone filler in asphalt mixtures with 50% content, improving the performance of the asphalt mixture. According to the described laboratory evaluation [23], research on asphalt mixtures using wood ash in the filler is continuing, and one of these studies is described in this article.  
The aim of this research is to examine the resistance to rutting of asphalt mixtures using bio-ash (50% in the filler; type AC 11 surf asphalt concrete) and to determine the water sensitivity of the asphalt specimens. The aim is also to confirm that wood bio-ash, as a filler covered with bitumen and embedded in the asphalt layer of the roadway, is not harmful to the environment, i.e., that there is no release of hazardous substances during road exploitation.
# Rethinking LEO Mega-Constellation Routing to Provide Fast Internet Access Services
## 4 Evaluation
### Experiment Setting  
We referenced the constellation parameters of first shell in Starlink's initial deployment and built a Walker-Delta constellation to evaluate the algorithm's performance. The specific parameters of the constellation are: 53.0:500/20/11, and the orbital altitude is 550 km. The constellation diagram, in the simulation software OPNET, is shown in Figure 4. Each satellite node moves according to the defined orbital parameters, and when it runs within the visible range of the ground station, it can establish a feed link with the ground station and become a landing satellite. At the same time, in order to simulate the traffic differences due to geographical factors, we divide the world into several square grids, each grid is assigned a traffic weight, based on population density and development level. The traffic generated by the traffic model, multiplied by the weight of the region, is the real traffic of users accessing the satellite network in this region.  
Each satellite can use a phased array antenna to build a satellite-user link and the internet service request rate of users is in a Gaussian distribution \(\mathcal{N}(\mu,\sigma^{2})\), where \(\mu\) represents the mean and \(\sigma^{2}\) represents the variance of this distribution. In the simulation, we model different network load conditions by changing the mean, \(\mu\). Each satellite can establish ISLs with two satellites in the same orbit and two satellites in the adjacent orbit, through the wireless transceiver terminals. Due to all kinds of electromagnetic interference in space, ISLs are unstable. In this paper, based on our experience in engineering testing, we assume that the packet loss rate of a single ISL is 0.5%.  
In the simulation environment, each satellite node in Figure 4 is composed of the satellite node model shown in Figure 5. In the satellite node model, each satellite has four wireless transceiver terminals, to establish ISLs with neighboring satellites. Each transmitting terminal is equipped with a cache queue, for temporarily storing data packets that are not sent out in time. The \"src\" module is used to generate user data received by each satellite, the \"sink\" module is used to delete useless data packets, and the \"proc\" module is mainly responsible for implementing the network layer protocol and the algorithm proposed in this paper. Solid lines with arrows between different modules indicate the direction of data flow between these modules, and dotted lines with arrows indicate the monitoring and acquisition of some parameters in these modules.  
It should be noted that we assume that the terrestrial network is very robust, without packet loss and congestion. In this way, we can better show the performance of the satellite network in data transmission.  
Figure 4: Constellation diagram in the simulation software.  
Figure 5: The model of satellite nodes in the simulation software.
# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 2 Materials and Methods
### Technology Selection
#### 2.2.3 Software  
The software developed for the system is divided into two main categories: server-side software and user-facing applications.  
The network server and application server are responsible for managing and processing the data transmitted by the nodes through the LoRaWAN gateway. Two platforms were selected for this purpose: The Things Network (TTS) as a cloud-based solution [32] and ChirpStack as an on-premise solution [33]. Both solutions allow integration with cloud platforms via MQTT to transmit data to the application server. MQTT (Message Queueing Telemetry Transport) is a standard messaging protocol that has gained significant popularity in an IoT environment. It facilitates the transmission and reception of data across networks characterized by constrained resources and limited bandwidth. In terms of infrastructure, this application server can be based on a virtual server in the cloud or a local server, such as a Raspberry Pi 4 Model B, a cost-effective and versatile single-board computer (SBC) commonly used in IoT applications.  
The implementation of data flows between sensors and applications has been achieved through the use of Node-RED. It is a royalty-free open platform based on a visual flow programming tool that works natively with the MQTT protocol in IoT projects [34]. With Node-RED, boxes and lines are used to communicate with the user and sensors. Boxes represent actions that receive data from sensors, and the data are then transformed and passed to the next box, also called node. Node-RED is essential for making data control applications; it is easy to use, simplifies code, and reduces operations, and one does not require advanced coding skills to use it.  
The user applications enable interaction with the system through a graphical interface that displays the processed data in a clear and user-friendly manner. Three applications have been developed for this purpose and are described below:  
* Web Application: The platform Grafana is used for the graphical visualization of data due to its high level of customization and its seamless integration with a wide variety of data sources.
* Mobile Application: This application allows the poultry farmer to monitor data related to nesting box visits and receive notifications. It was developed for Android using Java, considering the user distribution in Spain and the comparatively lower development costs associated with Android versus other platforms such as Apple's iOS.
* Telegram Bot: A bot integrated into the Telegram messaging platform provides real-time notifications of hen visits to the nesting boxes. This offers a simple and effective means of receiving alerts without the need for additional applications.
## 4 Experiments and Analysis  
To verify the effectiveness of our AD-DAF framework, four experiments are designed in this paper. The first experiment is to verify the applicability of different deep semantic segmentation models in the proposed framework. The second experiment is to verify the effectiveness of the framework for cross-domain building extraction from remote sensing images in large areas. The third experiment is to test the performance of the framework on a benchmark dataset. The fourth experiment is to compare the differences between the framework and the main domain adaptation methods.  
These experiments are implemented via Python on a desktop server with an Intel (R) Core (TM) i9-10900K processor, 32 GB RAM (random access memory). The four experiments are conducted by the Keras library with TensorFlow 2.1 backend. In the pre-training process, the learning rate is \(1\times 10^{-4}\) and the number of epochs is set to 20.  
The Precision, Recall, F1, and IoU (intersection over union) score are selected to quantitatively evaluate the accuracy of the cross-domain building extraction. Specifically, Recall expresses the proportion of correctly extracted buildings to the total buildings in the image [44]. Precision represents the proportion of correctly extracted buildings in the extracted result [44]. F1 is the weighted average of Recall and Precision. IoU is the percentage of the intersection of the predicted and true results in their concurrent sets. The larger their values are, the more effective the method is. They can be calculated by the following equations [3].  
\[\text{Recall }=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}} \tag{1}\]  
\[\text{Precision }=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}} \tag{2}\]  
\[\text{F1}=\frac{2\times\text{Precision }\times\text{Recall}}{\text{ Precision }+\text{ Recall}} \tag{3}\]  
\[\text{IoU }=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}+\mathit{FN}} \tag{4}\]  
where \(\mathit{TP}\) denotes the number of correctly predicted building pixels in the extracted result, \(\mathit{FP}\) denotes the number of incorrect building pixels in the extracted result, and \(\mathit{FN}\) denotes the number of missed building pixels.
## 2 Train Dynamical Model and Problem Statement
### Description of Train Dynamical Model with Actuator Faults and Actuator Saturation
#### 2.2.2 Actuator Saturation  
From a practical perspective, control input is bound to be constrained by actuator output ability caused by various factors, such as maximum output of working motors. Thus, we introduce a saturation function \(sat(F)\) to show the effect of actuator saturation, which has the following form:  
\[sat(F)=\left\{\begin{array}{ll}F_{\max}&F>F_{\max}\\ F&F_{\min}\leq F\leq F_{\max}\\ F_{\min}&F<F_{\min}\end{array}\right., \tag{5}\]  
where \(F_{\max}\) and \(F_{\min}\) represent the upper and lower bounds of \(F\), respectively. Define \(sat(F)=F+\sigma(t)\), where \(\sigma(t)\) can be expressed as follows:  
\[\sigma(t)=\left\{\begin{array}{ll}F_{\max}-F&F>F_{\max}\\ 0&F_{\min}\leq F\leq F_{\max}\\ F_{\min}-F&F<F_{\min}\end{array}\right.. \tag{6}\]
# A Dual-Band Modified Franklin mm-Wave Antenna for 5G Wireless Applications
## 3 Parametric Analysis  
A parametric study analyzes the patch element's impact, and the slots etched in the ground plane on the antenna. The design's performance is mainly influenced by the incremental length (R), the patch element length (P\({}_{\text{L}}\)), the length and width of the slots in the ground plane L\({}_{\text{G}}\), L\({}_{\text{C}}\), W\({}_{\text{G}}\), W\({}_{\text{C}}\).
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 5 Challenges and Open Research Topics
### Open Research Topics
#### 5.2.6 EMF-Aware Radio Access Network (RAN) Slicing  
RAN slicing [113] must evolve to incorporate EMF awareness. Techniques optimizing antenna deployment, transmit power, and frequency bands while meeting EMF constraints can enhance network flexibility and safety.
# Research on Fault Detection and Automatic Diagnosis Technology of Water Hammer in Centrifugal Pump
## 5 Test and Experiment  
### Test Object  
In order to verify the effectiveness of the proposed centrifugal pump water hammer fault diagnosis system and method, a vertical multistage centrifugal pump, model CR10-02 A-FJ-A-E-HQQE, was selected as the test object. Two acceleration sensors were installed at the pump body (measuring point 1) and water outlet (measuring point 2) of the centrifugal pump to monitor the vibration signal changes during the operation of the centrifugal pump, as shown in Figures 7 and 8. The driving power of the centrifugal pump motor used in the experiment was 1.5 kW, and the rotating speed was 3000 r/min.  
Figure 6: Flow chart of data transmission strategy.  
Figure 7: Field experiment photos of centrifugal pump (measuring point 1).
## 3 Example Analysis
### Performance Verification of Large Samples
#### 3.2.2 Analysis of Network Attack and Fault Detection Performance  
We mainly use accuracy, precision, recall, and F1 score as performance indicators to measure network attack and fault detection methods. Accuracy is to measure the proportion of all correctly classified samples in the test set and to measure the overall recognition performance of the model for various samples. Precision is a measure of the proportion of all positive samples identified as positive samples and is an important reference to measure the accuracy of network attack identification. Recall is a measure of the proportion of true samples among all correctly classified samples, and is an important reference to measure the degree of false positives in network attack identification. The F1 score comprehensively considers the factors of precision and recall and is an important reference to measure the performance stability of the network attack recognition model. The F1 score is calculated as shown in Equation (5):  
\[\text{F1}-Score=\frac{2\ *\ \text{Precision}\ *\ \text{Recall}}{\text{Precision}\ +\ \text{Recall}} \tag{5}\]  
The global performance of the CIKS-MFS-FLGB method under large sample conditions is shown in Table 2.  
Compared with the traditional LightGBM classifier, the accuracy of the CIKS-MFS-FLGB method is increased by 14.67%, the precision is increased by 15.67%, the recall rate is increased by 18.46%, and the F1 score is increased by 16.63%. The average prediction time for each sample is 3.5194 milliseconds, and the response speed to network attack detection is very fast. In each optimization stage of the LightGBM algorithm, the accuracy, precision, recall, and F1 score show a steady growth trend. On the whole, the CIKS-MFS-FLGB model greatly improves the detection precision of network attacks and maintains the stability of the power system. To show the local performance of the model in detail, the classification report of the model is shown in Figure 9:  
In order to better reflect the advantages of the model, Figure 9 shows the traditional LightGBM algorithm as a comparison. On the one hand, it can be seen from Figure 9 that the CIKS-MFS-FLGB model can significantly improve the detection precision of network attacks. This advantage is specifically reflected in the improvement of the precision, recall, accuracy, and F1 score of 37 types of data. The precision of the CIKS-MFS-FLGB method is improved by 20.1%. The recall of the CIKS-MFS-FLGB method is improved by 22.81%. The F1 score is also improved by 15.36%. On the other hand, the addition of false samples does not affect the sample precision of each class. Therefore, the noise content of the false samples obtained by the CIKS algorithm is very low, which has little negative impact on the prediction results. The generated samples can better fit the characteristics of the power data.
## 4 Stability Analysis of GFMC APF  
This section examines the stability of the GFMC APF, focusing on the repetitive controller's impact on mitigating harmonic distortions. The voltage and current control loops, along with the associated delays, are modeled to analyze their influence on system stability.