esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
## 4 Simulator and Field Experiment
### Doppler Effect  
When transmitters or receivers move relative to each other, the apparent frequency shift in the received signal is known as the Doppler effect. Transmission through LoRa was found to be immune to the Doppler effect. It has been found that this immunity allows the use of the LoRa modulation in satellite radio communications on routes over 500 km without the limitations related to the Doppler effect [19]. At the same time, we wanted to use the Doppler effect to estimate the relative position of the nodes. In a study conducted to investigate the Doppler effect on the LoRa components [20], it appeared that that the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Device** & **D1** & **D2** & **D3** & **D4** \\ \hline A1 & 36.7 & 253.1 & 241.3.0 & 412.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Node distance measurement results.  
Figure 11: The node found its relative position using the wireless sensor network protocol.  
most affected transmission from the Doppler effect was in Spread Factor (SF) 12. Therefore, the researchers investigated the Doppler effect using vehicles moving at different speeds between 50 km/h and 80 km/h while transmitting a 50 bit message during a transmission time of more than 2 s.  
The researchers noted that although LoRa was immune to the Doppler effect, it likely captured frequency anomalies due to the Doppler effect and predicted the speed of the moving node.  
With this proposition, we could estimate the approaching or receding of two radio transmitters relative to each another. The Doppler effect would indicate whether two aircraft were moving away from or closer to each other. If they came closer, then using the Doppler effect, we could estimate the distance between the two aircraft and even verify the reliability of the aircraft. Aircraft flying in space and transmitting a radio signal cause the Doppler effect depending on their direction. Assuming that an aircraft reported movement in a certain direction, but the measurement of the Doppler effect indicated that it was not moving or even moving in the opposite direction, we could determine that it was an fake aircraft. If we added the Doppler effect as an additional layer of protection to the algorithms that we described in the previous sections, then it would be possible to check whether an aircraft's responses to the WSN matched the Doppler effect. Otherwise, the system would report the possibility of a cyberattack such as an impersonator.
# Coastal Wind Measurements Using a Single Scanning LiDAR
## 2 Materials and Methods
### Scanning Mode and the Velocity Volume Processing (VVP) Method  
#### 2.3.1 Scanning Mode  
Four scanning modes were available for the 100s: the plan position indicator (PPI) mode, range height indicator (RHI) mode; Doppler beam swinging (DBS) mode, and fixed mode. The choice of scanning mode depends on the purpose of the measurements. The RHI mode and the fixed mode are frequently used for wind turbine wake measurements [15; 16] and virtual met tower measurements [24], respectively. The current study uses the PPI mode, as it is compatible with the VVP method [25; 26; 27] for retrieving horizontal wind speeds and directions from the LiDAR-measured radial wind speeds.  
In the PPI mode, the scanning LiDAR measures the radial wind speeds by sweeping the scanning head over a range of azimuth angles while the elevation angle is fixed. In this study, a scanning range of up to 3000 m, with a radial resolution of 50 m and a data accumulation time of 1 s, were employed as the scan settings. The data accumulation time is the integration interval used to derive each radial wind speed. As the azimuth and elevation angle settings, such as the scanning width and scan rate, would be the primary factors affecting the accuracy of the wind speed measurements, the PPI measurements were carried out using different azimuth and elevation angles in this study.  
Table 2 lists the experimental duration and scan settings for each experimental case. The reference sensor (V1 or SA) used for each validation is shown in the \(U_{\text{ref}}\) column. The evaluation height for each case is also described in the table. The impacts of the azimuth angle settings were investigated in the experiments for Cases 1 through 5. In Cases 1 to 4, the accuracies of the wind speeds at a height of 47 m were compared for four different azimuth widths. In Case 5, the settings were identical to those in Case 4 except for the scan rate, which was reduced to \(1^{\circ}\) (from \(3^{\circ}\)) so that the number of data at the  
Figure 2: Snapshot of the carrier-to-noise ratio (CNR) distribution obtained from the 100s with an azimuth width of \(45^{\circ}\) and an elevation angle of \(0^{\circ}\) for the period 2019-05-11 09:05:33 to 09:05:47 LST.  
given radial position increased. This allowed us to investigate the effect of the number of input data on the quality of wind speed computed using the VVP method. In Case 6 and Case 7, the impact of the elevation angle on the measurement accuracy was investigated by comparing the results for different elevation angles. Case 8 had identical settings to those of Case 2, but ran for a longer period, to assess the impact of the wind direction. In addition to the validation of the 10-minute mean wind speeds and wind directions, the accuracy of the TI values was also examined by comparing them against the measurements obtained from the SA in Case 9.
# An Efficient Dummy-Based Location Privacy-Preserving Scheme for Internet of Things Services
## 3 Enhanced-DLP Scheme  
In the domain of the IoT privacy-preservation, the dummy-based location scheme has been widely used for preserving the user's location privacy. For example, the DLS and DLP schemes are two well-known dummy-based location selection schemes for a data-driven IoT LBS, which consider the side information available to an adversary. However, the computational cost of these two schemes are very expensive, and the DLS is vulnerable to privacy breaches with Attack scheme for DLS(ADLS) attacks (This is a dummy-based location selection attack scheme proposed in reference [9]). Aiming at solving these problems, we present an Enhanced-DLP scheme which can efficiently select dummy locations to form an anonymous set. The Enhanced-DLP scheme selects dummy locations in an enhanced greedy manner for a tradeoff between computational cost and privacy requirements, which can effectively reduce the computational complexity of the dummy location selection and improve the level of privacy protection for the user.
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 5 Electrospinning of Chitosan
### Voltage  
Voltage plays an important role in the electrospinninging process and has an influence on the nanofiber diameter and the surface morphology of the nanofiber mat. If the electrostatic force outweighs the surface tension of the electrospinninging solution, a thin thread in the nano-range is produced. By regulating the voltage, different morphologies can be produced depending on the final requirement, from nanofibers to membranes [99; 100].
## 5 Methodology
### Training  
Training the trajectory imputation model involves several key components that contribute to its overall performance. In this subsection, we elaborate on some crucial parts of the training phase, like the optimization strategy, gradient clipping, and learning rate scheduler. But firstly, let us talk about the core training strategy. Incomplete trajectories are termed as features and corresponding missing points are termed as labels. This is how the training was performed, so during the testing phase, given some incomplete trajectories, we obtained the missing points of corresponding trajectories as outputs. The dataset was split into training, validation, and testing sets using a ratio of 70% for training, 15% for validation, and 15% for testing  
* **Optimization** To update the model parameters, an Adam optimizer was utilized. The Adam optimization algorithm computed the adaptive learning rates for each parameter based on their first-order moment estimate (mean) and the second-order moment estimate (uncentered variance). The update rule for the parameters \(\theta\) is given by \[\theta_{t+1}=\theta_{t}-\frac{\text{lr}}{\sqrt{\theta_{t}}+\epsilon}\hat{m}_ {t}\] (16) where \(\hat{m}_{t}\) and \(\hat{v}_{t}\) are the biased first- and second-moment estimates, \(\epsilon\) is a small constant to prevent division by zero, and lr is the learning rate.
* **Gradient Clipping** Gradient clipping was employed to prevent exploding gradients during backpropagation. This technique involves scaling the gradients if their norm exceeds a predefined threshold (max_grad_norm). The scaled gradient \(g^{\prime}\) was computed as follows: \[g^{\prime}=\left\{\begin{array}{ll}\frac{\text{max\_grad\_norm}}{\|g\|}\cdot g &\text{if }\|g\|>\text{max\_grad\_norm}\\ g&\text{otherwise}\end{array}\right.\] (17)
* **Learning Rate Scheduler** The learning rate was scheduled using the ReduceLROnPlateau (a PyTorch library function which decreases learning rate when a metric has ceased improving) scheduler. This scheduler adjusted the learning rate if the validation losses plateaued, enabling the model to fine-tune its parameters more effectively. The learning rate update rule is given by: \[lr_{new}=\text{factor}\times lr_{old}\] (18) where \(lr_{old}\) is the previous learning rate, and factor is a user-defined factor.
## 3 Challenges in Terahertz-Based FSO Communication
### Manufacturing Challenges in THz Communication
#### 3.6.3 Material Constraints and High Absorption Loss  
The traditional metallic and dielectric materials used in RF and optical components exhibit high losses at THz frequencies, particularly due to free-electron absorption and polarization effects. The selection of low-loss materials, such as high-resistivity silicon, indium phosphide (InP), gallium arsenide (GaAs), and 2D materials like graphene, remains an active research area. However, the scalability and fabrication costs of these materials pose major obstacles for commercial THz communication devices [57].
# A Fast Hole-Filling Method for Triangular Mesh in Additive Repair
## 5 Hole Filling Based on Hole Size  
### Small-Sized Hole Filling by Centroid-Based Method  
In the case of small-sized holes, since the number of hole boundary vertices is small and their boundary shapes are not complicated, the missing regions will not heavily affect the completeness of the whole mesh. Besides, fastly closing the small-sized holes can also make the filling patches assort with surrounding meshes well wherever they appear in flat or high curvature regions. Therefore, a fast and simple technique may be very reasonable for handling this type of holes instead of applying advanced hole-filling algorithms. In our work, a simple and fast algorithm named centroid-based method is utilized to fill small-sized holes, and the pseudocode is in the Algorithm 1. The hole-filling process is as follows: as shown in Figure 4a, there exists a closed loop including six boundary vertices \((v_{1},v_{2},...,v_{6})\), and the hole can be identified by using the method described in Section 4.2. Depending on these hole boundary vertices, their centroid vertex \(v_{0}\) can be calculated, as shown in Figure 4b. For each pair of two continuous boundary vertices, they will be connected with \(v_{0}\) to create a new triangle in counterclockwise order with oriented normal (see Figure 4c). By looping the rest of boundary vertices in the same way, the other five new triangles can be created. Therefore, the hole shown in Figure 4a is finally closed by inserting six newly created triangles.  
```
0:\((v_{0},v_{1},...,v_{k})\)\(\{\)Hole boundary vertices\(\}\)
0:\(P_{f}\)\(\{\)Faired filling patch\(\}\)
1: Compute the barycenter \(v_{c}\) of the hole loop \((v_{0},v_{1},...,v_{k})\)
2:for\((v_{0},v_{1},...,v_{k})\)do
3: Create a new triangle \(f_{i,j,c},(i,j)\in k\) by connecting each pair of continuous vertices \((v_{i},v_{j})\) and \(v_{c}\).
4:endfor
5:Produce the final filling patch \(P_{f}\)
```  
**Algorithm 1** Small-sized hole filling
## 3 The Hybrid Distributed Topology Control Scheme (HDTC)
### Phase III: Reforming the Communication/Routing Group  
The proposed group reformation strategy considers a vehicle joining/leaving a group and maintains a group in a fully-distributed way. In order to keep the local network structure stable, the group size may be a key parameter to achieve sufficient network reachability. However, due to node movement, the group size changes as time proceeds. In the proposed approach, referring to the neighboring information and  
Figure 6: Flow diagram of the HDTC scheme.  
its group size, each vehicle (e.g., vehicle _u_) triggers its group update process if necessary. The following subsections describe possible schemes for handling new admissions and releases of nodes in a group.
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results
### BOSS Algorithm Tests
#### 3.3.2 Ghost Noise Removal  
This test aims to investigate the BOSS algorithm behaviour against a nonuniform gradient noise: ghost noise. This undesired noise source is due to camera lens reflections, spacecraft structure reflection and dust all over the camera lenses. The ghost noise is named in such a way due to its appearance: it is a vague and partially transparent shape superposed on the background and image objects and a nonuniform gradient signal which may cover the whole image or part of it. If it is too strong, it can hide the useful signal associated with stars and RSOs and make them difficult or even impossible to be segmented and detected like the stray light. A change in the ghost noise intensity during the mission may cause the loss of information if no calibration is performed. This could be avoided with a calibration-less algorithm such as the U-Net-based proposal in this work, and it is shown with simulated images in Figure 9 and with real images in the next sections. This noise does not increase with the increase of the sensor exposure time, while the gradient level tends to be more uniform. Here, the same facility has been used but with an added external flashlight source. The stray light and ghost noise were produced with three different positions of an external flashlight source which was moved at different heights on the left side of the camera and target screen inside the darkroom. Even here, it is possible to see  
Figure 9: Left column: images from NSSF with ghost noise; right column: segmented images by the BOSS algorithm; from top to bottom three different ghost noise scenarios (**a–c**).  
a good segmentation quality provided by BOSS (Figure 9). A particular thing that can be noted is the white edges at the images' corners. They are due to a displayed undesired box in the night sky images made by Stellarium.
## 3 Results
### Mitigation of Odor and Targeted Gases as a Function of UV-A Dose
#### 3.1.6 Particulate Matter  
The PM percent reduction ranged from 9% to 55% for all tracked particulate size ranges, except for PM-1; however, the mitigation effect was not significant (Table 6). The measurements showed variation in PM concentration in the swine barn exhaust, which likely affected the lack of statistical significance. The significant reduction of PM with UV-A photocatalysis was demonstrated in the pioneering study by [PERSON] et al. (2012) [21] and deserves to be investigated further, especially in the context of airborne pathogens.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size of PM** & **Control (mg\(\cdot\)m\({}^{-3}\))** & **Treatment (mg\(\cdot\)m\({}^{-3}\))** & **\% Reduction (_p_-Value)** \\ \hline Total PM & 0.22 \(\pm\) 0.16 & 0.20 \(\pm\) 0.09 & 9.1 (0.89) \\ PM 1 & 0.10 \(\pm\) 0.13 & 0.12 \(\pm\) 0.06 & \(-\)20.0 (0.68) \\ PM 2.5 & 0.09 \(\pm\) 0.11 & 0.06 \(\pm\) 0.04 & 33.3 (0.48) \\ PM 4–PM 10 & 0.08 \(\pm\) 0.08 & 0.06 \(\pm\) 0.04 & 25.0 (0.81) \\ PM 10 & 0.11 \(\pm\) 0.07 & 0.05 \(\pm\) 0.04 & 54.5 (0.39) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mitigation of PM with UV-A photocatalysis treatment. Control temperature = 28.5 \(\pm\) 2.3 \({}^{\circ}\)C, control RH = 69.8 \(\pm\) 9.5%, treatment temperature: 31.5 \(\pm\) 1.2 \({}^{\circ}\)C, treatment RH = 66.0 \(\pm\) 4.3%. Values in the table are mean \(\pm\) S.D.
###### Abstract  
Smart home environments, which consist of various Internet of Things (IoT) devices to support and improve our daily lives, are expected to be widely adopted in the near future. Owing to a lack of awareness regarding the risks associated with IoT devices and challenges in replacing or the updating their firmware, adequate security measures have not been implemented. Instead, IoT device identification methods based on traffic analysis have been proposed. Since conventional methods process and analyze traffic data simultaneously, bias in the occurrence rate of traffic patterns has a negative impact on the analysis results. Therefore, this paper proposes an IoT traffic analysis and device identification method based on two-stage clustering in smart home environments. In the first step, traffic patterns are extracted by clustering IoT traffic at a local gateway located in each smart home and subsequently sent to a cloud server. In the second step, the cloud server extracts common traffic units to represent IoT traffic by clustering the patterns obtained in the first step. Two-stage clustering can reduce the impact of data bias, because each cluster extracted in the first clustering is summarized as one value and used as a single data point in the second clustering, regardless of the occurrence rate of traffic patterns. Through the proposed two-stage clustering method, IoT traffic is transformed into time series vector data that consist of common unit patterns and can be identified based on time series representations. Experiments using public IoT traffic datasets indicated that the proposed method could identify 21 IoTs devices with an accuracy of 86.9%. Therefore, we can conclude that traffic analysis using two-stage clustering is effective for improving the clustering quality, device identification, and implementation in distributed environments.  
device identification; internet of things; machine learning; traffic analysis; two-stage clustering +
Footnote †: journal:  
00 2023 by the authors.  
[EMAIL_ADDRESS] (M.A.); [EMAIL_ADDRESS] (T.Y)
# End-of-Life Scenarios for Mass Timber: Assumptions, Limitations and Potentials--A Literature Review
## 3 Results
### End-of-Life Scenarios: Reuse, Recycling, Energy Recovery and Landfill
#### 3.5.1 Reuse  
Twenty-nine papers have addressed reuse as an important EoL scenario. Reuse refers to the second or subsequent utilization of mass timber components for their originally intended purpose without any structural changes in the material [41; 42]. Reusing existing wood products not only reduces the need for extracting virgin resources but also extends the carbon storage period of the reused wood. [PERSON] et al. [34] suggest that reusing mass timber products may support sustainable forest practices by reducing the demand for harvesting, a benefit that conventional LCA approaches cannot fully capture.  
In addition, several studies have emphasized the importance of considering reuse at the design stage, specifically through the approach known as Design for Disassembly and Reuse (DfD&R). [PERSON] [4] and [PERSON] and [PERSON] [43] have identified downstream and upstreamDfD&R, with downstream focuses on salvaging materials from existing demolished buildings, whereas upstream focusing on designing strategies to facilitate the future reuse of wood when it reaches the EoL. [PERSON] et al. [44], also suggested incorporating dismantlable components in design as part of the building process. [PERSON] et al. [45] have highlighted the current lack of consideration of designs for disassembly, evident in the vast amount of construction and demolition waste (CDW) directed to landfill, which represents an undesirable EoL scenario from a climate-conscious perspective. This research suggested that producing standardized modular elements of wood can help to avoid storage costs and increase the market acceptance for salvaged timber. In addition, reversible joints in mass timber, such as reusable metal brackets or carpentry mortise-and-tenenon joints, though typically more expensive, could be effectively designed if circular construction becomes mandatory through policy requirements [45]. [PERSON] et al. [46] have also suggested wood joints and wood nails over metal fasteners for an even lower environmental impact. Similarly, in the studies of [PERSON] et al. [47], thermo-hydro-mechanical densified wooden nails were developed and tested in order to avoid the use of adhesives or metal in timber assemblies.  
The prefabricated and modular nature of mass timber such as CLT already manifests many advantages in terms of structural capacity, design flexibility, fire, seismic and thermal performances, as well as construction efficiency with a dry construction process [18]. These qualities could be further incorporated with reversible construction methods in order to maximize reuse of mass timber at its EoL.
# Mechanical Structural Design and Actuation Technologies of Powered Knee Exoskeletons: A Review
## 4 Other Key Technologies
### Control Strategies  
The control system design of the power-assisted wearable knee joint exoskeleton robot is widely concerned because it largely affects the power-assisted effect. At present, a common control strategy of the knee exoskeleton is not available due to its vast diversity in design purposes, hardware structures, and actuation modes [8; 89]. In particular, according to the integration idea of the control system, the control strategy of the knee exoskeleton mainly includes offline trajectory setting, online trajectory regulation, and real-time trajectory tracking [90].  
The offline trajectory setting is adopted in the medical exoskeleton of knee joint for rehabilitation training of patients and can be completed by manually setting the desired position information or with the gait data of healthy subjects [68; 91]. This control strategy is simple and direct, but its efficiency is low and other auxiliary tools are generally used to ensure the balance of the system. Based on the measurements of sensors, the online trajectory regulation of the knee exoskeleton can be executed with model-based controlmethods [21; 92]. This control strategy is usually required to ensure that the trajectory is in an acceptable safe range and that information input is used to identify human intentions for the adaptation to the parameter changes of the real walking scenario. However, the knee exoskeleton is a typical human-machine interaction system, so different users and walking conditions significantly affect its performance. It is necessary to design a real-time trajectory tracking method for target knee exoskeletons based on the measurements of human-machine coupled interaction and motion information of the wearer [30; 32; 93]. A variety of controllers can be chosen. PID control is a common choice and other choices include adaptive control, sliding mode control, and impedance controller with disturbance rejection. The controller needs to respond to the movement information of the wearer in a timely manner like the human central nervous system, so the control system requires a fast response to the movement of the wearer and minimize human-machine interference [94]. In other words, the knee joint exoskeleton robot and the wearer are interrelated and unified. It is necessary to consider both the driving effect of exoskeleton robot on human body and the leading role of human motion intention, so as to achieve high assistance performance and wear comfort.