esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
## 1 Introduction
We therefore introduce asymmetric searchable encryption. Our main contributions are summarized as follows. 1. In this paper, a new multi-user dynamic searchable scheme is proposed on the basis of the predecessors. A new validation Bloom filter structure (ABF) based on the existing Bloom filter is proposed. The new ABF not only includes the original features of the Bloom filter but also adds a counter module, which makes the solution easy to implement in dynamic updates and greatly reduces the error rate. 2. In this paper, a new file set encryption scheme is designed, which uses a lightweight algorithm to reduce the overhead of initialization and update. At the same time, the ABF and state op of encrypted files are used to realize dynamic update of data. 3. The scheme satisfies forward and backward safety. Forward security is satisfied by updating the search token, and backward security is realized by a new file set encryption scheme. Compared with other schemes, the scheme in this paper not only has a great advantage in time cost but also fully considers the problem of historical storage, avoiding multiple different storage states for the same file.
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 2 Materials and Methods
### Speed Analysis and Validation on an IBM Device  
If a research application were to require a fast prediction time, the time to execute _model.predict()_ on each dataset was recorded within Python for each algorithm in order to provide this for a researcher's consideration. A 2-qubit datapoint was obtained from an IBMQ quantum computer by running _qiskit_ibm_runtime_[20]. The quantum computer was _IBMQ_perth_, whose qubit layout is shown in Figure 8. This quantum computer is no longer available for use but the following specifications have been reported for this device in the literature [21; 22] and information about the job is retrievable [23]. It is a 7 superconducting transmon qubit quantum computer with a quantum volume of 32. The basis gates are the CNOT, I, RX, SX, and X gates. The T1 time = 168.85 msec and the T2 time = 132.51 msec. The median CNOT error rate = 8.690 \(\times\) 10\({}^{-3}\) and the median SX error rate = 2.8060 \(\times\) 10\({}^{-4}\). The median readout error = 2.930 \(\times\) 10\({}^{-2}\).  
There was no use of any error mitigation from _qiskit_ibm_runtime_ for these data. The phase and channels were 1/3 and [0.092, 0.58175, 0.181, 0.14525], respectively. This data point used for validation was directly extracted from the IBM Quantum jobs page on the date of the experiment. The best 2-qubit model was then used to predict this datapoint, and the prediction was compared to the actual phase and the traditional method of phase estimation.
## 2 Research Method and Process  
### Research Method and Research Question  
This research applies the existing DSR methodology (DSRM) [18]. The DSRM research process consists of six activities [18] (Figure 1). Initially, a research problem is defined, and the value of a solution is described and motivated. Subsequently, objectives of a solution are specified. Then, an artifact is designed and developed. The artifact can be utilized for demonstrating the solving of the research problem. Evaluation focuses on comparing objectives of a solution to the results obtained with the developed artifact(s) in use. Different evaluation criteria can be used for performing ex-post evaluations [20]. At the end of the evaluation activity, the researcher may iterate back to the design and development activity for improving performance of the solution. Finally, the problem, artifact(s), and results are communicated to the research community (typically in publications). Figure 1 illustrates the DSRM process model and its nominal process sequence and knowledge discovery targets between the phases guiding the research process [18].  
In the following, our research applying the DSRM addresses the following research question:  
* What kind of concept and system architecture enables real-time uninterrupted streaming data transfer from moving vehicles towards a control center?
## 3 Results
### Uncertainty Propagation and Correlation Analysis
#### 3.4.1 One-Dimensional Scalar Analysis Results  
The two quantities of interest in this scalar analysis are velocity and temperature. Figures 9 and 10 show the Empirical Cumulative Distribution Function (ECDF) plots for maximum temperature and average velocity. These plots were generated using the entire data set of the three-dimensional domain. It is clear from Figure 9 that most of the data for the maximum temperature lie below 1000 K. The temperatures of the system are well below the boiling temperature of the salt, thereby not imposing any design safety risks. The respective standard deviation is 10.73 K. Similarly, Figure 10 shows that 99% of the data foraverage velocity lie between 0.4 and 0.5 m/s. These velocities are attained for a natural circulation-driven system that can manage the reactor system during accident scenarios by keeping the salt flowing.  
In the scalar sensitivity correlation analysis study, the SRCCs are computed for the ensemble average of system maximum and system average temperature and velocity. The rank correlations for maximum temperature and average velocity are shown in Figures 11 and 12, respectively. Kinetic parameters do not have much of an impact on  
Figure 10: Empirical Cumulative Distribution Function plot for average velocity.  
Figure 9: Empirical Cumulative Distribution Function plot for maximum temperature.  
the temperature or velocity. Specific heat, thermal expansion, and heat transfer coefficient have the most important correlations at the end of the transient.
## 2 Stochastic Modeling of Roof Snow Load
### Ground Snow Loads
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Climatic Region** & **Altitude \(a\) (m a.s.l.)** & \(COV_{g}\) Range & \(COV_{g}-a\) Dependence \\ \hline  & & & \(COV_{g}=0.2-0.8\) if \\ Alpine region (except for the German part) & 200–2500 & 0.2–1 & \(a\leq 1000\) m \\  & & & \(COV_{g}=0.4-1.0\) if \\  & & & \(a>1000\) m \\ \hline Alpine region & 440–1200 & 0.3–0.7 & not significant \\ (German part) & 440–1200 & 0.3–0.7 & not significant \\ \hline United Kingdom and Eire & 0–450 & 0.6–1.8 & \(COV_{g}=0.6-0.8\) if \(a>200\) m \\ \hline Iberian Peninsula & 0–1700 & 0.5–6 & mostly \(COV_{g}=1.0-2.0\) \\ \hline Mediterranean region & 0–1500 & 0.1–1.5 & mostly \(COV_{g}=0.4-1.1\) \\ \hline Central East & 0–300 & 0.2–1.2 & \(COV_{g}=0.5-1.2\) if \(a<100\) m \\  & & & \(COV_{g}=0.2-0.6\) if \(a\geq 100\) m \\  & & & but mostly \(COV_{g}=0.5-0.6\) \\ \hline Central West & 0–400 & 0.6–1.2 & not significant \\ \hline Greece & 100–1100 & 0.4–1.2 & not significant \\ \hline Norway & 0–700 & 0.2–0.6 & not significant \\ \hline Finland–Sweden & 0–600 & 0.2–0.7 & not significant \\ \hline Iceland & 0–650 & 0.3–1.3 & not significant \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Coefficient of variations of the annual maxima of ground snow load (\(COV_{g}\)) in Europe [15].
## 5 WBAN and the Communications Protocol Stack
### MAC Layer
#### 5.3.2 MAC Layer Adaptation Schemes
\begin{table}
\begin{tabular}{p{142.3 pt} p{284.5 pt}} \hline \multicolumn{1}{c}{**Protocol**} & \multicolumn{1}{c}{**Protocol Summary**} \\ \hline S-MAC & Saves energy by switching between active and sleep states. Maintains a common sleep schedule between neighbor nodes in low duty cycle operations. Duty cycle needs synchronizing to a specific load which can affect performance. \\ \hline T-MAC & Uses adaptive duty cycle to dynamically end active part of the cycle to reduce energy wasted on idle listening. Can suffer from idle listening, reduced throughput, and increased latency. \\ \hline DMAC & Utilizes data gathering trees to solve interruption problem. Adapts duty cycle depending on load. \\ \hline B-MAC & Incorporates preamble sampling to reduce wakeup period. Drawback is that all nodes need to listen to the long preamble. \\ \hline WiseMAC & Uses preamble sampling to mitigate overhearing and reduce power consumption. WiseMAC suffers from a long preamble and has no mechanism to adapt to changing traffic patterns. For the same delay, preamble sampling lowered power consumption by 57% when compared to 802.15.4. \\ \hline X-MAC & Uses reduced length preamble, early node acknowledgement resulting in increased energy savings. Offers flexible adaptation to both bursty and periodic data sources. \\ \hline NCCARQ-WSN & Network coding based. Uses less control packets than traditional cooperative ARQ protocols. Up to 50% more energy efficient without compromising QoS. \\ \hline NC-PAN & Uses a hybrid cooperative network coded ARQ technique. Performance gains of up to 35% compared to B-PAN and C-PAN. \\ \hline \end{tabular}
\end{table} 
Table 2: Low power MAC protocol.
## 1 Introduction
Additionally, as sea ice concentration prediction involves spatiotemporal series prediction, it is crucial to efficiently extract both temporal and spatial information. To address these challenges, we propose a deep recurrent neural network model based on the encoder-decoder architecture, named DED-ConvLSTM. This model learns temporal and spatial features through a cascading and more profound methodological approach. Building upon the ED-ConvLSTM framework, we have adjusted the dilation rates of each ConvLSTM layer to enlarge the receptive field. Additionally, to enhance the spatial learning capability of the DED-ConvLSTM model, we have integrated a Pyramid Convolution (PC) module, enabling multi-scale learning of spatial information. The specific contributions of our work are as follows:  
1. We proposed a PDED-ConvLSTM model based on the ConvLSTM model. The model utilizes a PC module to learn spatial features at multiple scales, and a DED-ConvLSTM module is used to fuse the spatial information learned by the PC module with the spatial and temporal features learned by the ConvLSTM module. The PDED-ConvLSTM model can extract the global contextual spatiotemporal information from the sequence of Arctic sea ice concentrations more efficiently, and thus accurately perform Arctic sea ice concentration predictions. 2. In this study, we realized the prediction of Arctic sea ice concentrations over a long timescale and improved the accuracy of the prediction by fusing the Arctic sea ice concentration and the related influencing factors. 3. We explored the impact of typical oceanographic variables on forecasts of Arctic sea ice concentrations, with positive results for future research on sea ice prediction. The rest of this paper is organized as follows: Section 2 demonstrates the proposed methodology of this study and Section 3 presents the experimental setup. The experimental results are analyzed and discussed in Section 4. Finally, this study is summarized in Section 5.
## 3 Results and Discussions
### 0.34 THz Triplers' Performances  
The tripler's test diagram is shown in Figure 11, in which the frequency synthesizer Agilent E8257D provided frequency reference to the F-band driving source, which mainly featured the \(\times\)9 frequency multiplication and power amplification. A tunable attenuator was inserted between the tripler and the F-band source to easily adjust the wanted input power up to 100 mW. The Erickson PMS waveguide power meter was used to measure the output power around 0.34 THz and calibrate the input power. When tested, the DC bias voltage was tuned to obtain the best performance within a certain frequency range.  
The performance of the tripler with different input power levels is depicted in Figure 12. With known input power and output power, efficiency can be calculated. As can be seen from the measurement results, a peak efficiency of 6.8% and maximum output power of 6.8 mW were obtained at 338 GHz with an input power of 100 mW and a DC bias voltage of \(-\)2.56 V. It was found that \(-\)1.28 V was the optimum bias voltage for every single Schottky die. In addition, the tripler showed greater than 1 mW output power in the range of 324 GHz\(-\)352 GHz when driven by 100 mW. From the perspective of the increasing tendency of output power with increasing input power from 50 mW to 100 mW, there was still room for further improvement in output power. It is indicated that the tripler still did  
Figure 11: Schematic diagram of the 0.34 THz tripler’s output power test setup.  
Figure 10: Tolerance analysis simulation results and comparison with original simulation data of (a) 0.34 THz tripler and (b) 0.34 THz SHM.  
not reach saturation when driven by 100 mW, which is currently already the highest available power, from 108 GHz to 117.33 GHz, in the lab. Moreover, a comparison between simulated and measured performance with 100 mW input power is given in Figure 13. Note that the measured curves had a similar trend to the simulated curves, though the measured conversion efficiency was a little lower. The frequency corresponding to the measured peak efficiency was close to the simulated frequency, implying the effectiveness of the diode design method and accuracy of performance prediction based on simulations in this work. Moreover, the measured results of the three triplers at room temperature are shown in Figure 13b. Note also that the three different triplers had very similar performance in terms of efficiency and bandwidth. This implies that the monolithic integration process proposed in this article is reliable and can ensure consistency between different products.
## 3 Attacks in Blockchain
### Detecting and Mitigating Malicious Blockchain Attacks
#### 3.3.4 Routing Attack  
Detecting routing attacks in blockchain networks is challenging due to the difficulty of distinguishing between benign and malicious network behavior. However, several techniques have been proposed to identify such attacks effectively. Network monitoring is a key technique, allowing the tracking of transaction routing paths and the detection of anomalies in network traffic [88]. For instance, a transaction propagating through an unusual path may indicate a routing attack [89]. Data analysis can uncover patterns and trends in network traffic that suggest malicious activities. For example, a sudden increase in the number of transactions routed through a specific node may signal a routing attack [90; 91]. Reputation systems are also critical, assigning scores to nodes based on their behavior. Nodes with low reputation scores can be flagged as potential sources of routing attacks [92].  
To mitigate routing attacks, several strategies have been proposed. Secure routing protocols, such as BGPsec and RPKI, use digital certificates to authenticate routing information and prevent spoofing attacks [93]. Distributed consensus mechanisms, including Proof of Work (PoW) and Proof of Stake (PoS), increase the difficulty for attackers to control the majority of the network's computational power or stake, thereby preventing routing attacks [94]. Peer-to-peer networking enhances network resilience by allowing nodes to communicate directly, reducing reliance on centralized routing authorities [95]. Furthermore, node reputation systems can identify malicious or compromised nodes, preventing them from propagating transactions and blocks in the network [96]. Nodes with low reputation scores can be excluded or subjected to additional scrutiny, thereby limiting the risk of routing attacks.
## 2 Routing Protocols for UAV Communication
### Ad Hoc On-Demand Distance Vector(AODV)  
The AODV routing protocol is an improved version of the DSR. AODV is a routing protocol that is commonly used in mobile or wireless ad hoc networks (MANETs or WANETs) [29]. An ad hoc network is a temporary distributed network comprising two or more nodes that allow packets to be sent without the need for traditional infrastructure such as routers and access points. A peer-to-peer (P2P) network is used by the nodes in the ad hoc network. In the AODV routing protocol, when a node needs to communicate, it sends a routing request to discover the communication link. Nodes find routes using defined messages by AODV, which are route requests (RREQ), route replies (RREP), and route errors (RERR). The originating node sends RREQ messages to all of its neighbors, while nodes with a route to the destination node send RREP messages back to the originating node. If the originating node's neighbor node lacks a route to a destination, it broadcasts RREQ signals while maintaining a reverse route to the originating node. Furthermore, when a node loses communication with its next-hop node source, it sends RERR messages to all nodes that received RREP messages. A route maintenance phase has been initiated to address link failure difficulties. However, due to the dynamic nature of the UANET system, network congestion is a concern with AODV.
## 3 Results and Discussion  
In an actual experiment, a commercial camera (Logitech's webcam) was used as a receiver and the resolution of a received image is 640 \(\times\) 480 (VGA resolution). We used the Open CV library to implement our proposed algorithm. Table 1 shows the parameters of a transmitter used in the simulation. The size of the LEA is 4 \(\times\) 4 and the number of symbols (or constellation points) is four. We transmit a total of 16,000 symbols to ensure the reliability of the received performance result and compute the SER. We used the CIE1931 color-space-based constellation shown in Figure 4.  
In our simulation, we assumed that the LEA on the display device moved horizontally with a constant velocity and that color distortion did not occur during transmission. We designedly added  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Color space & CIE1931 \\ RGB model & CIE RGB \\ Reference white & E \\ LED array size & 4 \(\times\) 4 (16) \\ Number of constellation points & 4 \\ Intensity (7 value) & 0.165 \\ Total number of symbols transmitted & 16,000 \\ R: (0.0735, 0.265) \\ Three positions of RGB LEDs in the CIE1931 space & G: (0.274, 0.717) \\  & B: (0.167, 0.009) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 9: Perspective projection to correct the distorted array.  
only a Gaussian error value to the points of the corners. Therefore, we consider only errors that occur in the process of projection as a target region for the symbol decision. Figure 10 shows the tracking results for a horizontally moving LEA. The centroid of the LEA was tracked over 100 frames to verify the performance of the Kalman filter. In the figure, \"GT\" indicates the ground truth, which represents the true location of the moving LEA. From Figure 10a, it can be seen that the LEA is moving from left to right. From Figure 10b, it also can be seen that the LEA moves horizontally, because the vertical movement is very small. The results show that the position of the moving LEA can be tracked better when the Kalman filter is applied.  
Figure 11: Perspective projected results for the detected LEA. (**a**) Distorted received image; (**b**) Perspective projected results.  
Figure 10: Tracking results for a horizontally moving LEA. (**a**) \(x\) plot vs. frame number; (**b**) \(y\) plot vs. frame number  
Figure 11 shows the correction results for the detected LEA after perspective projection. In the figure, the small yellow box inside the LED represents the area used for the symbol (or color) decision. Although LEA detection and correction were successfully performed, the symbol (or color) decision might be difficult because of image distortion, which causes the yellow box to be placed outside the LED region. However, if perspective projection is performed, the decision area is determined correctly and the SER performance can be improved.  
Figure 12 presents the improvement in SER performance when we use a Kalman filter with perspective correction.
# Comparison of Hysteresis Based PWM Schemes \(\Delta\Sigma\)-PWM and Direct Torque Control
## 4 Simulation Results
### Comparison of Voltage Space Vector Trajectory  
The voltage space vector trajectories for both PWM schemes are compared in Figure 9. Since \(\Delta\Sigma\)-PWM as well as DTC utilize PWM signals to construct the voltage space vector, filters have to be applied to measure the actual voltage value. Consequently, the trajectories in Figure 9 are related to several switching states.  
For \(\Delta\Sigma\)-PWM, the magnitude of the voltage space vector moves within a range of \(0.983\cdot u_{0}\) and \(1.013\cdot u_{0}\). In contrast, the magnitude of the voltage space vector trajectory ranges from \(0.969\cdot u_{0}\) to \(1.014\cdot u_{0}\) at DTC.  
The rotation of the DTC sector boundaries can be explained by the fact that the stator flux is obtained from stator voltage by integration. Consequently, they coincide with angle boundaries of \(\Delta\Sigma\)-PWM.  
Figure 8(a) presents the trajectory of the voltage space vector from \(\Delta\Sigma\)-PWM. The reference space vector is synthesized by space vector modulation. Each cycle consists of voltage vectors from at least two different groups (see (16)). The trajectory moves clockwise as well as counterclockwise. This behavior can be explained by the impact of the switching states on the actual voltage space vector. If, for example, a zero vector is applied the trajectory moves towards the origin. This behavior results from a continuous reduction  
Figure 8: Phase current spectrum at nominal operating point.  
Figure 9: Trajectory of voltage space vector.  
of the magnitude of the voltage space vector, taking the first-order lag characteristic of a PMSM into account. In contrast, if the control applies an active voltage vector, the trajectory moves towards this voltage space vector. Whereas the movement of the trajectory against the direction of rotation is visible in Figure (b)b, it cannot be observed in Figure (a)a. This behavior is based on the differences in the switching frequency. As it will be seen later, the average switching frequency is lower with DTC, than with \(\Delta\Sigma\)-PWM. For this reason, the voltage space vector is held longer in the first case. Because the applied filter has the same group delay counterclockwise movements of DTC trajectory can be seen.  
Further explanations refer to the second sector. In Figure (b)b, the control uses exclusively the two active voltage vectors (010) and (110). If the switching state (110) is set, the trajectory moves in the direction of rotation. (010) causes a motion against the direction of rotation. The speed refers to the distance between the applied \(d\) voltage vector and the actual position of the voltage space vector. The closer this distance is, the slower is the speed of the voltage space vector trajectory.
## 3 Materials and Methods
### Datasets and Evaluation Metrics
#### 3.5.3 Multiple Object Tracking Accuracy (MOTA)  
The MOTA [61] interprets alignment and structural errors in frames due to misses, tracking, false positives, etc., including overall object detection. The field of computer vision uses this performance metric for multiobject tracking and is useful for intuitively measuring the tracker's evaluation by keeping the paths/trajectories. It also estimates the object's location by making the precision independent [61]. The relationship for MOTA is given by:  
\[MOTA=1-\frac{(FN+FP+IDSw)}{GT} \tag{5}\]  
The bases of error are combined in this relation as \(GT\) (number of ground truths), _FN_ (false negatives), _FP_ (false positives), and _IDSw_ (Number of Identity Switches).