esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Forklift Behavior
The explanation for the lack of payload effect at a lower weight could be that friction between the floor and the lower weight pallet load was not great enough to prevent the sliding of the pallet which reduced the intensity of the impact. However, at heavier weights the resulting frictional force between the floor and the pallet was enough to prevent the sliding of the pallet which increased the resistance and results in greater impact intensities. This result indicates that, at least for heavy top loads, the weight of the top load needs to be tracked during data collection. Due to the change in mean acceleration, pallet design and entry speed were found to be the most influential factors on the acceleration measured on the forklift at the moment of impact. Figure 5 shows the interactions between the main effects. A change in pallet design affects the mean peak acceleration for the different types of forklifts. The change from a plastic to a wooden pallet design is associated with higher peak accelerations for the reach truck and the electric forklift. Figure 4: Main effects plot for for forklift Peak Acceleration response. Tukey pairwise comparison indicates means that do not share a letter are significantly different at \(\alpha=0.05\). Entry speed also affects the acceleration response in each forklift. The effect of the change in speed was greatest for the reach truck (46% increase). The changes in the behaviors of the gas and the electric forklifts were similar. Pallet design was also affected in a different manner by a change in speed. The wooden design shows a greater peak acceleration response when the speed increased 0.22 m/s. Pallet design affected the peak acceleration response measured for the different top loads. When wood was impacted, there was a significant increase in the peak acceleration for the 1134 kg top load. Although the top load was significant, this behavior is pallet-dependent. The behavior is explained by the higher weight of the loaded pallet and the coefficient of friction, both of which create a greater resistance to impact. The acceleration measured during impacts with differently weighted top loads is also dependent on the entry speed. At higher speeds, there are significant differences between the 227 kg, 680 kg, and 1134 kg top loads (12-14% difference). At lower speeds, the acceleration measured was not significantly different for the 227 kg and 680 kg top loads.
## 2 Description of Experiments  
The experiments are carried out on a visual experimental system named DEBECO-LT (Debris Bed Coolability-Low Temperature). Figure 1 shows the diagram of the experimental system. In this paper, air and water are sent to the bottom of the test section, where they mix and flow into the porous media pipe. The wall of the experimental section is made of plexiglass, and two stainless steel wire meshes are fixed at the top and bottom to ensure that the glass balls will not move casually. Rosemont-3051 transmitters (0.04%) are fixed at the inlet and outlet of the test section to measure the differential pressure signals; the signal acquisition frequency is 400 Hz. FastcamMini Photron high-speed camera (102, 400 frame/s) is used to capture clear flow pattern photos. The previous paper describes the experimental system in more detail [27].  
The experimental pressure is equal to the local atmospheric pressure, about 0.1 MPa. The room temperature is about 10 \({}^{\circ}\)C. Spherical glass particles with diameters of 1.5 mm, 3 mm, and 6 mm are packed into three different porous media beds. The superficial velocity of air is 0.005-0.44 m/s and that of water is 0.59-1.17 mm/s. More details of the experimental setup can be found in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Particle Sizes (mm)**} & \multirow{2}{*}{**Porosity**} & \multicolumn{2}{c}{**Superficial Velocity**} & \multicolumn{2}{c}{**Reynolds Number**} \\ \cline{3-6}  & & **Water (mm/s)** & **Gas (m/s)** & **Water** & **Gas** \\ \hline
1.5 & 0.391 & 0.59\(\sim\)1.17 & 0.005–0.44 & 1.13–2.26 & 1.87–97.93 \\
3 & 0.385 & 0.29–1.17 & 0.005–0.44 & 1.21–5.12 & 1.98–161.92 \\
6 & 0.4 & 0.29–1.17 & 0.005–0.44 & 2.31–10.01 & 3.65–327.55 \\ \hline \end{tabular}
\end{table}
Table 1: Details of experiment setup.  
Figure 1: Diagram of DEBECO-LT and test section. 1—Stainless steel wire mesh, 2—Fittingflange, 3—Connecting pipe, 4—Gas–liquid separator, 5—Differential pressure transmitter, 6—Test section.
## 3 VRIS Image Steganography Model
### Basic Results of the VRIS Basic Model
#### 3.3.2 AI-Evasion Shield  
In the architecture of VRIS, the AI-Evasion Shield module (Table 2) is the key component to improve the steganographic performance of encrypted images, and the discriminator as the core of this module adopts a multi-layer cascaded convolutional network structure, with each layer incorporating a LeakyReLU activation function (with the negative slope set to 0.2). This choice aims to avoid the gradient vanishing problem, and ensure that  
Figure 4: Visual-Masker module structure.  
the gradient information can be effectively propagated even in the deeper layers of the network, accelerating the learning and enhancing the nonlinear feature extraction as well as the model's ability to characterize complex image data.  
In order to further enhance the stability and generalization ability of the model, a batch normalization layer is embedded after some of the convolutional layers, a strategy that effectively reduces the internal covariate bias, accelerates the training process, facilitates the extraction of more effective and stable feature representations from the input image, and enables the discriminator to focus on the most discriminative abstract information in the image by gradually reducing the spatial dimensionality of the features.  
Compared with the traditional ReLU activation function, the LeakyReLU activation function allows small negative gradients to pass through, thus alleviating the problem of neuron 'death', while the batch normalization layer stabilizes the training process and speeds up convergence by normalizing the inputs between layers.  
During the training process, the discriminator of AI-Evasion Shield receives two sets of inputs: one is the original cover image, and the other is the second-level encrypted image processed by the VRIS steganography algorithm with the addition of random Gaussian noise. Through a series of convolution, activation and batch normalization operations, the discriminator maps these input images to a scalar value between 0 and 1, which is converted by a sigmoid function and used as the discriminator's confidence score for the authenticity of the input images. This score not only reflects the discriminator's judgement on the authenticity of the image, but also provides an important reference for subsequent image processing and steganography techniques.  
Through this design, the AI-Evasion Shield discriminator not only learns to differentiate between authentic cover images and second-level encrypted images, but also significantly enhances the difficulty for machine learning models to detect hidden information. This adversarial training approach improves the discriminator's performance while simultaneously increasing the concealment of hidden information within the encrypted image.
# A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning
## 2 Thermal Consideration and FEM Stimulation  
### Pulsed Thermography  
In pulsed thermography (PT), a high-power exponential heating impulse is applied to the samples, and a thermal response is measured during a period of time. Due to the heat conduction, a surface region which has an internal defect underneath the surface perturbs the thermal waves propagation on the surface of specimens in comparison to the sound (non-defective) region. We can then see the changes of the temperature variation, since the internal defects possess different thermo-physical (conductivity, density and heat capacity) that have an impact on heat flow. These thermal differences can be observed as surface features and recorded with an infrared camera as indicated in Figure 1[10].  
Temporal evolutions can be observed from the defective regions and subsurface sound regions. A thermal contrast is acquired as a feature vector which is obtained distinctly via the thermal value from the defective region subtracted from the corresponding value from the surrounding sound region [11] as indicated in Equation (1), where \(Td(t)\) is the temperature value on the pixel point of the defect area. The temperature value on the reference point of the sound area is \(T_{s}(t)\) Then the \(\Delta T(t)\) is the absolute thermal contrast extracted from the defect and sound region. The thermal contrast  
Figure 1: Pulsed thermographic testing using optical excitation.  
is an excellent technique to distinguish the temperature difference to learn the depth of the defects, as shown in Figure 1.  
\[\Delta T(t)=T_{d}(t)-T_{s}(t) \tag{1}\]
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems
### Stable Latency Applications  
#### 12.2.1 Electronic Monitoring and Stabilization of Link Latency  
Since about 2005, there has been intense work carried out on the monitoring and stabilization of fiber path latency by sending a test signal to a remote site and back on a pair of closely spaced wavelengths (\(\approx\)0.8 nm) via the same fiber to minimize the influence of chromatic dispersion and strain. The stability of time transfer over a 1000 km long SMF link improved to few ps was reported by several teams [125; 126; 127], and a 0.3 ps stability during 3 h was achieved during time transfer via a 96 km fiber link in a metro network [128]. This method has been adopted for long- and medium-distance time distribution, where the cost of equipment is compensated for by use of cheap, and usually already installed, SMF.
# Focal Dice Loss-Based V-Net for Liver Segments Classification
## 1 Introduction
### Liver Segments' Classification  
There are some approaches proposed in the literature for liver segments' classification. Most of the published research tries to obtain the segments from an accurate modeling of the liver vessels.  
[PERSON] et al. employed a geometric fit algorithm, based on the least-squares method, to generate planes for separating the liver into its segments [45].  
[PERSON] et al. exploited a semi-automatic approach for calculating the segments [6]. The user has to input the root points for the branches of the portal vein and the hepatic veins, and then, a Nearest Neighbor Approximation (NNA) algorithm is implemented to assign each liver voxel to a specific segment. The classification was very straightforward: the voxel belonged to the segment whose branch was nearest to that voxel.  
Among the approaches that aim to directly classify liver segments, it is worth noting the work of [PERSON] et al. [21], who offered the first publicly available dataset for liver segments' classification. They realized an architecture based on U-Net [46], but with considerations for the Global and Local Context (GLC), resulting in a model that they referred to as GLC U-Net.  
[PERSON] et al. collected a huge CT dataset, composed of 500 cases, to realize CAD systems targeted at the liver region [47]. The ComLiver datasets include labels for liver parenchyma, vessels, and Couinaud segments. For the task of performing Couinaud segmentation, the authors compared only 3D CNN models, namely Fully Convolutional Networks (FCNs) [48], U-Net [46; 49], U-Net++ [50], nnU-Net [51], Attention U-Net (AU-Net) [52], and Parallel Reverse Attention Network (PraNet) [53].  
In this work, we introduce two possible definitions of the Focal Dice Loss (\(FDL\)), and we compare them with a definition already provided by [PERSON] et al. [54]. One of the proposed \(FDL\) definitions is similar to that of [PERSON] et al., whereas the other one is more adherent to the original formulation provided by [PERSON] et al. [55], considering the modulating factor on the single voxel and not on the whole Dice loss. In particular, we trained a 2.5D CNN model, the V-Net architecture, originally proposed for 3D segmentation by [PERSON] et al. [56], but in a multi-class classification fashion. We also implemented and tested a rule-based post-processing to correct the segmentation errors.
# Vulnerability Analysis of LTE-R Train-to-Ground Communication Time Synchronization
## 3 Attack Analysis of Time Synchronization Process of LTE-R Train-to-Ground Communication  
The time synchronization process of LTE-R train-to-ground communication is vulnerable to attack. The main reasons for this are as follows:  
* LTE-R has an all-IP architecture. The LTE-R railway wireless communication system is designed with an all-IP architecture, and the IP protocol is an unreliable packet communication protocol. It has a potential fault whereby the PTP message loss and wrong sequence caused by an attacker's intrusion are not detected. The attacker can continuously attack the PTP synchronization process, and this is not easily detected. This problem seriously affects the traffic safety of high-speed railways.
* The PTP sends synchronization messages in the form of multicast addresses [17]. Attackers can make full use of this feature to monitor and obtain the synchronization messages sent by the master clock. Moreover, attackers can make a spoofing packet with the frame format shown in Figure 4 to complete two-way deception between the master and slave clocks. This method is also imperceptible.  
Figure 3: The time synchronization process of LTE-R train-to-ground communication.  
In addition to meeting the frame structure depicted in Figure 4, the forged data packet also needs to meet the characteristics of the MAC destination address being a broadcast address and the message type being UDP. Due to the above factors, in the LTE-R time synchronization process, the attacker can imperceptibly join the PTP message interaction process between eNodeB and the OBC by forging the PTP message to complete the interception, maliciously tamper with the system, and delay the sending of normal PTP time synchronization messages. The time reference between eNodeB and the OBC drifts, resulting in inconsistent clocks of the train-to-ground equipment based on LTE-R, which seriously affects the safety of train operation.
## 5 Verification of the Prediction Model for Height of WFFZ
### Field Measurements of Height of WFFZ  
In Coal Mine A, panel 31110X near the stop line was selected for T1 drilling. Using the upward water injection method, the height of the WFFZ was measured on-site. The detection instruments used in this experiment are shown in Figure 9a.  
Figure 8: Development characteristics of aquifers in the roof of coal seam 3\({}^{-1}\).  
Figure 9b depicts the borehole positions and measuring point layout. The depth of the borehole is 180 m, and the angles with respect to the horizontal and vertical directions are 55\({}^{\circ}\) and 12\({}^{\circ}\), respectively. Twenty-one measuring points were set up for the water pressure testing. They are divided into three layout patterns. Two measuring points, 10 m apart, were located at the end of the hole and at a vertical height of 140 m, respectively. Fifteen measuring points, 3 m apart, were located between vertical heights of 140 to 100 m. These measuring points were used to search for the highest point of the water-flowing fractured zone. Three points, 15 m apart, were located at vertical heights of 100 m and below.  
Water pressurized into the formation at different measuring points in the borehole under different pressures was shown in Figure 10. At vertical heights of 93, 101, 110, and 124 m, the pressurized water volume reached a peak and then gradually decreased. In  
Figure 9: (**a**) Schematic diagram of experiment device. (**b**) T1 borehole orientation and measuring point layout.  
addition, water could not be injected at some of the measuring points between 90 and 120 m, indicating there should be no water-conducting fractures. And the total water injected at each measuring point in the borehole under different pressures was shown in Figure 11. After 126 m, the volume of water pressurized into the formation tended to level off, indicating that this position was close to the top of the water-flowing fractured zone. Based on a comprehensive analysis of the changes in the total amount of water injected and the amount of water pressurized into the formation in each test section, the measured height of the WFFZ in panel 31110X was 126 m.
###### Abstract  
As the Internet of Things (IoT) concept materialized worldwide in complex ecosystems, the related data security and privacy issues became apparent. While the system elements and their communication paths could be protected individually, generic, ecosystem-wide approaches were sought after as well. On a parallel timeline to IoT, the concept of distributed ledgers and blockchains came into the technological limelight. Blockchains offer many advantageous features in relation to enhanced security, anonymity, increased capacity, and peer-to-peer capabilities. Although blockchain technology can provide IoT with effective and efficient solutions, there are many challenges related to various aspects of integrating these technologies. While security, anonymity/data privacy, and smart contract-related features are apparently advantageous for blockchain technologies (BCT), there are challenges in relation to storage capacity/scalability, resource utilization, transaction rate scalability, predictability, and legal issues. This paper provides a systematic review on state-of-the-art approaches of BCT and IoT integration, specifically in order to solve certain security- and privacy-related issues. The paper first provides a brief overview of BCT and IoT's basic principles, including their architecture, protocols and consensus algorithms, characteristics, and the challenges of integrating them. Afterwards, it describes the survey methodology, including the search strategy, eligibility criteria, selection results, and characteristics of the included articles. Later, we highlight the findings of this study which illustrates different works that addressed the integration of blockchain technology and IoT to tackle various aspects of privacy and security, which are followed by a categorization of applications that have been investigated with different characteristics, such as their primary information, objective, development level, target application, type of blockchain and platform, consensus algorithm, evaluation environment and metrics, future works or open issues (if any), and further notes for consideration. Furthermore, a detailed discussion of all articles is included from an architectural and operational perspective. Finally, we cover major gaps and future considerations that can be taken into account when integrating blockchain technology with IoT.  
blockchain technology; Internet of Things (IoT); security; privacy; systematic; survey +
Footnote †: journal: _Sensors_**2023**, 23, 788. [[https://doi.org/10.3390/s23020788](https://doi.org/10.3390/s23020788)]([https://doi.org/10.3390/s23020788](https://doi.org/10.3390/s23020788))
# Object-Level Semantic Map Construction for Dynamic Scenes
## 3 Methodology
### Object Motion Detection through 3D Tracking Based on Object Model Database  
In a dynamic scene, the current frame data captured by an RGB-D sensor include the dynamic object. Generally, camera pose tracking is based on the assumption that the environment is static. However, as dynamic objects move independently of the camera, this assumption will be broken, resulting in inconsistent motion between the dynamic object and other parts of the image. Thus, dynamic objects inevitably affect the camera pose tracking and map construction and need to be eliminated. Considering the continuous motion nature of the moving objects, the method proposed in this paper makes full use of the scene prior knowledge in the dynamic object data and static object database updated by the last image frame to obtain motion information for each object in the database in the current frame. Note that the current frame is not segmented or associated with the object database at this point, so the dynamic part of the current frame is unknown.  
First, the OpenGL rendering pipeline is used to project the objects in the dynamic object database based on the camera pose corresponding to the last frame to produce a dynamic object mask. Assuming that moving objects generally undergo continuous motion, the dynamic part in the current frame is roughly eliminated by overlaying this mask and the current frame data. In fact, due to the motion of the camera and that of the moving object itself, the dynamic object mask cannot accurately cover the dynamic part of the current frame image. Therefore, the boundary of the dynamic object mask is expanded by 5 pixels, finally yielding the dynamic object mask \(Mask_{dynamic}\), as shown in Figure 2. This processing reduces the number of observations in the current frame data but is nevertheless preferable to adding outliers in camera pose estimation. Meanwhile, the non-dynamic map constructed at the camera pose corresponding to the last frame (including the static object and background models) is rendered. Then, alignment is performed between the current frame and rendered map by minimizing joint geometric error and photometric error functions for rough camera pose estimation. The geometric error and photometric error are defined as follows.