esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on an Energy-Efficient and Energy-Balanced Routing Protocol for Wireless Sensor Networks
## 7 Conclusions  
Energy consumption is a major optimization problem relating to WSN applications in general. Since SNs are mainly battery powered devices, the rate of energy consumption of these sensor devices must be properly managed in order to protract the network lifetime and functionality for a reasonable period. Managing the energy consumed by these SNs can be quite a challenging problem because of the network structure and formulations. However, researchers and practitioners have proposed various methods of managing the energy consumption in WSNs, during either data transmission or by the SN hardware devices. Concentrating on the energy consumed during data transmission, this survey paper reviewed different energy-efficient and energy-balanced routing protocols that attempt to extend the network lifetime and functionality by minimising the energy consumption in the network. Firstly, in Section 2, we summarised different survey papers on routing protocols for WSNs and explained the difference of these existing survey papers from our survey work. In subsequent sections, we emphasised the importance of an energy-efficient and energy-balanced routing protocol, and introduced a taxonomy to classify the studied routing protocols based on their proposed mode of communication towards the BS. In explaining the proposed taxonomy, we grouped the studied routing protocols based on the design decision variables used in selecting the path(s) to BS. We established the strengths and weaknesses of the choice of the decision variables used in the design of these energy-efficient and energy-balanced routing protocols. Additionally, we classified these routing protocols based on the solution types or algorithms used to solve this energy consumption optimization problem. Thus, keeping in view the coverage of this survey paper relating to energy consumption optimization problems, we envisage that this paper will open up possible research directions in developing an energy-efficient and energy-balanced routing protocol.  
This research is funded by the Advanced Sensor Networks SARChI Chair program, co-hosted by University of Pretoria (UP) and Council for Scientific and Industrial Research (CSIR), through the National Research Foundation (NRF) of South Africa.  
The authors declare no conflict of interest.
# Simulation of Rock Failure Cone Development Using a Modified Load-Transferring Anchor Design
## 4 Summary and Conclusions  
The numerical modeling approach based on the use of a linear connector element should be regarded as a milestone in advancing the understanding of rock failure mechanisms under the action of a detachment (fracturing) anchor. Previous treatments of this subject, largely derived from studies on installation anchors embedded in materials such as concrete, tended to oversimplify the problem and did not allow for a comprehensive description of the processes governing the initiation and propagation of failure zones in rock masses.  
The widespread conceptualization of the failure region as a so-called breakout cone represents a significant simplification. While this model is undoubtedly useful in the design and standardization of anchorage systems in concrete structures, it does not permit the optimization of the rock detachment process, particularly in configurations involving interaction between adjacent failure zones [46].  
The conducted numerical analyses confirmed the feasibility of the proposed detachment technology, its effectiveness, and its potential applicability in selected areas of engineering practice. Moreover, the numerical findings are consistent with the results obtained from experimental tests carried out using a prototype detachment anchor, especially in terms of the shape and extent of the resulting fracture zone.  
A comprehensive parametric analysis is required to systematically compare the results obtained for various material and geometric configurations. Previous studies focused primarily on validating the fracture model for a selected rock type and a specific anchor design. However, preliminary investigations [51; 56] have examined the influence of parameters such as tensile strength (\(f_{\text{t}}\)), Young's modulus (\(E\)), fracture energy (\(G_{\text{f}}\)), and anchor head angle (\(\beta\)) on failure propagation and breakout geometry. In subsequent phases, a structured comparative analysis will be conducted, encompassing different loading scenarios and geotechnical conditions, with the use of standardized indicators and visualization techniques. These analyses will be extended to include a broader range of lithological conditions and modified anchor geometries, enabling the assessment of model sensitivity and applicability under real-world conditions.
# Modeling and Multi-Objective Optimization Design of High-Speed on/off Valve System
## 4 Utilizing AFSA for Multi-Objective Optimization Design Based on HSV
### Optimization Results  
Optimization design was conducted employing a multi-objective optimization algorithm. The individual characteristics were determined based on the structural parameters of the driving solenoid coil, and the population was initialized accordingly. Fifty randomly distributed individuals were generated within the predefined range as outlined in Table 1. The objective functions selected for this study were the opening and closing times, wherein the output flow was assessed using the established BPNN model. The opening and closing times were determined based on the valve's output flow, with full valve opening considered achieved when the flow reached within 5% of its maximum value. The ultimate goal of the optimization process was to minimize both objective functions to the greatest extent feasible. Following the process depicted in Figure 12, iterative calculations were performed with a maximum of 200 iterations. Subsequently, all non-dominated solutions generated throughout the iterations were preserved, while duplicate non-dominated solutions were eliminated. The resulting Pareto front was visualized as a two-dimensional scatter plot. Figure 12 displays the final Pareto optimal set, consisting of 30 solutions, showcasing the opening and closing times for HSV. Each data point represents a distinct configuration of electromagnetic solenoid coil parameters, and no particular parameter configuration demonstrates superiority over others in terms of HSV's dynamic performance. Hence, these solutions are deemed optimal.  
As shown in Figure 13, the non-dominated solutions obtained through the AFSA algorithm are uniformly distributed along the Pareto front. The required opening and closing times of the ball valve are inversely proportional. This is because the proposed structure of the HSV in this study utilizes hydraulic pressure generated by the valve port differential to reset the valve core, rather than a traditional spring structure. Therefore, it is not possible to adjust the reset force by modifying the spring stiffness or precompression. Additionally, the model developed in this study considers the influence of eddy current losses on response time. The reset time of the valve is greatly affected by the inductance of the driving solenoid coil. While increasing the driving force of the electromagnet, the inductance and eddy current losses also increase, resulting in an inability to simultaneously decrease the opening and closing times of the electromagnet.  
Figure 13: Distribution of multi-objective optimization solution set.
## 1 Introduction
On the basis of previous algorithms, this paper analyzed the structure of the CKF [17] and proposes a strong tracking CKF algorithm with multiple fading factors to improve the filtering accuracy. To solve the problem that Cholesky decomposition fails due to the different model dimensions in the CKF algorithm, an improved strong tracking CKF algorithm based on SVD [18] is proposed. The aim of the algorithm is to improve the standard Markov matrix to improve the adaptability of the algorithm. The simulation results show that, compared with the single-model algorithm and the standard IMM algorithm, the proposed algorithm can significantly improve the tracking accuracy and convergence speed of the hypersonic target.
###### Abstract  
In this study, we analysed remote sensing data collected during the Beirut port explosion on 4 August 2020 at 15.08 UT. For this purpose, we selected three Low-Earth-Orbit (LEO) satellite missions that passed near the Beirut port explosion site immediately after the event. The satellites involved were Swarm-B, the Defence Meteorological Satellite Program (DMSP-F17), and the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-2). This study focused on identifying the possible ionospheric signatures of explosion in both hemispheres. The conjugate hemispheric points were traced using the International Geomagnetic Reference Field (IGRF) model. We found that the satellite data revealed disturbances not only over the explosion site in the Northern Hemisphere, but also in its corresponding conjugate region in the Southern Hemisphere. Ionospheric electron density disturbances were observed poleward in the conjugate hemispheres along the paths of the Swarm and DMSP satellites, whereas the magnetic field data from Swarm-B showed both equatorward and poleward disturbances. Additionally, the ionospheric disturbances detected by Swarm-B (18:52 UT) and DMSP-F17 (16:30 UT) at the same location suggested travelling ionospheric disturbance (TID) oscillations with identical spatial patterns for both satellites, whereas the disturbances observed by COSMIC-2 south of the explosion site (10\({}^{\circ}\)N) indicated the radial propagation of TIDs. COSMIC-2 not only recorded equatorward topside (>550 km) ionospheric electron density disturbances, but also in the conjugate hemispheres, which aligns with the time frame reported in previous studies. These ionospheric features observed by multiple LEO satellites indicate that the detected signatures originated from the event, highlighting the importance of integrating space missions for monitoring and gaining deeper insight into space hazards. The absence of equatorward ionospheric disturbances at the altitudes of DMSP-F17 and Swarm-B warrant further investigation.
## 3 Results
### Carbon Nanotube-Based Sensors
#### 3.1.2 Sensing Mechanisms and Applications  
Carbon nanotube-based sensors rely on different sensing mechanisms to detect and measure strain, deformation, and other parameters relevant to bridge maintenance. One commonly utilized mechanism is the piezoresistive effect, where the electrical resistance of carbon nanotubes changes with strain [38; 39]. As the nanotubes experience mechanical deformation, the alteration in their structure affects the flow of electrons, resulting in a measurable change in resistance. This change in resistance can be correlated with the applied strain, providing valuable information on the structural behavior of the bridge.  
Carbon nanotube-based sensors are also sensitive to other physical parameters, such as temperature and humidity [40; 41]. Variations in temperature cause changes in the electrical conductivity of carbon nanotubes, enabling their use as temperature sensors. Additionally, carbon nanotubes' high surface-to-volume ratio makes them susceptible to adsorption and desorption of moisture molecules, allowing them to function as humidity sensors.  
The applications of carbon nanotube-based sensors in bridge maintenance are diverse. These sensors can be integrated into bridge components, such as concrete or composites, to monitor strain, deformation, and structural integrity. They can also be attached to critical sections of the bridge to detect and track the development of cracks or other forms of damage. Furthermore, carbon nanotube-based sensors can provide real-time data on temperature and humidity changes, aiding in the identification of potential corrosion or moisture-related issues.
# Hybrid Precoding Design for Subarray-Structure-Enabled mmWave URLLC System
## 3 Problem Solution
### Digital and Analog Precoder Design [21]
#### 3.2.1 Analog Precoder Design  
Assuming that \(P_{t}=\|\mathbf{w}^{\mathrm{opt}}\|^{2}\), \(\mathbf{Af}\) can be treated as every nonzero element in \(\mathbf{A}\) multiplied by the corresponding row in \(\mathbf{f}\), and there exists  
\[\|\mathbf{Af}\|^{2}=\frac{N_{\mathrm{t}}}{N_{\mathrm{RF}}}\|\mathbf{f}\|^{2}=P_ {t}. \tag{21}\]  
Moreover, the optimization goal of problem (19) can be rewritten as  
\[\min_{(\theta_{i})_{i=1}^{N_{\mathrm{t}}}}\|(\mathbf{w}^{\mathrm{opt}})_{i,i} -e^{j\theta_{i}}(\mathbf{f})_{i,i}^{H}\|^{2}, \tag{22}\]  
where \(l=\left[i\frac{N_{\mathrm{RF}}}{N_{\mathrm{t}}}\right]\), which means the minimum integer that is no less than \(\left(i\frac{N_{\mathrm{RF}}}{N_{\mathrm{t}}}\right)\) is assigned to \(l\). \(\mathbf{A}\) is set up as a matrix approximation problem for the phase rotation as  
\[\arg\left(\mathbf{A}\right)_{i,l}=\arg\left(\mathbf{w}^{\mathrm{opt}}\right)_{ i,i}(\mathbf{f})_{i,\cdot}^{H},\ i\leq N_{\mathrm{t}},l=\left\lceil i\frac{N_{\mathrm{ RF}}}{N_{\mathrm{t}}}\right\rceil. \tag{23}\]  
From Equation (23), we note that the special characteristic of \(\mathbf{A}\) simplifies the analog precoder design; thus, the constant mode constraint can be easily solved according to the above transformation.
# An Efficient Probe-Based Routing for Content-Centric Networking
## 3 Design and Description of Our Proposal
### Routing Complexity  
For network overhead, it includes bandwidth overhead and time overhead.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 3 Methodology  
The proposed SAR target incremental recognition method based on hybrid loss function and class-bias correction is shown in Figure 2. Firstly, a small number of old target class samples are preserved and input into the model along with the new target class samples. Secondly, the model is trained using knowledge distillation loss, cross-entropy loss, and class separation loss, where the class separation loss is to reduce the confusion between target classes. Thirdly, a bias correction layer is introduced to correct the bias in the fully connected layer since the imbalance between the number of old and new samples will lead to bias in the fully connected layer of the model.  
For the convenience of presenting the proposed method, some notations of incremental learning are defined in the following. Incremental learning assumes that one or a batch of new class samples arrives at a time, the model learns multiple tasks in turn, and each task contains several new classes. It is assumed that there are totally \(N+1\) phases of incremental learning (1 initial phase and \(N\) incremental phases). In the initial phase, cross  
Figure 2: Illustration of one incremental phase of the proposed SAR target incremental recognition method based on hybrid loss function and class-bias correction.  
entropy classification loss is used to learn model \(\mathbf{M}_{0}\) by the old classes of data \(\mathbf{D}_{0}\), and then preserve model \(M_{0}\) to memory. Due to the memory limitation, the whole \(\mathbf{D}_{0}\) is difficult to be preserved, so a small number of samples \(\mathbf{d}_{0}\) is preserved instead of \(\mathbf{D}_{0}\). \(\mathbf{d}_{0}\) is a tiny subset of the old classes data \(\mathbf{D}_{0}\) and \(\mathbf{d}_{0}\subset\mathbf{D}_{0}\) and \(|\mathbf{d}_{0}|\ll|\mathbf{D}_{0}|\). In \(i\)-th incremental phase having \(n\) old classes and \(m\) new classes, a new model \(M_{i}\) is trained to classify the \(n+m\) classes. The previous \(\mathbf{d}_{0}\sim\mathbf{d}_{i-1}\) is abbreviated as \(\mathbf{d}_{0:i-1}\). \(M_{i-1}\) and \(\mathbf{d}_{0:i-1}\) are loaded from memory and \(\mathbf{d}_{0:i-1}\) is combined with the new classes data \(\mathbf{D}_{i}\) to train \(M_{i}\) initialized by \(M_{i-1}\). In the incremental phase, the model \(M_{i}\) is trained using the hybrid loss functions and bias correction method. The goal is to train a model that performs well on all currently observed classes without catastrophic forgetting.
# Measurement and Analysis of RSS Using Bluetooth Mesh Network for Localization Applications
## 6 Summary and Conclusions  
In this paper, we presented a systematic study of indoor multipath radio channel characteristics using a RSS measurement system designed with BLE devices and Bluetooth mesh networking technology. The main advantage of the measurement system is that it can be conveniently configured to support simultaneous measurements with multiple transmitter and receiver devices in flexible topologies. We have also designed a communication protocol to associate channel ID information to advertising messages, which is necessary to enable channel-aware multi-device RSS measurements given the fact that standard BLE devices do not report which advertising channel is used for publishing a specific message. Based on channel measurement and analysis, we demonstrated that small-scale fading effects introduce large random variations in RSS measurements, making RSS unreliable for localization purposes. Our results have also confirmed that three advertising channels undergo different fading effects, conforming to different path loss models. Thus, mixing RSS measurements from three advertising channels results in larger linear regression residual fitting errors. However, we showed that with a new six-receiver configuration, it is possible to reduce the residual fitting errors in path loss models through a space-time-frequency diversity combining scheme, which leads to more accurately correlating RSS measurements to distance and thus to effectively improving the performance of the RSS-based localization techniques. With the proposed method, three advertising channels are randomized and there is no need to parse channel information from received packets. Such a finding is especially valuable because it indicates that it is possible to implement the proposed multi-receiver configuration and the diversity combining scheme for RSS-based localization applications using commercial off-the-shelf standard BLE devices.  
Conceptualization, Y.C. and X.L.; methodology, Y.C. and X.L.; software, Y.C. and X.L.; validation, Y.C., H.K. and X.L.; formal analysis, Y.C., H.K. and X.L.; resources, X.L.; writing--original draft preparation, Y.C. and X.L.; writing--review and editing, Y.C., H.K. and X.L.; visualization, Y.C. and X.L.; supervision, X.L.; project administration, X.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Center for Integrated Intelligent Mobility Systems (CIIMS) at University of North Texas (UNT), Denton, TX, USA.  
The authors declare no conflict of interest.
# Assessing the Cloud-RAN in the Linux Kernel: Sharing Computing and Network Resources
## 4 Assessing the vBBU Performance in the Linux Kernel
### Results and Discussion
#### 4.2.1 Resource Sharing Impact
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Thread**} & \multicolumn{2}{c}{**0-1 (microseconds)**} & \multicolumn{3}{c}{**2-3 (microseconds)**} & \multicolumn{3}{c}{**4-7 (microseconds)**} & \multicolumn{3}{c}{**8-15 (microseconds)**} \\ \cline{2-11}  & Idle & User & Kernel & Idle & User & Kernel & Idle & User & Kernel & Idle & User & Kernel \\ \hline ru-thread & 93.8 & 53.1 & 90.9 & 0.4 & 41.9 & 4.8 & 0.1 & 1 & 0.3 & 5.7 & 4 & 3.9 \\ \hline Ite-softmodem & 99.4 & 49.5 & 91.6 & 0.3 & 49.1 & 7.8 & 0.01 & 0.7 & 0.2 & 0.3 & 0.6 & 0.4 \\ \hline fepp\_processing & 99.3 & 63 & 89.7 & 0.4 & 35 & 9.3 & 0.05 & 1.1 & 0.5 & 0.3 & 0.9 & 0.4 \\ \hline feptx\_thread & 99.4 & 51.4 & 76.1 & 0.5 & 44.8 & 23 & 0.03 & 3.5 & 0.7 & 0.03 & 0.1 & 0.1 \\ \hline TASK\_GTP & 98.6 & 58.6 & 84.8 & 0.8 & 38.1 & 10.7 & 0.4 & 2 & 3 & 0.2 & 1.1 & 1.2 \\ \hline UDP\_TASK & 95.2 & 52 & 77.9 & 1 & 41.4 & 12.9 & 2.8 & 4.4 & 3.7 & 0.8 & 2 & 1.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Scheduling latency of vBBU’s threads (percentage). Evaluated scenarios: (i) Idle (no resource sharing); (ii) User (sharing resources with collocated user-space workloads); (iii) Kernel (sharing resources with collocated Kernel threads). Figure 5: vBBU procedures: number of DL NACKs computed over a set of six experiments (total observation time-span is 60 min). Evaluated scenarios: (i) Idle (no resource sharing); (ii) User (sharing resources with collocated user-space workloads); (iii) Kernel (sharing resources with collocated Kernel threads). for NACKs, where the vBBU benefits from the efficient processing of high packet rates using NAPI compared to the Idle scenario, Kernel thread processing introduces a noticeable increase in the scheduling latency of the vBBU. This occurs because Hard-IRQ processing of incoming packets via Kernel threads cannot be preempted by the vBBU, causing prolonged wait times for it to access CPU-time. Certainly, non-RT threads benefit from having more available CPUs in both idle and stressed systems. Since non-RT threads can be preempted by RT threads in the RT-Kernel, having more CPUs increases the opportunities for a non-RT thread to execute.
## 4 Results
### Training and Evaluation of U-Net Model for Aircraft Semantic Segmentation  
To train the proposed model, the Adam optimization algorithm was selected as a method for efficient gradient-based stochastic optimization. The aim of the training process is to reduce the loss, which involves learning all parameters associated with the model. This optimization process is applied over the entire 256 \(\times\) 256 pixel input image, representing the input to the U-Net architecture. The Dice coefficient serves as the loss function in this context, helping to quantify the error between the predicted results and the provided ground truth mask. The weights for the model are initialized randomly, and the ReLU activation function is applied to the hidden layers' neurons. During the training phase of this study, considering the limited memory capacity of the GPU, a batch size of eight is employed. A set of image patches is fed into the network during each iteration to facilitate the backpropagation process. Additionally, the learning rate is set to 0.00001 to regulate the update step size during optimization.  
The training of the model is conducted over 20 epochs, and during this training process, a dropout rate of 0.5 is applied. After the completion of these 20 epochs, the mean IoU was calculated, yielding a value of 95.08%. The output of the neural network is a 256 \(\times\) 256 mask, representing the semantic segmentation of the input image. To ensure that the pixel values in this mask fall within the range of [0, 1], the sigmoid activation function is utilized. The pixel accuracy and mean IoU scores are employed as performance metrics to assess the model's effectiveness. Training is stopped when the performance score for the validation dataset no longer exhibits improvements, indicating that the model has reached a stable and optimal state.