esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Satellite IP Route Lookup Using Cuckoo Bloom Hybrid Filter
### CBHF Algorithms  
As described in Section 2.1, a property of the Bloom filter is that it does not support the deletion of elements stored in the filter. Since online routing update occurs frequently in networks, and to provide the consistency of allowing deleting in the CBHF, we adopt the counting Bloom filter proposed in ref. [32]. The basic idea of the counting Bloom filter is to associate a counter with each bit of the standard Bloom filter; whenever an element is inserted into or removed from the filter, the counters corresponding to the bit positions are incremented or decremented accordingly. We will detail this further in Section 5, and here we only focus on the CBHF algorithms.  
In the following, we denote the Bloom filter as \"BF\", and the cuckoo filter as \"CF\", and all the algorithms are detailed in Appendix A. Algorithm A1 shows the pseudocode of the inserting procedure in the CBHF. For an element \(x\), it will be inserted into the specific  
Figure 4: IP route lookup using a CBHF.  
filter according to the level ascription. If the element \(x\) belongs to an unpopular level, it will be inserted into a Bloom filter. Otherwise, \(x\) will be inserted into a cuckoo filter.  
Similarly, Algorithms A2 and A3 describe the pseudocode of the querying and deleting process, respectively. Some content about the Bloom filter and cuckoo filter algorithms can be referred to in Section 2.
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 5 QAOA Mappings: Orderings and Schedules
### Example: Traveling Salesperson Problem (TSP)
#### 5.1.2 Compilation  
**Encoding orderings.**  We encoded orderings in two stages: First into strings, and then into bits making use of the encodings from Section 4. Here, we focus on a \"direct encoding\" as opposed to the \"absolute encoding\" that is introduced in Section 5.3. Other encodings of orderings are possible, such as the Lehmer code and inversion tables. In direct encoding, an ordering \(\iota=(\iota_{1},\ldots,\iota_{n})\) is encoded directly as a string \([n]^{n}\) of integers. Once in the form of strings, any of the string encodings introduced in Section 4 can be applied. We applied the one-hot encoding with \(n^{2}\) binary variables; the binary variable \(x_{j,u}\) indicates whether or not \(\iota_{j}=u\) in the ordering, in other words, whether city \(u\) is visited at the \(j\)-th stop of the tour.  
**Phase separator.**  We used the phase function \(g(\iota)=4f(\iota)-(n-2)\sum_{u=1}^{n}\sum_{v=1}^{n}d(u,v)\), which translates to a phase separator encoded as:  
\[H_{\text{P}}^{\text{(enc)}}=\sum_{i=1}^{n}\sum_{u=1}^{n}\sum_{v=1}^{n}d(u,v)Z_ {u,j}Z_{v,i+1}. \tag{53}\]  
The phase separating unitary corresponding to Equation (53) imparts a phase determined by the sum of the distances between successive cities to a state corresponding to a tour. This unitary can be implemented using \(n^{2}(n-1)\) two-qubit gates, which mutually commute. Using the same color-parity partition of the terms as for the color-parity ordering swap mixer, this can be done in depth \(2\kappa\leq 2n\).  
**Mixer.**  The individual value-selective ordering swap partial mixer, which swaps cities \(u\), \(v\) between tour positions \(i\) and \(j\), is expressed in the one-hot encoding as:  
\[U_{\text{PS},\{i,j\},\{u,v\}}^{\text{(enc)}}(\beta) =e^{-i\beta H_{\text{PS},\{i,j\},\{u,v\}}}\;, \tag{54}\] \[H_{\text{PS},\{i,j\},\{u,v\}}^{\text{(enc)}}(\text{enc)} \tag{55}\]  
where:  
\[S^{+} =X+iY=\ket{1}\bra{0}, \tag{56}\] \[S^{-} =X-iY=\ket{0}\bra{1}. \tag{57}\]  
The \(i\)th adjacent value-selective swap partial mixer (Equation (47)) is the special case:  
\[H_{\text{PS},i,\{u,v\}}^{\text{(enc)}}=S_{u,i}^{+}S_{v,i+1}^{+}S_{u,i+1}^{-}S_ {v,i}^{-}+S_{u,i}^{-}S_{v,i+1}^{-}S_{u,i+1}^{+}S_{v,i}^{+}\;. \tag{58}\]  
Each of the two terms of the form \(S^{+}S^{+}S^{-}S^{-}\) in Equation (58), can be written as a sum of eight terms, each a product of 4 Pauli operators (e.g., \(XXYY\)). The color-parity partitioned ordering swap mixer of Equation (52) can be implemented using \((n-1)\binom{n}{2}\) of these 4-qubit gates, implementable in depth \(2\kappa\leq 2n\) in these gates. The two-qubit gate circuit depth is at most \(2\kappa\) times the depth of a compilation for such 4-qubit gates.  
**Initial state.**  The initial state, an arbitrary ordering, can be prepared from the zero state \(\ket{00\ldots 0}\) using at most \(n\) single-qubit \(X\) gates.
# Real-Time Geophysical Applications with Android GNSS Raw Measurements
## 2 Materials and Methods
### Vadase  
The first algorithm used to analyze GNSS measurements in this study is the variometric approach developed by the Geodesy and Geomatics Division of Sapienza University of Rome within the VADASE algorithm. This approach, starting from the single difference time variation of carrier-phase measurements acquired on a certain frequency, allows estimation of the 3D velocity and displacements of a GNSS receiver. The model is fully described in [9].  
VADASE was developed to be used in GNSS seismology where it soon became widespread due to the differences with respect to the traditional algorithms used to study velocity and co-seismic displacements, such as real-time kinematic (RTK) and Precise Point Positioning (PPP) [13]. In fact, using these approaches, displacements with centimeter accuracy can be obtained only with an external infrastructure able to provide differential correction from a base station or precise products--orbits, clock, and earth orientation parameter (EOP)--in real-time. Contrariwise, VADASE needs only broadcast parameters and, not requiring any ambiguity resolution algorithm, is able to estimate real-time velocity and displacements with accuracy comparable to traditional techniques on geodetic class receivers without the need for convergence time [14; 15].  
The availability of raw GNSS measurements from Android smartphones opens the way to new applications of this approach in mass-market GNSS receivers. The results obtained in the reconstruction of kinematic track and gesture [16] encouraged the analysis in the field of high-frequency movements.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
## 3 The Compression Framework
### Reduction of the Primary Data
#### 3.1.4 Residual Post-Processing  
Error guarantees are often required in scientific data compression and this is a significant departure from image and video compression. Here, we require that the error (\(\ell_{\infty}\) or an \(\ell_{2}\) measure) does not exceed a prespecified bound. For standalone images or tensors, a straightforward approach that guarantees this is using truncated basis function representations based on MGARD [10; 11; 12]: here, the input domain is recursively subdivided with a fixed basis function used within each subdomain to represent the data. If the fixed basis is unable to meet the error requirement, it is abandoned and the subdomain further divided. In this way, error guarantees are met and a subdivision procedure hammered out per image. Depending on the methods of dividing grids, MGARD has two versions. One is MGARD uniform, which only focuses on guaranteeing PD error bounds and achieving the best possible compression without considering QoI preservation. It divides grids and assigns quantization bins uniformly. The other is MGARD non-uniform, which considers both guaranteed PD error bounds and the preservation of linear QoI. It uses an additional \(s\) parameter that assigns different weights and quantization bins depending on the QoI. Hence, it divides grids non-uniformly. MGARD non-uniform achieves a lower compression amount compared to MGARD uniform with the same error bound, since it uses precise quantization bins for some coefficients due to QoI preservation. In our approach, we apply MGARD uniform on residuals--the difference between the output of an AE and the PD--instead of applying MGARD on the PD. Since we have an additional step for QoI preservation, we choose MGARD uniform for the residual post-processor. Furthermore, we use the reconstructed residuals selectively based on an error level of each instance. We need to store not only the compressed data from an AE, but the compressed residuals as well, which affects the overall compression ratio. Therefore, we set error thresholds to reduce the number of residuals being used. If an error of an instance is larger than the threshold, we process the residual of the instance with MGARD so that the error does not exceed the threshold.
## 3 Experimental Setup  
The human studies protocol used to collect the data for this study was approved by the Institutional Review of the Mass General Brigham (formerly, Partners Healthcare System) at the Massachusetts General Hospital. Study participants were 50 pairs of adult females (total of 100 subjects) with each pair comprised of one patient with PVH (diagnosed with vocal fold nodules) and one normal control subject matched to the patient by age and occupation. Due to the higher incidence of female patients with PVH than male in the overall population [52; 53] and potential sex-specific effects (e.g., due to differences in fundamental frequency), only females were selected for this study. The patient matching was done to normalize for general vocal behavior differences. Clinical diagnoses were based on a complete team evaluation by laryngologists and speech-language pathologists at the Massachusetts General Hospital Voice Center that included (a) a complete case history, (b) endoscopic imaging of the larynx, (c) aerodynamic and acoustic assessment of vocal function [54], (d) a patient-reported Voice-Related Quality of Life questionnaire [55], and (e) a clinician-administered Consensus Auditory-Perceptual Evaluation of Voice assessment [56]. All patients were enrolled prior to the administration of any voice treatment. Written informed consent was obtained from all subjects. The average (standard deviation) age of all subjects was 25.0 (10.5) years old.  
Each subject was recorded reading a phonetically balanced text (Rainbow Passage, [57]), at a comfortable loudness level, using a Voice Health Monitor system that consists of an accelerometer attached to the front of the neck below the larynx and connected to an smartphone application [14]. Also, synchronized recordings of oral airflow volume velocity provided a reference signal from which glottal airflow could be extracted using standard inverse filtering [32]. The sampling frequency for each signal is 20,000 Hz with an average of 30 s per passage. A typical set-up of the accelerometer attached to the neck surface is shown in Figure 6.
## 3 Proposed Algorithm  
In this section, we propose a low-complexity channel estimation algorithm, the SBL-GAMP algorithm. First, the SBL framework is used to accomplish sparse recovery of the basis coefficients of the created channel model in Equation (23). Then, in order to reduce the computational complexity, GAMP is introduced into the expectation maximization algorithm, and a low-complexity SBL framework based on GAMP is established without degrading the performance. The GAMP framework may successfully tackle the Gaussian message propagation problem in the SBL algorithm due to the Gaussian assumption in the SBL algorithm. Meanwhile, the sum-product method is used to realize belief propagation and track Gaussian messages in the factor graph. In addition, GAMP utilizes a series of scalar operations on the input and output nodes to achieve the approximation of the a posteriori estimates of the joint vectors in the SBL E-step, which greatly reduces the computational effort.
# Priority of Emergency Vehicle Dynamic Right-Of-Way Control Method in Networked Environment
## 3 Experimental Verification  
### Experiment Overview  
To evaluate the performance of the proposed control model in the actual traffic environment, this paper selected Qinghua Road in Wuhan City, Hubei Province, as an actual case for discussion and research. Qinghua Road starts from Qingwang Road in the west and ends at Huashan Avenue in the east, with a total length of about 12 km. It is an essential urban road between the East Third Ring Road and the East Fourth Ring Road in Wuhan. As demonstrated in Figure 7, Qinghua Road is a two-way four-lane road. It is a major traffic  
Figure 6: Flowchart of the traversal search algorithm.  
The speed limit of Qing Hua Road is 60 km/h, and the road capacity is 1200 pcu/h per lane. In order to analyze the operation effect of different CV flows on EVs and CVs and obtain different CV flows, the adopted flow considered the high road saturation state according to the actual traffic capacity of the road, which can better verify our method's effectiveness. To verify the influence of the length of the buffer space in the front of EVs on the running time, we set the length of different buffer spaces under a certain flow rate based on the actual conditions of the road and vehicle operation.
## 2 Numerical Analysis Model
### Vehicle-Bridge Coupled Model
#### 2.2.3 Vehicle-Bridge Coupling Vibration Equation  
In analyzing the vitality equation of the bridge system, the train and the bridge are considered a whole system. The total potential energy of the train-bridge system at t seconds is equal to the sum of the potential energy of the train, girder and piers [19].  
\[\Pi=\sum_{i=1}^{n}\Pi_{pi}+\Pi_{bi}+\Pi_{pi} \tag{6}\]  
where \(\Pi_{pi}\),\(\Pi_{bi}\) and \(\Pi_{pi}\) denote, respectively, the potential energy of \(i\) train, \(i\) bridge, \(i\) piers. The dynamic matrix of the prestressed concrete continuous girder bridge-train system at time \(t\) can be obtained [19; 20].  
\[[M]\{\ddot{\delta}\}+[C]\{\dot{\delta}\}+[K]\{\delta\}=\{P\} \tag{7}\]  
where \(\{\ddot{\delta}\}\), \(\{\dot{\delta}\}\), \(\{\delta\}\) express, separately, the train-bridge array of acceleration, velocity and displacement.  
Figure 2: Space vibration of cross section of box girder: (**a**) transverse displacement \(U\); (**b**) vertical displacement \(V\); (**c**) torsion angle \(|\varphi_{i}\); (**d**) distortion angle \(\gamma\); (**e**) computation module.  
The train-bridge displacement array \(\{\delta\}\) can be divided into \(k\) known parameters and \(n\) unknown parameters, i.e., Equation (7) can be represented by Equation (8).  
\[\begin{bmatrix}M_{kk}&M_{kn}\\ M_{nk}&M_{nn}\end{bmatrix}\begin{pmatrix}\ddot{\delta}_{k}\\ \ddot{\delta}_{n}\end{bmatrix}+\begin{bmatrix}C_{kk}&C_{kn}\\ C_{nk}&C_{nn}\end{bmatrix}\begin{pmatrix}\dot{\delta}_{k}\\ \dot{\delta}_{n}\end{bmatrix}+\begin{bmatrix}K_{kk}&K_{kn}\\ K_{nk}&K_{nn}\end{bmatrix}\begin{pmatrix}\delta_{k}\\ \delta_{n}\end{bmatrix}=\begin{array}{c}P_{k}\\ P_{n}\end{array} \tag{8}\]  
The function of track irregularity and creep irregularity can be input in Equation (8) to calculate the system spatial vibration response. Equation (8) can be written as:  
\[[M_{nn}]\{\ddot{\delta}_{n}\}+[C_{nn}]\{\dot{\delta}_{n}\}+[K_{m}]\{\delta_{n} \}=\{P_{n}\}-[M_{nk}]\{\ddot{\delta}_{k}\}-[C_{nk}]\{\dot{\delta}_{k}\}-[K_{ nk}]\{\delta_{k}\} \tag{9}\]  
\[[M_{kk}]\{\ddot{\delta}_{k}\}+[C_{kk}]\{\dot{\delta}_{k}\}+[K_{kk}]\{\delta_{k }\}+[M_{kn}]\{\ddot{\delta}_{n}\}+[C_{kn}]\{\dot{\delta}_{n}\}+[K_{kn}]\{\delta _{n}\}=\{P_{k}\} \tag{10}\]  
where \(\{\ddot{\delta}_{k}\}\), \(\{\dot{\delta}_{k}\}\), \(\{\dot{\delta}_{k}\}\) is the excitation for transverse vibrations in the train-bridge system.  
Typically, the irregularity of the track is entered into the coupled vibration equations as a function of power spectral density [25]. In this paper, the creep amplitude of the main girder is considered as track irregularity and is introduced into the car-bridge system for dynamic calculations. The complex nonlinear dynamical equations above are generally solved by a time domain stepwise integration method [26].
# TSKT-ORAM: A Two-Server \(k\)-ary Tree Oblivious RAM without Homomorphic Encryption +
## 7 Comparisons
### Practical Comparisons
#### 7.2.3 Access Delay Comparison  
The access delay for both TSKT-ORAM and CNE-ORAM is mainly contributed by the communication and computation delays.  
Suppose the network bandwidth between the client and each of the servers is 10 MB/s and the data block size \(B=1\) MB. For each query, CNE-ORAM needs at least four seconds to transfer the data blocks needed, while TSKT-ORAM needs about 2.2-4.6 s.  
To estimate the delay caused by computation, the authors of CNE-ORAM [30] measure the delay of an XOR operation to be about one millisecond using a 2012 MacBook Pro with a 2.4-GHz Intel i7 processor. For fairness, we adopt the same hardware to compare the computation delays of both schemes. The delay for TSKT-ORAM is about 4.5-9 s, while the delay for CNE-ORAM ranges from 11.2-23.8 s.  
Therefore, we can see that TSKT-ORAM incurs a delay that is less than half of that caused by CNE-ORAM.  
Note that, for comparison fairness, we assume the above values of network bandwidth and computational power, which may be very different from the practical settings. Particularly, a client with high-level security concerns could use a much better network service to have much higher communication bandwidth with the servers, and the servers typically have much more powerful computational capacity than a MacBook.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **CNE-ORAM** & **TSKT-ORAM** \\ \hline Communication Cost & \(>40\cdot B\) & \(22\cdot B\)-\(46\cdot B\) \\ Computational Cost & \(>10(\lambda-10)\cdot\log N\) & \(18(\lceil\frac{\log N+1}{\log k}\rceil-1)(k-1)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Practical comparisons (\(2^{16}\leq N\leq 2^{34}\), \(B=1\) MB). The communication cost in the table is the client-server communication cost per query. The computational cost is the number of XOR operations needed on the server side.
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
### VANETs Characteristics
#### 1.2.3 Real-Time Limitations  
Regardless of the intrinsic delays between flexible platforms, some VANETs applications want the information to reach in real time. For example in fault discovery and collision prevention systems where the driver has minimum reaction time (several milliseconds) available to decode and react to the received message.
## 1 Introduction
[26] conducted premixed combustion experiments on NH\({}_{3}\)/syngas/air, NH\({}_{3}\)/CO/air, and NH\({}_{3}\)/H\({}_{2}\)/air, and measured their laminar combustion velocities. They found that with the addition of H\({}_{2}\) and CO, the decomposition rate of ammonia was significantly increased. [PERSON] et al. [14] measured the laminar flame velocity of NH\({}_{3}\)/CO at different mixing ratios and excess air coefficients in a heat-flow furnace. It was found that with the increase in NH\({}_{3}\), the laminar flame velocity of the NH\({}_{3}\) and CO mixture first increased and then decreased. When the volume fraction of NH\({}_{3}\) was 85%, the laminar flame velocity of mixture was the highest, and was much higher than that of either pure NH\({}_{3}\) or pure CO. In comparison to combustion efficiency, research on NO formation during co-firing CH\({}_{4}\) and CO with NH\({}_{3}\) is insufficient. [PERSON] et al. [27] and [PERSON] et al. [26] found that the NO\({}_{x}\) emission increased after adding doped combustion gas to ammonia. [PERSON] et al. [28] realized the ideal combustion characteristics of NH\({}_{3}\)/CH\({}_{4}\) in gas turbines by using rich-poor staged combustion, with low NO\({}_{x}\) and CO emissions. [PERSON] et al. [29] experimentally studied the NO emission characteristics of NH\({}_{3}\)/CH\({}_{4}\)/air on premixed cyclone burners. The results showed that the NO\({}_{x}\) emission was the highest when the calorific value ratio of NH\({}_{3}\) in the mixed fuel was 0.5. [PERSON] et al. [30] and [PERSON] [31] paid attention to the influence of free-radical concentration on NO\({}_{x}\) emission. However, the formation and inhibition mechanisms of NO\({}_{x}\) are still unclear. In this paper, the NO generation characteristics of NH\({}_{3}\) mixed with CO or CH\({}_{4}\) was studied. With adopting a high-temperature furnace reaction system, experiments were conducted on the combustion of NH\({}_{3}\)/CH\({}_{4}\)/Ar mixture and NH\({}_{3}\)/CO/Ar mixture under various operating conditions. The effects of ammonia ratio, excess air coefficient (\(\alpha\)), temperature, residence time, and fuel concentration on NO formation were investigated and are discussed in detail. This will help guide the clean and efficient utilization of ammonia, which is a new renewable energy source, and can turn industrial by-products into wealth.
## 5 Discussion
The training and testing processes of CBF-IDS require more computational resources and time, which are not problematic for network environments with substantial computational capabilities. However, in scenarios with limited computational capabilities and a need for rapid response to intrusion threats, prolonged training and testing may have adverse effects. Therefore, the deployment of CBF-IDS in such scenarios needs to be carefully considered. Additionally, resource-constrained network environments pose challenges to the deployment of CBF-IDS, especially in some IoT devices, including small sensor nodes and smart wearable devices. These devices typically have compact sizes and simple structures, making them unsuitable for hosting high-performance computing and storage equipment. Consequently, they have limited computational resources and memory [64]. The complexity of CBF-IDS may exceed the performance limits of these devices, making it difficult to deploy effectively in such environments. Therefore, future work will focus on addressing the challenges posed by the complexity of CBF-IDS to further enhance its efficiency, meeting the requirements of diverse network environments. Feature engineering is considered an effective solution, and recent research has successfully utilized feature engineering to improve the performance and efficiency of IDS. [PERSON] et al. [26] utilized a recursive feature elimination decision tree based on the Pearson correlation method, which can eliminate irrelevant features, improve resource utilization, and reduce the complexity of IDS. [PERSON] et al. [27] used the MGWO to select the optimal feature set, excluding irrelevant and noisy features, thereby enhancing the efficiency and performance of the IDS. In addition, [PERSON] et al. [28] proposed the LGBA-NN to determine feature subsets and hyperparameters, effectively improving the detection performance of botnet attacks. These research findings provide important inspiration for our future work. They clearly demonstrate that feature engineering is an effective method for improving IDS detection efficiency. Therefore, in future work, we will draw inspiration from and enhance these methods to reduce the computational resources and time consumption of CBF-IDS.