esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Attack Scenario Analysis
#### 2.6.1 Flooding Attack Scenario Analysis  
Flooding is a type of denial-of-service (DoS) attack that consists of two frames and injects them into the CAN bus. [45; 46; 47]. It is performed repeatedly in short cycles, delaying the transmission of messages via the CAN bus and consuming resources of the target ECU, thereby interfering with the service. Flooding mechanism is shown in Figure 3.  
The flooding attack mechanism is executed at 0.005-s intervals, generates payloads consisting of 7 bytes and 8 bytes of data, respectively, modifies the message ID to '0 x 05040601', and injects it into the CAN bus [45]. Therefore, the Motor ECU processes a payload consisting of 8 Bytes, and there may be a delay in processing the Main ECU's message.  
The flooding attack process was analyzed over time, as shown in Table 4. It occurred between 50 s and 90 s, but the operation was normal. However, due to the flooding attack, the motor stopped from the 90 s to 120 s and 130 s to 160 s ranges.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Timestamp** **(s)** & 10/20 & 30/40/50 & 60/70/80/90 & 100 & 110/120 & 130 & 140/150/160 & 170/180 \\ \hline
**status** & Booting & Take Off & Hovering & Motors Stop & Motors Stop & Hovering & Motors Stop & Landing \\ \hline
**Label** & Normal & Normal & Flooding & Normal & Flooding & Flooding & Normal & Flooding & Normal \\ \hline \hline \end{tabular}
\end{table}
Table 4: Timeline of flooding attack scenario (types 01 and 02).  
Figure 3: Flowchart of Flooding Attack.  
The changes occurring on the CAN bus due to flooding attacks are presented in Table 5.  
The Main and Motor ECUs exchange messages on the CAN bus, and at this time, the attacker injects messages into the CAN bus. Therefore, the Motor ECU does not receive messages from the Main ECU, and processing delays occur, causing the UAV's motor to stop operating. Table 6 provides details on the composition of flooding payloads.  
There are two types of payloads injected by the attacker, with a data length of 8 bytes, and the Transfer ID is stored in the ninth data byte. The Transfer ID is an integer value used by the message and destination node to distinguish it from the other messages, and the payload adds one to the Transfer ID value of the previously transmitted message. Currently, the Transfer ID of the first flooding payload starts with 80.  
The main features of the flooding payload are as follows: First, the data length is seven or eight. Second, the ninth data byte storing the Transfer ID increases by one. Third, the sixth and seventh data bytes are always 00.
# Prediction of the Absorption Characteristics of Non-Uniform Acoustic Absorbers with Grazing Flow
###### Abstract  
In this paper, planar and the cylindrical broadband non-uniform acoustic absorbers were constructed, both of which use broadband absorption units (BAUs) as their building blocks. The impedance boundary Navier-Stokes equation (IBNSE) method was developed to predict the absorption characteristics of the lined duct with non-uniform acoustic absorbers, in which each small piece of perforated plate is acoustically equivalent to a semi-empirical impedance model through the boundary condition. A total of four semi-empirical impedance models were compared under different control parameters. The full Navier-Stokes equation (FNSE) method was used to verify the accuracy of these impedance models. It was found that the IBNSE method with the Goodrich model had the highest prediction accuracy. Finally, the planar and the cylindrical non-uniform acoustic absorbers were constructed through spatial extensions of the BAU. The transmission losses and the absorption coefficients of the rectangular duct-planar acoustic absorber (RDPAA) and annular duct-cylindrical acoustic absorber (ADCAA) systems under grazing flow were predicted, respectively. The results demonstrated that the broadband absorption of the designed non-uniform acoustic absorbers was achieved. The developed IBNSE method with Goodrich model was accurate and computationally efficient, and can be used to predict the absorption characteristics of an acoustically treated duct in the presence of grazing flow.  
broadband absorption unit; planar and cylindrical non-uniform acoustic absorber; semi-empirical impedance model; impedance boundary Navier-Stokes equations method; grazing flow
## 2 Related Work  
### Application of Self-Attention Mechanism in Semantic Segmentation  
ViT represents the application of Transformer as an encoder in semantic segmentation tasks. Studies [32, 33, 34] indicate that models must adopt distinct attention strategieswhen applied to different datasets. This has prompted numerous researchers to explore novel attention models in semantic segmentation tasks, such as Swin-Unet [35] and CSWin-Unet. Swin-Unet employs the Swin encoder, which integrates a shift window mechanism into the self-attention process. By shifting the window, Swin captures detailed information between patches. CSWin-Unet uses CSWin as an encoder, representing an enhanced iteration of Swin. CSWin employs horizontal and vertical attention mechanisms to continuously capture local and global features through self-attention.Additionally, various models have been developed to enhance efficiency through alternative approaches. For example, nnU-Net [36] investigates the potential of enhancing sub-features within feature maps during preprocessing to improve the model's capacity to capture intricate details. PVT [37] has devised a novel encoder architecture based on a Spatial Attention mechanism and constructed a pyramid vision transformer. PVTv2 [38] introduces three new elements to PVT: overlapping block embeddings, linear complexity attention layers, and convolutional feedforward networks. In a more recent development, [PERSON] proposed the Multi-scale Hierarchical Vision Transformer [39] (MERIT), which combines two versions of MaxViT to address the limitations in capturing multi-scale information. MERIT employs a combination of \(MaxViT224\) and \(MaxViT256\) to construct the encoder. \(MaxViT224\) accepts feature maps of size 224 \(\times\) 224 as input, capturing fine-grained features, while \(MaxViT256\) accepts feature maps of size 256 \(\times\) 256, capturing global features. These encoders are used in an alternating sequence, enabling MERIT to perform self-attention operations at multiple scales, thereby enhancing its generalization capacity. Despite its excellent performance, MERIT is constrained to combining different versions of the same attention mechanism and cannot fully leverage the potential of a dual backbone network encoder.
# Exploiting Data Similarity to Improve SSD Read Performance
## 5 Experiment
### Results and Analysis
#### 5.2.2 Distribution Degree of Redundancy Data  
Figure 11 illustrates the average redundancy rate for the eight workloads under different allocation schemes. The redundancy rate for static allocation ranges from 2.3% to 46.5%, with an average of 18.5%. The high redundancy rate in the static allocation is primarily caused by the assignment of duplicate data with the same LPN to the same planes in webmail workloads. This results in an uneven data distribution, excessive data duplication, and missed opportunities for parallel read access. The dynamic allocation scheme, which employs a round-robin mapping strategy with update tokens, effectively reduces the data duplication rate. The duplication rate in the dynamic allocation ranges from 1.9% to 29.3%, with an average of 13.5%. In contrast, DS-FTL allocates duplicate data to different planes whenever possible, enhancing the efficiency of subsequent read requests. The final duplication rate for DS-FTL ranges from 1.2% to 28.3%, with an average of 12.2%. In comparison to static and dynamic allocation, DS-FTL consistently reduces data  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trace** & **Total I/Os** & **Write I/Os** & **Write Ratio** & **Average Request Size (KB)** \\ \hline home1 & 8,973,201 & 8,882,831 & 0.990 & 16.06 \\ \hline home2 & 5,390,703 & 4,901,091 & 0.909 & 23.47 \\ \hline home3 & 918,010 & 908,863 & 0.990 & 34.16 \\ \hline home4 & 2,491,157 & 2,354,060 & 0.945 & 7.99 \\ \hline online & 5,700,499 & 4,211,806 & 0.739 & 7.99 \\ \hline webmail & 7,795,815 & 6,381,985 & 0.819 & 7.99 \\ \hline webresearch & 2,414,031 & 2,414,008 & 0.999 & 24.57 \\ \hline webusers & 5,697,272 & 5,127,101 & 0.900 & 12.31 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Workload characterization.  
duplication in individual planes across all workloads, resulting in a reduction ranging from 3.4% to 62.8%, with an average reduction of 23.6%.  
To further investigate the impact of DS-FTL on reducing data duplication, we compare the duplication rates between static allocation and DS-FTL in different planes for the home2 workload, as demonstrated in Figure 12. DS-FTL significantly reduces the duplication rate in planes with a high density of duplicate data, enhancing uniformity in the overall data distribution.  
Figure 10: Read and write latency under different allocation schemes. (**a**) Write Latency. (**b**) Read Latency.
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
## 4 Analytical Energy Consumption Model
### Analytical Model
#### 4.2.1 Packet Generation and Transmission
Finally, \(\alpha\) is an auxiliary parameter defined as follows:  
\[\alpha=\left[\frac{T_{slp}}{T_{c}}\right] \tag{1}\]  
Then, if node A requires \(k\) tries to successfully send a packet to B, the energy wasted to exclusively complete the transmission of such packet obeys the following equation:  
\[E_{T}(\mathrm{A})=(k-1)E_{C}(\mathrm{A})+E_{C}^{\prime}(\mathrm{A})+E_{l}^{DAR} \tag{2}\]  
In this expression, \(E_{C}(\mathrm{A})\) is the energy wasted by node A in any non-successful transmission cycle, \(E_{C}^{\prime}(\mathrm{A})\) is the energy wasted in the last (successful) transmission cycle, in which node A actually receives an acknowledgement, and \(E_{l}^{DAR}\) is the energy wasted in idle listening during the final DELAY_AFTER_RECEIVE period. More specifically, we have:  
\[E_{C}(\mathrm{A}) =E_{l}^{CCA}+E_{tx}^{\mathit{left}}(d_{f}(\mathrm{A}))+E_{l}^{ack} \tag{3}\] \[E_{C}^{\prime}(\mathrm{A}) =E_{l}^{CCA}+E_{tx}^{\mathit{left}}(d_{f}(\mathrm{A}))+E_{rx}^{ack} \tag{4}\]  
Here \(E_{l}^{CCA}\), \(E_{tx}^{\mathit{left}}(d_{f}(\mathrm{A}))\), \(E_{l}^{ack}\) and \(E_{rx}^{ack}\) are respectively the energy losses experienced by node A to sense the channel, transmit a single packet to B, wait for the corresponding acknowledgement and actually receive such acknowledgement.
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 3 Overview of Wound Pathophysiology and Wound Healing Process
### 3D Bioprinted Wound Dressings
#### 3.2.1 Bioinks for Wound Dressings
In the second type, cells are bioprinted without an exogenous biomaterial referred to as the scaffold-free approach, which mimics embryonic development.
# Hybrid Shielding for Hypervelocity Impact of Orbital Debris on Unmanned Spacecraft
## 2 Numerical Modelling  
The main aim of the proposed research is to explore and optimise the benefits of using lightweight honeycomb structures in the Whipple shield design, with potential application in unmanned spacecraft. A set of numerical models is developed and validated, based on an aluminium honeycomb Whipple shield. These passive shielding models are implemented in LS-DYNA and described in detail in the following paragraphs. The validated models are then used to perform a thorough analysis of the effects of different shield parameters (core density, impact location, etc.) on the energy absorption performance and impact protection of the Whipple shield.
## 2 Test Procedures
### Tested Material, Specim Preparation and Saturation Method  
The quartzitic silt soil in powdered form used in the tests was taken from Nantong tidal flat area, China. The Nantong silt was taken from within 2-5 m below the seabed. The detailed location is shown in Figure 3. Figure 4 shows the particle size distribution of Nantong silt, including 57.7% silt, 1.3% clay, and 41.0% fine sand particles. The natural void ratio is 0.935. The index properties of the silt are summarised in Table 1. Specimens with an outer radius of \(r_{\text{o}}\) = 50 mm, inner radius of \(r_{\text{i}}\) = 30 mm, and height of \(H\) = 200 mm were tested. The specimens were prepared by dry deposition method in eight equal-mass layers; a more detailed process of sample preparation was presented in [25]. All specimens were tested under saturated, carbon dioxide flushing, and de-aired water flushing was carried out first, then back pressure saturation followed [25]. When Skempton's \(B\)-value > 0.95, the specimen was considered fully saturated [26]. Following saturation, \(p^{\prime}\)0 = 100 kPa was applied to all specimens to complete the isotropic consolidation.  
Figure 2: GDS hollow cylinder apparatus test system.
## 3 Results and Discussion
### Radiation Patterns  
Radiation patterns for the proposed antenna are observed at 3.2 GHz, 5.2 GHz, 6 GHz, and 8.9 GHz. The measured and simulated E-H plane radiation patterns for band stop and in-band frequencies are presented in Figure 7. The measured and simulated H-plane radiation patterns were found to be omnidirectional, while the E-plane radiation patterns for notch frequencies of 3.2 GHz, 5.2 GHz, and 8.9 GHz show the directional patterns. The radiation patterns are found to be stable for the entire UWB.
# Navigation Safety on Shipping Routes during Construction
## 2 Literature Review
### Literature Analysis
It should be noted that it is very difficult to reduce or exclude risk factors that influence the ship, especially human factor impact, due to a small number of crew on various ships, very intensive work regimes, and other reasons. The analysis of the available literature on ships' passing limitations in narrow passages revealed that despite in-deep investigations conducted regarding the research issue, there is still a lack of comprehensive research results directly related to navigation safety on shipping routes during performed construction work when ship's sailing conditions may rapidly change, and it is necessary to make the correct decision to avoid collisions or accidents in narrow channels. Therefore, this article is focused on developing the methodology and showing its practical application to increase navigational safety and avoid ship collisions or accidents in mentioned conditions.
# GNSS Signal Acquisition Algorithm Based on Two-Stage Compression of Code-Frequency Domain
## 4 Simulation Results
### Results on Dynamic Acquisition  
Figure 7 shows the detection probabilities of the proposed algorithm with different Doppler frequencies. When the \(SNR=-12\) dB, \(FCR\) = 2:1, the Doppler frequency offsets are 0 kHz, 26 kHz, 52 kHz and 78 kHz, the detection probabilities are 96.3%, 93.2%, 98.5%,  
Figure 5: The acquisition results with different \(FCR\): (**a**) \(FCR\) = 8:1; (**b**) \(FCR\) = 4:1; (**c**) \(FCR\) = 2:1.  
Figure 6: The detection probabilities with different \(SNR\).  
and 92.3%, respectively, and their values are very close to each other. When the \(FCR\) is changed to 4:1 or 8:1, we can get a similar conclusion. As analyzed in Section 3.1, the frequency-domain preprocessing improves the correlation bandwidth, keeping more neighboring acquisition frequency points within the correlation bandwidth, which greatly reduces its sensitivity to Doppler frequency.  
Figure 8 compares the detection probabilities of the proposed algorithm with other traditional acquisition algorithms. With the increase of Doppler frequency, the acquisition probabilities of the PMF-FFT and the CC acquisition algorithms decrease. A large Doppler frequency restricts the matching filter length of the PMF-FFT acquisition algorithm [32] and the correlation bandwidth of the CC acquisition algorithm, which limits their application in highly dynamic situations. As we can see, when the Doppler frequency is greater than about 52 kHz, the acquisition performance of the CFC acquisition algorithm begins to surpass that of the other two algorithms.  
Figure 8: The detection probabilities of several algorithms with different \(SNR\): (**a**) Doppler = 0 kHz; (**b**) Doppler = 26 kHz; (**c**) Doppler = 52 kHz; (**d**) Doppler = 78 kHz.  
Figure 7: The detection probabilities with different Doppler frequencies.
# Research on Distributed Autonomous Timekeeping Algorithm for Low-Earth-Orbit Constellation
## 3 Methodology  
This section presents the estimation method for the filter noise matrix parameters and redesigns the D-KPW time scale algorithm by integrating the advantages of the Kalman filter and the weighted average algorithm. Based on this, the D-KPW (Control) algorithm is proposed, which reduces computational complexity while improving various performance metrics.
# Multi Ray Model for Near-Ground Millimeter Wave Radar
###### Abstract  
A quasi-optical multi-ray model for a short-range millimeter wave radar is presented. The model considers multi-path effects emerging while multiple rays are scattered from the target and reflected to the radar receiver. Among the examined scenarios, the special case of grazing ground reflections is analyzed. Such a case becomes relevant when short range anti-collision radars are employed in vehicles. Such radars operate at millimeter wavelengths, and are aimed at the detection of targets located several tens of meters from the transmitter. Reflections from the road are expected to play a role in the received signal strength, together with the direct line-of-sight beams illuminated and scattered from the target. The model is demonstrated experimentally using radar operating in the W-band. Controlled measurements were done to distinguish between several scattering target features. The experimental setup was designed to imitate vehicle near-ground millimeter wave radars operating in vehicles. A comparison between analytical calculations and experimental results is made and discussed.  
Keywords: radar; millimeter wave radars; multipath; multi-ray model +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Microscopic Test Analysis
Within the fully carbonated zone, the significantly weakened alkaline environment fails to provide sufficient alkalinity for alkali-activation reactions, rendering mechanical performance predominantly dependent on calcite's pore-filling effects on harmful voids [54]. The strength of the active carbonation zone arises from the combined contributions of calcite-induced pore densification and the bonding effect of hydration-derived C-A-S-H gel on shield machine muck particles. The non-carbonated zone maintains a highly alkaline environment, where compressive strength correlates with hydration reaction progression [55]. The temperature field modulates the CO2 diffusion rate, Ca2+ dissolution kinetics, and pore water evaporation process, thereby influencing calcite formation dynamics. It concurrently alters alkaline ion migration behaviors within pores and ultimately governs harmful pore-filling efficiency and the alkaline microenvironment required for subsequent C-A-S-H gel formation. The humidity field governs calcite's pore structure improvement effects by driving pore water migration under humidity gradients, while synergistically regulating alkaline ion transport to constrain the kinetics of later C-A-S-H gel generation. Calcite, exhibiting a strongly negative zeta-potential in alkaline solutions, generates repulsive forces and crystallization pressure during excessive formation, inducing volumetric expansion [50]. Thus, a strict control of temperature-humidity conditions during carbonation curing is essential to ensure calcite's positive microstructural contributions. The coupled temperature-humidity regulatory mechanism, governing both harmful pore filling efficiency and cementitious product formation environments, constitutes the critical control factor determining material macro-mechanical performance. Figure 12: Mechanism of temperature and humidity effects on the mechanical properties of alkali-activated slag-solidified shield muck materials during carbonation curing.
## 5 Experiments
### Results and Discussion
#### 5.2.2 Respiration Detection in Scenario 1  
The first experiment was conducted with three subjects; here the results of subject 1 are visualized in Figures 8 and 9, and the results of three subjects are listed in Table 2. In the experiment, the signal measured by respiratory belt was considered the ground truth signal. Figures 8b and 9b show the time domain and frequency domain of reference respiration, from which we ascertain that the reference RR is around 0.3418 Hz. According to the maximum energy method, the raw slow-time echo signal of human target can be obtained, as shown in Figure 8a, and the corresponding frequency spectrum is shown in Figure 9a. It shows that we are unable to observe the clear respiratory component because it was submerged by the interference from UAV motion. Thus, we must compensate the platform motion to achieve accurate results.  
After applying each process of the method described in Section 4, the clear respiratory signal can be automatically extracted from the mixed signal as shown in Figure 8c. In its frequency spectrum as shown in Figure 9c, the maximum peak appears at 0.3652 Hz, which is the detected RR of the subject. The results of the background residual method are indicated in Figures 8d and 9d, and the detected RR is also 0.3652 Hz. Table 2 displays the metrics comparison between the reference method and the proposed method in this work. It shows that in the detection area with relatively smooth ground, both of the two methods can separate respiratory signal from platform motion interference, but this proposed method has a better effect in the aspects of res  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario 1**} & \multicolumn{2}{c}{**RR (Hz)**} & \multicolumn{3}{c}{**Accuracy (\%)**} & \multicolumn{3}{c}{**SNR (dB)**} \\ \cline{2-9}  & **Reference** & **Our Method** & **BGR** & **Our Method** & **BGR** & **Our Method** & **BGR** \\ \hline Subject 1 & 0.3418 & 0.3652 & 0.3652 & 93.15 & 93.15 & 15.82 & 10.56 \\ Subject 2 & 0.2032 & 0.2153 & 0.2210 & 94.05 & 91.24 & 16.18 & 11.49 \\ Subject 3 & 0.2889 & 0.2833 & 0.2833 & 98.05 & 98.05 & 15.27 & 11.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of respiratory rate in scenario 1. The data length was 30 s for each experiment.  
Figure 7: Observed signals extracted using the range sampler.  
Figure 8: Results of subject 1 in scenario 1. (a) Raw radar echo signal of subject 1 obtained by maximum energy method. (b) Reference respiration from respiratory belt. (c) Respiration extracted using our proposed method. (d) Respiration extracted using background residual method.  
Figure 9: _Cont._