esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Bottleneck Analysis Using Symbol-Level Profiling
### Kata Containers  
Figure 9 illustrates the CPU cycles that each symbol occupies for the read and write operations with 4 KB and 1 MB blocks. Note that the y-axis is the normalized CPU cycles (over the total CPU cycles of an operation on the x-axis, such as read with 4 KB). First, _handling EPT_ shows the highest overhead regardless of the block size and the type of operations, and it occupies more than 26.8% in all cases. The profiling results show that _handling EPT_ involves a large number of page table walks [37]. In particular, the overhead of _handling EPT_ increases in the write operation because the I/O requests should be stored in the memory space of the VM and delivered to the host OS.  
Second, the overhead for _mode transition_ increases in the operations with small blocks (i.e., 4 KB) because small blocks have more I/O request rates than large blocks. In addition,  
Figure 8: CPU usage in processing random file I/O operations under r, Kata containers (K), gVisor (G), and Firecracker (F). (**a**) Random read, and (**b**) random write.  
Figure 7: Random file I/O performance of runc, Kata containers (Kata), gVisor, and Firecracker (FC) with different block sizes. (**a**) Random read, and (**b**) random write.  
memory processing_ increases when the block size is small (4 KB). This is because Kata containers require memory mapping for every file I/O request to deliver the requests to the host OS, which increases the amount of memory (un-)mapping and (de-)allocation. In addition, _memory processing_ includes virtiofsd clearing the dirty flag of the memory used. These results show that the architecture of Kata containers incurs additional memory overhead, such as reconstructing EPT entries, frequent memory mapping, and clearing flags. Note that the overhead of _memory processing_ decreases with the 1 MB blocks because the number of memory (un-)mapping operations decreases with large blocks.
# Iterative Parameter Estimation Algorithms for Dual-Frequency Signal Models
## 3 The Newton Iterative Parameter Estimation Algorithm
\right],\] (24) \[h_{11}(\hat{\theta}_{k-1}) = \sum_{i=1}^{L}\cos^{2}(\hat{\omega}_{1,k-1}t_{i}),\] (25) \[h_{12}(\hat{\theta}_{k-1}) = \sum_{i=1}^{L}\cos(\hat{\omega}_{1,k-1}t_{i})\cos(\hat{\omega}_{ 2,k-1}t_{i}),\] (26) \[h_{13}(\hat{\theta}_{k-1}) = \sum_{i=1}^{L}t_{i}\sin(\hat{\omega}_{1,k-1}t_{i})[y(t_{i})-\hat{ b}_{k-1}\cos(\hat{\omega}_{2,k-1}t_{i})]-\hat{a}_{k-1}t_{i}\sin(2\hat{\omega}_{1,k-1}t_{i}),\] (27) \[h_{14}(\hat{\theta}_{k-1}) = -\sum_{i=1}^{L}\hat{b}_{k-1}t_{i}\cos(\hat{\omega}_{1,k-1}t_{i}) \sin(\hat{\omega}_{2,k-1}t_{i}),\] (28) \[h_{22}(\hat{\theta}_{k-1}) = \sum_{i=1}^{L}\cos^{2}(\hat{\omega}_{2,k-1}t_{i}),\] (29) \[h_{23}(\hat{\theta}_{k-1}) = -\sum_{i=1}^{L}\hat{a}_{k-1}t_{i}\cos(\hat{\omega}_{2,k-1}t_{i}) \sin(\hat{\omega}_{1,k-1}t_{i}),\] (30) \[h_{24}(\hat{\theta}_{k-1}) = \sum_{i=1}^{L}t_{i}\sin(\hat{\omega}_{2,k-1}t_{i})[y(t_{i})-\hat{ a}_{k-1}\cos(\hat{\omega}_{1,k-1}t_{i})]-\hat{b}_{k-1}t_{i}\sin(2\hat{\omega}_{2,k-1}t_{ i})],\] (31) \[h_{33}(\hat{\theta}_{k-1}) = \sum_{i=1}^{L}\hat{a}_{k-1}t_{i}^{2}\cos(\hat{\omega}_{1,k-1}t_{i })[y(t)-\hat{b}_{k-1}\cos(\hat{\omega}_{2,k-1}t_{i})]-\hat{a}_{k-1}^{2}t_{i}^{2} \cos(2\hat{\omega}_{1,k-1}t_{i}), \tag{32}\]\[h_{34}(\hat{\mathbf{\theta}}_{k-1}) = \sum_{i=1}^{L}\hat{a}_{k-1}\hat{b}_{k-1}t_{i}^{2}\sin(\hat{\omega}_ {1,k-1}t_{i})\sin(\hat{\omega}_{2,k-1}t_{i}), \tag{33}\] \[h_{44}(\hat{\mathbf{\theta}}_{k-1}) = \sum_{i=1}^{L}\hat{b}_{k-1}t_{i}^{2}\cos(\hat{\omega}_{2,k-1}t_{i })[y(t)-\hat{a}_{k-1}\cos(\hat{\omega}_{1,k-1}t_{i})]-\hat{b}_{k-1}^{2}t_{i}^{2 }\cos(2\hat{\omega}_{2,k-1}t_{i}).\] (34) \[h_{12}(\hat{\theta}_{k-1}) = \sum_{i=1}^{L}\cos(\hat{\omega}_{1,k-1}t_{i})\cos(\hat{\omega}_{ 2,k-1}t_{i}),\]0  
The procedure for computing the parameter estimation vector \(\hat{\mathbf{\theta}}_{k}\) using the NI algorithm in Equations (18)-(35) is listed as follows:  
1. Let \(k=1\), give the parameter estimation accuracy \(\varepsilon\), set the initial value \(\hat{\mathbf{\theta}}_{0}=\mathbf{1}_{n}/p_{0}\). 2. Collect the observed data \(y(t_{i})\), \(i=1,2,\cdots,L\), form \(\mathbf{Y}(L)\) by Equation (19). 3. Form \(\hat{\mathbf{\varphi}}_{k}\) by Equation (22), form \(\hat{\mathbf{\Phi}}_{k}\) by Equation (20). 4. Form \(\hat{f}_{k}\) by Equation (23), form \(\hat{F}_{k}\) by Equation (21). 5. Compute \(h_{mn}(\hat{\mathbf{\theta}}_{k-1})\) by Equations (25)-(34), \(m=1,2,3,4\), \(n=1,2,3,4\), and form \(\mathbf{H}(\hat{\mathbf{\theta}}_{k-1})\) by Equation (24). 6. Update the estimate \(\hat{\mathbf{\theta}}_{k}\) by Equation (35). 7. Compare \(\hat{\mathbf{\theta}}_{k}\) with \(\hat{\mathbf{\theta}}_{k-1}\), if \(\|\hat{\mathbf{\theta}}_{k}-\hat{\mathbf{\theta}}_{k-1}\|\leqslant\varepsilon\), then terminate this procedure and obtain the estimate \(\hat{\mathbf{\theta}}_{k}\); otherwise, increase \(k\) by \(1\) and go to Step 3.
## 4 Charging Method
### Constant Voltage (CV) Charging Method  
The constant voltage (CV) charging method is a straightforward option that involves maintaining a consistent voltage during the battery-charging process. One of the main advantages of employing CV charging over CC charging is its ability to mitigate the detrimental effects of overcharging, which can lead to a reduction in battery lifespan. The provided figure illustrates the gradual decrease in the battery-charging current over time until it reaches a predetermined threshold value, as observed when employing this particular charging method. Figure 13 shows the structure of a typical CV charging method.  
High current rates during early charging are required to keep the terminal voltage under control. However, rapid charging leads to lattice breakdown and heat loss. One of the most challenging aspects of charging a CV is finding the correct voltage to strike a balance between charging speed, electrolyte degradation, and battery capacity. Quick CV charging speeds up the process but reduces battery life. When a battery is low in its state of charge, the charging current can overshoot the safe limit. When a large amount of battery current flows through it, the lattice frame collapses, causing the active material at the poles to break apart [70].  
Figure 12: Constant current charging method for EV battery with battery voltage (green) and battery current (red).  
Figure 13: Constant voltage (CV) charging method for EV battery with battery current (green), battery voltage (red) and reference (black).
# Safety Assessment of Microwave Breast Imaging: Heating Analysis on Digital Breast Phantoms
## 2 Materials and Methods
### Mathematical Modeling
Moreover, it is worth noting that the active time should be distributed within the entire examination, which lasts about 8 min, obtaining a low-duty cycle. However, to obtain conservative results, 15 s of continuous exposure was considered for each of the five positions of the transmitting antenna chosen in this study and for each selected frequency. The electrical properties of the healthy tissues were assigned according to the IT'IS Foundation database [25] and assumed to be invariant with respect to temperature. Values of the specific heat capacity of the different breast tissues, including cancer, were set for every tissue [26]. The specific heat capacity of cancer was assigned to the benign tumor tissue as well. Regarding the mass density, its correlation with the tissue water content was exploited. Precisely, a linear regression between the water content of the skin, breast gland, adipose tissue, and muscle [26] and the mass density of the corresponding tissues [25] was used to obtain the breast cancer mass density given its water content [26]. The same mass density was also assigned to the benign tumor. The values set for all the thermal parameters are shown in Table 1. The EPs of benign and malignant tumors were set, distinguishing between breast benign abnormalities and cancer at the frequency of 2 GHz, 4 GHz, 6 GHz, and 8 GHz, according to the literature [4]. For each tissue, the peak SAR value and its position were extracted from the simulation results. The collected SAR values were used to compute the maximum temperature increase inside each breast tissue. To further investigate the sensitivity of simulation results with respect to the used input parameter values, a 10% variation was taken into account for these values. Precisely, two additional simulations were performed in a later stage at the intermediate frequency of 6 GHz and with the horn antenna in the position leading to the largest temperature increase in model #1. In each additional simulation, the values of the electrical and thermal properties of breast tissues were decreased or increased by 10%. All the properties were increased or decreased together, assuming that the variation in the properties is due to a variation in the tissue water content.
## 5 Discussion  
From the results presented in this paper, showing a median latency of 10-20 ms observed across the tested devices and topologies, simply encapsulating GOOSE packets for transmission over a 5G network may not meet the protocol's stringent substation latency requirements of less than 4 ms. However, the measured latencies are indeed sufficient for most other smart grid protocols, such as the tested Modbus and DNP3 protocols. In  
Figure 12: Protocol latency comparison.  
Figure 13: Packet size comparison.  
our future work focused on 5G networking for smart grid applications, the evaluation of different frequency bands and channel widths will be incorporated as additional avenues of exploration, along with frame optimization, 5G resource scheduling, and overall timing in radio operations, to further reduce the achievable end-to-end latency. There are also promising solutions within recent 3 GPP releases that can be evaluated for GOOSE and other smart grid protocols, namely Sidelink Relaying [31] and Sidelink Enhancement [34]. Rather than requiring the utilization of a gNB, sidelink communication is often used in V2X for smart cities. This technique could be repurposed for use in smart grids, where lower latency communication is desired within the physical substation. The standard TCP over 5G communications outlined in this paper can be used in conjunction with sending traffic to a SCADA system or different substations. We plan to conduct further research into additional mechanisms to reduce the latency impact discussed in this paper by leveraging new and existing 5G mechanisms and parameter optimizations utilized within URLLC while expanding the supported packet length to achieve support for transporting OT protocol payloads.  
We also aim to further expand the scope and capabilities of our testbed, specifically in terms of scale, and embrace a zero-trust architecture. To address the scale of the testbed, incorporating additional base stations and UE would allow for further testing of fairness and latency evaluation within a congested network. This platform would also allow for modifying existing Core Network functions and adding new VNFs, which would be leveraged for realizing a zero-trust architecture [5]. Our research will also focus on identifying and resolving any challenges in applying our findings to real-world implementations of 5G for smart grid OT equipment. For example, we aim to explore the impact of using 5G gateways, rather than directly communicating over 5G for grid communications, and a viable migration path for already deployed smart grid systems toward 5G for an aging grid infrastructure [35; 36].
## 4 Design
### Optimization and Security Analysis in the Consensus Stage  
#### 4.5.1 Consensus Stage Optimization  
In the PBFT algorithm, digital signatures serve the purpose of ensuring message integrity, identity authentication, and resistance against attacks from Byzantine fault nodes. Digital signatures not only provide identity verification but also validate whether messages have been tampered with. In the PBFT algorithm, the RSA algorithm is commonly used for digital signatures. In RSA, nodes use their private keys to encrypt messages and generate digital signatures, while other nodes can use the corresponding public keys to verify the signatures. RSA is known for its high reliability and strong security. However, it has been criticized for its relatively slow encryption/decryption speed and the requirement for larger key lengths. As a result, the use of RSA in the PBFT algorithm can impact the algorithm's operational speed in the system, thereby reducing system throughput and increasing latency, among other adverse effects.  
Refs. [24; 25] provide a comparison between RSA and ECC. Although the ECC ecosystem is relatively less mature compared to RSA and is suitable for relatively smaller environments, in the experiments conducted, the ECC algorithm outperforms RSA in terms of operational efficiency and security. For RSA to achieve this security level, it requires a 2048-bit key. On the other hand, ECC, being more efficient in terms of key size and security, only needs a 224-bit key to achieve the same level of security. Additionally, the ECC algorithm outperforms RSA in terms of total runtime for encryption and decryption, which is beneficial for the current requirements of the PBFT algorithm. The advantages of ECC over RSA in terms of key size and operational efficiency make it a promising choice for cryptographic operations, especially in resource-constrained environments, which could potentially improve the overall performance and efficiency of the PBFT algorithm.
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 4 Experimental Results and Analysis
### Evaluation on the KITTI Dataset  
The KITTI dataset provides real vehicular scenes containing both dynamic and static objects, and is widely used to evaluate the effectiveness of visual SLAM systems in separating moving and stationary objects in dynamic environments. In this experiment, the performance of the SLAM system proposed in this paper was compared and analyzed against ORB-SLAM3 under a monocular setting across eleven image sequences, with a focus on the impact of the dynamic object elimination strategy on the system's localization accuracy and robustness. The performance of different systems in each sequence is summarized in Tables 1 and 2, where bold font indicates the best result for that metric. In Tables 1 and 2, both System(D) and System(D+O) represent the dynamic SLAM systems proposed in this paper; System(D) refers to the system that uses a dynamic mask to directly eliminate the entire dynamic object, while System(D+O) refers to the system that uses a combination of dynamic mask and outlier mask in a dual-mask method to precisely exclude dynamic points. In the absolute trajectory evaluation on the KITTI dataset, System(D+O) is the most accurate in most sequences; in relative pose estimation, System(D) and System(D+O) are similar, with both performing better than the ORB-SLAM3 system. Meanwhile, in the evaluation of ATE, the average standard deviations of ORB-SLAM3, System(D), and System(D+O) are 19.01, 16.90, and 17.64, respectively. Compared to ORB-SLAM3, System(D) achieves a reduction of approximately 11.1%, while System(D+O) achieves a reduction of about 7.2%. In the evaluation of RPE, the average standard deviations are 0.373, 0.282, and 0.325 for ORB-SLAM3, System(D), and System(D+O), respectively, with System(D) and System(D+O) reducing the standard deviation by 24.4% and 12.9%, respectively. These results indicate that System(D), which directly removes dynamic objects using a dynamic mask strategy, achieves the lowest average standard deviations in both ATE and RPE evaluations. This suggests significantly reduced error fluctuations compared to ORB-SLAM3 and verifies the effectiveness of the proposed strategy in suppressing error dispersion and enhancing the stability of the SLAM system, with particularly notable advantages in relative pose estimation. To further investigate the specific impact of potential dynamic objects on SLAM system performance, Table 3 lists the distribution of potential dynamic objects in each sequence along with an analysis of their impact on localization accuracy. For brevity, in this paper we denote System(D) as \"D\", System(D+O) as \"DO\", and ORB-SLAM3 as \"3\". The analysis shows that the distribution of dynamic and static objects significantly affects system performance. In the KITTI dataset sequences, ORB-SLAM3 only outperforms the dynamic SLAM system in sequences with fewer dynamic objects, such as sequences 03 and 06. This suggests that the dynamic SLAM system may erroneously eliminate static objects, resulting in reduced trajectory estimation accuracy.
## 5 Issues Relating to the Fundamental Design Aspects
### Charging Optimization
#### 5.1.3 Issues Pertaining to Energy Efficiency  
UAV energy is mainly dissipated during flight and transmission. Hence, including an energy supplement is a promising idea for achieving a stable and sustainable data collection procedure. As discussed earlier, energy can be obtained from charging stations or by designing energy-efficient schemes to improve or extend UAV service time [92]. This is particularly important in applications involving large-sized update packets as more energy is required to send each data packet from the source to the destination. The uplink transmit power of IoT devices is impacted by the location of hovering spots [1], especially since those locations determine the transmit distance of sensor nodes. Thus, UAVs can be made to hover above nodes [88] (or carefully optimized spots) to collect sensed data (via air-to-ground communication links of low altitude) in order to reduce the sensor nodes' energy consumption and improve the network lifetime.  
One major challenge is maintaining highly reliable and stable communication among sensor nodes due to their onboard energy constraints [92]. As such, UAV-assisted WSN data gathering needs to factor in energy efficiency on the part of the UAV, which traverses the sensor nodes and finally relays the data to the destination, and the sensor nodes that observe the environment. It is interesting to note that the sensor network lifetime highly depends on implementing energy-efficient routing and MAC protocols. A popular technique used in this regard is scheduling sensor nodes to remain in sleep mode when no major activity is performed. Thus, techniques such as wakeup radio can be used in WSN or IoT applications to wake sleeping nodes when it is time to transmit to the UAV to save their energy. Additionally, sensor nodes can form clusters and send sensed data to the cluster heads that upload data to the UAV. Such cluster heads' roles can be rotated and even some nodes with higher initial energy can either be randomly deployed or strategically positioned to function as advanced nodes to reduce the energy burden on the other regular nodes in the network.
## 1 Introduction
### Joint Communication and Sensing  
Integrating communication and radar sensing into a joint communication and sensing (JCAS) system represents an appealing solution. Various terms, including radar communication (RadCom), joint radar and communication (JRC), joint communication and radar (JCR), dual-functional radar communication (DFRC), and integrated sensing and communication (ISAC), have been coined to refer to JCAS. In this context, we adopt JCAS, which involves the collaborative design and utilization of a specific transmitted signal for both communication and sensing purposes. This approach reveals the potential for shared components between communication and sensing systems [1].  
While most receiver elements can be commonly utilized, the baseband signal processing at the receiver typically differs for communication and sensing. JCAS is reported to address two limitations in passive sensing through joint designs. This distinguishes JCAS significantly from passive sensing concepts, aligning more closely with spectrum sharing principles. Despite combining the communication and sensing signals into a single system, they employ separate waveforms, and the resources are allocated distinctly in terms of time, frequency, and code [1].  
Figure 1 shows a basic JCAS scenario. A cellular system is equipped with array antennas, efficient for beamforming but also typically with multi-cell interference. Multiple users use the arrays to communicate for sensing and high-rate low-latency [2].
## 4 Result Analysis
### Experimental Environment and Parameter Settings  
This experiment was implemented using Python 3.11, Torch 2.2.1, and the relevant Python statistical packages. The data were collected from the internal Low Earth Orbit  
Figure 6: Structural diagram of the internal low earth orbit constellation service simulation system.  
constellation business simulation system. Both the compilation and testing of the experiment were completed on a hardware device with an AMD Ryzen 7-5800 CPU (AMD, Santa Clara, CA, USA) and an RTX 3070 GPU (NVIDIA, Santa Clara, CA, USA). In the traffic prediction experiment of this paper, the traffic matrix from Section 4.1 was used for single-step prediction, with 80% of the data as the training set and 20% as the test set. The learning rate of the model was determined through preliminary experiments. A learning rate that is too large may lead to unstable training, while one that is too small may result in slow convergence. After multiple attempts, the learning rate was set to 0.027. The batch size needs to balance computational resources and model convergence. For an RTX 3070, setting the batch size to 16 may cause unstable gradient updates, while setting it to 64 would impose significant memory pressure. Therefore, the batch size was set to 32. The node feature dimension was 1, and the hidden feature dimension was 64. This hidden feature dimension provides sufficient expressive power to capture complex patterns in the traffic data without excessively increasing the number of model parameters. The training cycle was 300 epochs, as the loss function tends to stabilize at this point without overfitting. The loss function used was the mean squared error (MSE), and the Adam optimizer was employed for gradient descent training. The Adam optimizer combines the advantages of momentum and adaptive learning rates, automatically adjusting the learning rate for different parameters, making it suitable for handling non-stationary objective functions.