esa-sceva/satcom-chunk-collection huggingface dataset:
# SATFuzz: A Stateful Network Protocol Fuzzing Framework from a Novel Perspective
## 4 Experimental Analysis
### Test Sequence Selection  
In Section 3.3, defines the optimal test sequence as including the minimum pre-lead sequence and the fittest post-end sequence, which is determined for a certain state to be tested by the test sequence selector. To make the best use of the post-end sequence in state-space exploration, we increased the proportion of request messages that can trigger high-priority states under the premise of a certain length.  
To verify the effectiveness of this method, we conducted comparative experiments in terms of three test sequence selection criteria. To reduce the impact of randomness, we ran 10 isolated instances of each group for each subject. We set the test time to 12 h, and the experimental results of the three groups are shown in Figure 5. The common point regarding the test sequence selection criteria of the three groups is that they all include the minimum pre-lead sequence. When selecting the post-end sequence, group one (G1 for short) prefers the sequence with the highest proportion of request messages that can trigger low-priority states within a certain length.  
Group two (G2 for short) chooses randomness, which is the same choice as that of AFLnet, and group three (G3 for short) realizes our method. It can be seen from Figure 5a that, for LightFTP, in terms of the vulnerability discovery efficiency, G1 decreased by 72.15% on average compared with G2, while G3 increased by 20.11% on average compared with G2. Figure 5b shows that, for Live555, in terms of the vulnerability discovery efficiency, G1, G2, and G3 are almost the same.  
By analyzing the experimental results, we found that it was easier to request messages that can trigger high-priority states in the post-end sequence to be received and processed normally by the protocol entity, which remains normal after processing the test case, indicating its stronger ability to explore the state space deeply. In addition, our method had no advantage on Live555 because, when the SFSM was applied to Live555, the status code set only contained high-priority states, a condition for which the test sequence selection criteria of the three groups are exactly the same.  
Figure 5: Comparative experimental results obtained with different test sequence selection criteria. (a) LightFTP. (b) Live555.
## 2 Theoretical Background
### Sound Absorption  
The absorbing performance of a given material depends on the angle of incidence of the sound wave, on the frequency, on the material properties and thickness, and on the surface finishing. The absorption coefficient is usually measured in single reverberation rooms, that allows an evaluation of the absorption properties in diffuse field, or by two or four microphones impedance tubes that evaluate only the properties for a sound wave impinging normally on the sample surface. In spite of this, the impedance tube requires small samples and, for this reason, it is particularly suitable during the research and development phase. To easily compare the properties of different materials, the weighted Noise Reduction Coefficient (\(NRC\)) is one of the most used indicators [19].  
\[NRC=\frac{\alpha_{125}+\alpha_{250}+\alpha_{500}+\alpha_{1000}+\alpha_{2000}}{ 5} \tag{6}\]  
The \(NRC\) summarizes the absorption characteristic of a material through a single value ranging between 0 (perfectly reflective material) and 1 (perfectly absorbent material).
## 4 Apron U-Shaped Area Traffic Operation Model  
### Notation  
For different apron U-shaped area operation procedures, this section establishes a U-shaped area traffic operation model to enable the simulation of the apron U-shaped  
Figure 6: Global-Shared Operation Procedure. (**a**) shows a scenario that arrival aircraft B follows departure aircraft A into the U-shaped area; (**b**) shows a scenario that departure aircraft C follows departure aircraft D leaves the U-shaped area. area operation process under different operation rules. To facilitate the description, the parameters and variables shown in Table 1 are defined. Network topology is a widely used method for airport surface operation modeling ([PERSON], et al., 2020; [PERSON], et al., 2019)[34; 35]. As shown in Figure 7, the airport's runway-taxiway-apron system is abstracted as a set of directed graphs G = (V, E), where V is the set of points and E is the set of directed edges. The nodes in the surface network graph are divided into four parts: runway nodes, taxiway nodes, U-shaped area entrance/exit nodes, and parking stand nodes. The nodes are connected to each other by straight or curved segments. It is worth noting that the model can be simplified by dividing the aircraft operation in the U-shaped area into two phases. For \(\forall f\in\mathcal{F}_{A}\) (\(\forall f\in\mathcal{F}_{D}\)), the aircraft operation can be divided into two phases: taxiing and stopping into the parking stand (pushing out of the parking stand). \(V_{f}\) is defined as the speed of the taxiing phase, which can be considered to be related to the aircraft type. \(\varpi_{A}^{f}(\varpi_{D}^{f})\) is the time required to park into a parking stand (push out of a parking stand), which can be considered to be related to the type of aircraft. Based on the operational characteristics of arrival and departure aircraft in the U-shaped area, the operational time of an aircraft in the U-shaped area consists of two parts: taxiing time and waiting time. The time of the two phases of the operation of aircraft \(f\) in the U-shaped area can be expressed as the difference between the nodal times, as shown below:  
\[t_{p}(f)-t_{entry}(f)=\frac{L_{p}^{f}}{V_{f}}+w_{f}^{p}+\varpi_{A}^{f}\quad f \in\mathcal{F}_{A} \tag{1}\]
# A Survey of Internet of Things (IoT) Authentication Schemes +
## 5 Analysis of IoT Authentication Schemes
### Smart Grids
Due to the limitation of one-time signature with respect to the size and the storage of the signature, the authors of [80] proposed a one-time signature-based multicast authentication in smart grid. Their scheme deployed a new one-time signature based on a nonlinear integer programming that reduces the computation cost.
## 4 Optimization Design of Acceleration Control Plan
### Sensor Fault Simulation  
Use the variation rule of low spool speed and nozzle throat area optimized by target shooting method in 4.2 as control instructions for the acceleration process, and the acceleration control scheme of low spool speed closed-loop and nozzle throat area open-loop is adopted. The Kalman filter bank is designed by selecting sensors of low spool speed, high spool speed, high-pressure compressor outlet total pressure and total temperature, which constitute the sensor fault diagnosis module. Next, the sensor fault simulation in the acceleration process is carried out. The result is shown in Figures 19-22.  
Figure 17: Changing curve of CDFS surge margin and turbine front temperature during acceleration. (**a**) CDFS surge margin. (**b**) Turbine inlet temperature.  
Figure 18: Changing curve of control quantity during acceleration. (**a**) Fuel flow. (**b**) Nozzle area.  
Figure 21: Changing curve of CDFS surge margin and turbine front temperature in acceleration FTC process. (**a**) CDFS surge margin. (**b**) Turbine inlet temperature.  
Figure 19: Sensor fault diagnosis results in acceleration FTC process.  
Figure 20: Changing curve of speed in acceleration FTC process. (**a**) Low spool speed. (**b**) High spool speed.  
At 3 s, the acceleration control begins. At about 3.8 s, 4% bias fault occurres in the low spool speed sensor, resulting in a large difference from the command speed. Therefore, the controller rapidly increases the amount of fuel to reduce the error between the two, as shown in Figure 22a. It can be seen from the changes of high spool speed and turbine front temperature that this adjustment leads to great changes in the actual engine state. From Figure 19, at 3.8 s the \(WSSR\) value of \(n_{h}\), \(P_{3}\), \(T_{5}\) filters change greatly, and only the \(n_{l}\) filter keeps a low value, so it is quickly determined that the low spool speed has been faulty. It is worth noting that the \(WSSR\) values of each filter also changes after 3 s, which is mainly caused by the difference between the on-board model and the real engine in the acceleration process. Compared with the change caused by sensor faults, the value is much smaller, so it does not affect the diagnosis. At about 4 s, the controller uses the analytical redundancy of low spool speed provided by the on-board model, and it can be seen that the engine states are restored. At about 5.4 s, the low spool speed output by the on-board model successfully keeps up with the control command, and the engine completes acceleration. In general, the scheme successfully achieves acceleration control under sensor faults. However, the sudden change of the control quantity caused by the sensor failure leads to the great change of the engine state, which is mainly because the actuation characteristics and small closed-loop control characteristics of the actuator are not considered in the digital simulation. Therefore, the hardware-in-the-loop test is used to further verify the results.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 1 Introduction
There is a need for a comprehensive article on multicarrier modulation in UWAC to provide the literature for the researchers on UWA multicarrier modulation technologies, such as spatial modulation technologies (SMTs), orthogonal frequency division multiplexing index modulation (OFDM-IM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM) and other similar modulation techniques. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Abbreviations** & **Full Forms** \\ \hline UUV & Unmanned Underwater Vehicles \\ UWAC & Underwater Acoustic Communication \\ UWC & Underwater Communication \\ UWOC & Underwater Optical Communication \\ UW-OFDM & Unique Word OFDM \\ UWSN & Underwater Sensor Network \\ UWWSN & Underwater Wireless Sensor Network \\ UWWC & Underwater Wireless Communication \\ ZP-OFDM & Zero Padding Orthogonal Frequency Division Multiplexing \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author(s)** & **Technique** & **Strength** & **Weakness** \\ \hline [PERSON] et al. [21] & UWSN & \
\begin{tabular}{c} Reviewed UWSN applications \\ and classification \\ \end{tabular} &
\begin{tabular}{c} All papers provide either a \\ survey or comprehensive \\ report on underwater sensor \\ networks, routing protocols, \\ and underwater optical \\ communication. There is a \\ huge gap to provide a \\ detailed survey on \\ multicarrier modulation \\ technologies, channel \\ problems, and current and \\ future trends for underwater \\ acoustic communication. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Strengths and weaknesses of previous works. The layout of the paper is as follows. Section 2 contains UWAC physical layer techniques including OFDM, FBMC, MIMO, SMTs, and OFDM-IM. The modeling of UWA channel problems and challenges has been explained in Section 3. The UWA modern technologies have been presented in Section 4. Research trends and their importance have been discussed in Section 5. Finally, the conclusion of the paper is in Section 6.
## 2 Methodology
### Measurement of Vibration around Concrete Pavement Joints  
Distributed optical vibration sensors (DOVS) can measure vibration around joints distributedly. DOVS relies on the technology of phase optical time-domain reflection (\(\varphi\)-OTDR), which makes a single fiber optic cable become a multipoint sensor for measurement and localization. Figure 3a illustrates the schematic principle of \(\varphi\)-OTDR. A measurement device continuously emits pulse light at one end of the cable when measuring. The light waves reflect the signal to the device along the cable. When a vibration event disturbs the cable, the cable's mechanical properties will influence the scattered amplitude of the reflected light. The relative amplitude of the reflected light is analyzed for vibration information, while the position information is analyzed according to the time difference of each reflected light. Therefore, DOVS can measure vibrations along the fiber optic cable continuously and distributedly.  
Figure 2: Impulse loads were applied on two concrete pavement slabs (plan view).  
The sampling frequency of a commercial DOVS measurement device can reach 2500 Hz but the spatial resolution is usually more than 2 m. It is not precise enough to locate vibrations in concrete pavement. [PERSON] et al. [31] proposed a design of vibration monitoring units based on looped fiber optic cables, as shown in Figure 3b. The length of the cable is 4 m in each monitoring unit (looped four times with a 0.3 m diameter). The designed monitoring unit has more than one measurement point to improve the accuracy of measurement. Also, the measurement can mostly represent the vibration information located on the thirty-centimeter-diameter monitoring unit, which improves the localization precision compared with a four-meter section of a linear cable.  
Therefore, as shown in Figure 3c several monitoring units can be arranged on both sides of a joint to measure the vibration around the joint at the same time.
# A Query Understanding Framework for Earth Data Discovery
## 4 Methodology
### Query Expansion  
The query expansion module rewrites the original user query by adding synonyms and acronyms of phrases existing in a knowledge base learned from user query logs and metadata (Figure 6). The knowledge base stores the semantic similarity between domain vocabularies that are computed by a similarity calculator ingesting metadata and Web usage logs. Queries in historical usages logs and terminologies in metadata attributes are the two sources for vocabularies in the knowledge base. For queries, the hypothesis of measuring their similarity is if two queries are similar: (1) they would co-occur in distinct users' search history more frequently and (2) the overlap between datasets clicked after searching the two queries would be larger in the context of large-scale clickstream data. The assumption of measuring terminologies similarity is that two vocabularies have a higher probability of appearing in the same metadata if they are similar to each other. Accordingly, query-user, query-data, and terminology-data co-occurrence matrices are constructed from user logs and metadata for similarity calculation. The Latent Semantic Analysis (LSA) [41] is applied to these co-occurrence matrixes to discover hidden semantic relations among vocabularies that consist of queries and terminologies. The independent vocabulary similarity scores derived from the three matrixes validate each other and become more credible if integrated while using borda voting to a final score ranging from 0 (i.e., no relation) to 1 (i.e., identical). These scores are stored in the knowledge base and periodically updated to learn new knowledge introduced by new metadata or interaction data [30; 42]. The user input query is expanded by adding the highly related terms to extracted phrases with similarity scores greater than a threshold (e.g., 0.95) (Figure 6). With the extended query the search engine retrieve datasets relevant to the query, but do not contain phrases in the original query. For example, given a query \"sea level pressure\", the search engine cannot retrieve documents, including the acronym \"SLP\". If the  
Figure 5: Workflow of named entity recognition (NER) train data preparation, train, and prediction.  
relation between the two terms is uncovered from metadata and user, the query is rewritten to \"sea level pressure\" or \"SLP\" to improve the recall of the search engine.
# Investigation of Titanium Alloy Cutting Dynamics in Thin-Layer Machining
## 5 Analysis of Stability with the 0-1 Test
Analyzing the plots presented in Figure 19 for Ti Grade 2, which include cutting force signals and corresponding phase plots, reveals a complex and variable dynamic behavior across the entire range of tested feed rates. Depending on the specific sample and cutting conditions, the system exhibited chaotic, periodic, or stochastic characteristics. This variability is particularly evident in Figure 19d, which displays the test output parameter \(K\). At the lowest feed rate of 3 \(\upmu\)m/rev, a representative sample demonstrates chaotic motion, with the \(K\) value approaching 1, signaling highly irregular and unpredictable cutting dynamics. This suggests that at such low feed rates, below the minimum undeformed chip thickness, the process is dominated by unstable deformation mechanisms rather than stable chip formation. Conversely, at the much higher feed rate of 100 \(\upmu\)m/rev, the cutting process exhibits more periodic behavior. Here, the \(K\) value remains significantly above zero but well below one, indicating that the cutting forces follow a more regular and repeatable pattern. Additionally, the minimum values of the asymptotic growth rate \(k\) in this process do not fall below 0.4, reinforcing the presence of persistent periodicity with some level of disturbance or noise. These observations highlight the sensitivity of the Ti Grade 2 cutting process to feed rate variations, with transitions between chaotic and periodic states reflecting changes in the underlying material deformation and chip formation mechanisms. Figure 19: Results of the 0–1 test for \(T\cdot dt=6\) s and variable feed for Ti Grade 2: (**a**) cutting force \(F\), (**b**) phase plot of the cutting force, (**c**) asymptotic growth rate \(k\), (**d**) output parameter \(K\) of 0–1 test.
## 3 Proposed MFFCNN-Based Fault-Diagnosis Method
### Feature Fusion  
Multi-scale feature learning realizes simple concatenations of different scale features. However, it cannot represent the difference in the importance of features. Therefore, an effective feature fusion mechanism is needed.  
In this paper, an attention mechanism module is used after the feature fusion layer to distribute the weight of multi-scale feature channels. The network can selectively strengthen the useful features for fault identification and suppress invalid or even wrong information. The structure of the efficient channel attention module is shown in Figure 9.  
Assume that the input feature graph of the attention module is \(\mathbf{Y}=[\mathbf{y}_{1},\mathbf{y}_{2},\cdots,\mathbf{y}_{c}]\) (\(\mathbf{y}_{i}\in\mathbf{R}^{W\times 1}\)), where \(W\) and \(C\) are size and channel dimension of the feature graph. By using global average pooling \(F_{Avg}\) to compress information of the feature graph \(\mathbf{Y}\), the channel statistical vector \(z\) is obtained:  
\[z_{i}=F_{Avg}(\mathbf{y}_{i})=\frac{1}{1{\times}W}\sum_{j=1}^{W}\mathbf{y}_{i}( j). \tag{9}\]  
After that, two fast one-dimensional convolutions are used to self-adaptively encode the channel correlation. The importance of different channels is quantified by activating function \(\sigma\), thus generating the weight vector \(\mathbf{z}^{\prime}\) of channels. The mathematical description of this process is shown as follows:  
\[\mathbf{z}^{\prime}=\sigma\big{(}F^{\prime}_{conv}(\delta(F_{conv}(\mathbf{z} )))\big{)}, \tag{10}\]  
where \(F_{conv}\) is the convolution operation using the convolution kernel of \(1\times\) k and channel vector \(z\); \(F^{\prime}_{conv}\) is the convolution operation using the convolution kernel of \(1\times\) k and vector after \(F_{conv}\); \(\delta\) and \(\sigma\) are the ReLU function and Sigmoid function, respectively.  
By multiplying the input feature graph \(\mathbf{Y}\) and the weight vector \(\mathbf{z}^{\prime}\) of the channel, the calibrated feature graph \(\mathbf{Y}^{\prime}\) of the channel can be obtained:  
\[\mathbf{Y}^{\prime}=\mathbf{Y}\cdot\mathbf{z}^{\prime}=[\mathbf{y}_{1}\mathbf{ z}^{\prime}_{1},\mathbf{y}_{2}\mathbf{z}^{\prime}_{2},\cdots\mathbf{y}_{C} \mathbf{z}^{\prime}_{C}], \tag{11}\]  
where \(\mathbf{z}^{\prime}_{i}\) represents the importance of the corresponding channel.  
In order to prevent network degradation and improve its generalization performance, a residual connection is added after channel calibration; thus, the output of the attention mechanism module is \(\mathbf{Y}^{\prime\prime}=\mathbf{Y}+\mathbf{Y}^{\prime}\).
## 1 Introduction
Their work provide interesting understanding on the angle of attack on a rotating wind turbine blade. However, very limited research literature are available for the measurement and analysis of angle of attack by experimental investigation in the field. In this work, an aerodynamic measurement platform in the field was developed based on a 100  kW horizontal axis wind turbine from Institute of Engineering Thermophysics (IET). Seven-hole probes were developed and installed at the leading edge to measure inflow direction, static and total pressure at the near field. Pressure scanners and tubes were installed in the chamber of blade to measure pressure distributions. Two data reducing processes, sideslip angle correction and induced velocity correction, were proposed to determine the effective angle of attack. Finally, the aerodynamic measurement was validated by the comparison with wind tunnel results and some particular aerodynamic phenomenon in the field were discussed. \begin{table}
\begin{tabular}{r c c c} \hline
**Test Projects or Research Teams** & **Turbines** & **Project Period** & **Determination Method of Angle of Attack** \\ \hline Combined Experiment Phase I and II by NREL & 3-bladed, 10 m diameter & 1987–1993 & Wind tunnel method \\ Unsteady Aerodynamics Experiment Phases II-IV by NREL & 3-bladed, 10 m diameter & 1993–1998 & Wind tunnel method \\ Delft University of Technology (DUT) & 2-bladed, 10 m diameter & – & Inverse BEM method and Vortex wake method \\ RIBS National Laboratory in Denmark & 3-bladed, 19 m diameter & 1987–1993 & Power method \\ Netherlands Energy Research Foundation (PCN) & 2-bladed, 27 m diameter & 1993–1997 & Stagnathon method \\ Imperial College (IC) and Rutherford Appleton Laboratory (RAL) & 3-bladed, 16 m diameter & – & Probe method \\ Mie University in Japan & 3-bladed, 10 m diameter & since 1997 & Wind tunnel method \\ DAN-AERO MW by RISQ & 3-bladed, 80 m diameter & 2007–2010 & Wind tunnel method \\ Lanzhou University of Technology in China & 2-bladed, 14.8 m diameter & since 2007 & – \\ \hline \end{tabular}
\end{table} 
Table 1: The existing work on aerodynamic measurement in the field.