esa-sceva/satcom-chunk-collection huggingface dataset:
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 2 Overview of Radio Wave Propagation for 6G Systems
### Throughput in 6G Networks  
In [36], the channel was characterized at 350 GHz based on ray-tracing simulations where the high-gain antennas influence the transmission conditions and position of the transmitter (Tx) for optimal signal coverage; and the achievable data rate was presented for different propagation conditions in generic indoor scenarios. At the same frequency of 350 GHz, [PERSON] et al. [37] measured the propagation loss using a VNA-based system to estimate the communication link performance and stated that the wireless communication link at 350 GHz can support 1 Gpbs and 100 Gbps along 8.5 m and 1 m links, respectively. In [32] it was reported that 1.3 Tbps and 0.9 Tbps throughput can be achieved over 0.1 m links within a 140 GHz bandwidth of the 260-400 GHz band for LoS and NLoS scenarios, respectively. Based on the applied full 3D ray tracing tool in an indoor office for a THz link of 5.5 m at 300 GHz, it was found, using highly directive antennas, that the THz channels can support up to several 100 GSymbols/s without any inter-symbol interference (ISI) [38].  
A summary of these channel characterizations at 0.1-1 THz is provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Frequency (GHz)** & **Scenario** & **Link Length (m)** & **Channel Characteristics** \\ \hline
[14] & 100–300 & Outdoor LoS & 8 & Snow attenuation \\
[15] & 103 & Outdoor LoS & 400 & Rain attenuation \\
[16; 17] & 120 & Outdoor LoS & 400 & Rain attenuation \\
[18] & 140.7 & Outdoor LoS & 800 & Rain attenuation \\
[19] & 148 and 156 & Outdoor LoS & 325 & Rain attenuation \\  & & Indoor LoS & 1.67 & Path loss, RMSDS, \\
[9; 27] & 300 & Indoor NLoS & 4.7 & and reflection \\
[26] & 300 & Indoor room & - & diffraction \\
[28; 29] & 300 & Indoor office & - & Path loss, K-factor, \\  & & & & RMSDS, and AoA spread \\
[30] & 300–320 & Indoor desktop LoS & 0.7 & Path loss and RMSDS \\
[31] & 300–316 & Indoor desktop LoS & 0.25 & Path loss \\
[37] & 350 & Indoor LoS & 8 and 1 & Path loss and throughput \\
[32] & 260–400 & Indoor desktop LoS & 0.1–0.95 & Path loss and throughput \\
[25] & 100 & Typical building materials & - & Effective attenuation \\
[33] & 140 & Indoor LoS, & 0.4–0.9 & Path loss, reflection, \\  & & OLoS, and NLoS & & and RMSDS \\
[34] & 140 & Indoor LoS & 35 & Path loss \\
[8] & 140 & Indoor office LoS & 5 & Path loss \\
[35] & 126–156 & Indoor office LoS & 1.6 & Path loss \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel characterization studies in the 0.1–1 THz range. RMSDS, root mean squared delay spread.
## 3 Background of the Methods Used
### Particle Swarm Optimization (PSO) Algorithm  
PSO is a swarm algorithm inspired by the behavior of the particles/social animals, such as fish, or birds. It is a stochastic optimization method that was introduced and developed by [PERSON], [PERSON] [43]. It was also classified as one of the metaheuristic techniques. The main idea of the PSO algorithm is to make better social information sharing among individuals in a crowd. Each individual act as a particle in the swarm. Then, they implement a searching procedure in a searching space. During the search process, they share information and experience to update better locations [44]. Thus, it was also considered as an evolutionary computation technique in the statistical community [44; 45; 46; 47; 48; 49]. The PSO algorithm implements five steps for optimal searching:  
- Step 1: Initialize the aboriginal population and velocity of particles. Subsequently, compute the particles fitness and detect the most logical place as local best and global best.  
- Step 2: Every particle flies roundly in the space of search with the initial velocity as established in the first step. The speed depends on the local best and global best. For each loop, the best solution is corresponding to the local best, and the best spot of the particle obtained so far is corresponding to the global best. In other words, corresponding to the local best and global best in each loop, the velocity is updated in this step. It is described as:  
\[v_{j}^{j+1}=wv_{j}^{(i)}+(c_{1}\times r_{1}\times(local\ best_{j}-x_{j}^{(i)}) )+(c_{2}\times r_{2}\times(\text{global\ best}_{j}-x_{j}^{(i)})),v_{\text{min} }\leq v_{j}^{(i)}\leq v_{\text{max}}\]  
where \(x_{j}^{(i)}\) indicates the position of the particles; \(v_{j}^{(i)}\) denotes the speed of the \(j^{\text{th}}\) particle at the \(i^{\text{th}}\) iteration; \(w\) represents the coefficient of inertial weight; \(i\) is the number of repetitions; and \(r_{1}\) and \(r_{2}\) symbolize numbers in the interval [0,1].  
- Step 3: After the new velocity is calculated and updated, the particles fly in the search space with the new speed. Corresponding to each position, the fitness of them is determined and updated through a fitness function (i.e., RMSE).  
- Step 4: Update local best and global best for the better position with lower RMSE. The local best can be updated as:  
\[x_{j}^{i+1}=x_{j}^{(i)}+v_{j}^{(i+1)};\ j=1,2,\ldots,n \tag{2}\]  
- Step 5: Check the satisfaction of the searching. If the fitness of the particle is the best (i.e., lowest RMSE), stopping the searching. Otherwise, return to step 2.  
The pseudo code of the PSO algorithm for optimization of searching is shown in Figure 3.
## 4 Experiments and Result Analyses
### Datasets
#### 4.1.1 Automobile Wheel Hub Defect Dataset  
The wheel hub images in this dataset are from the GDXray Casting dataset [31], and the wheels are aluminum. The original wheel defects in the GDXray Casting dataset are labeled with a bounding box and suitable for object detection, but not for semantic segmentation. We mark automobile wheel hub defects pixel-by-pixel to obtain labels that can be used for semantic segmentation.  
The automobile wheel hub defect dataset includes 348 images, which cover four typical defects, namely, fray, hole, nick, and spot, as shown in Figure 8.
# Development of an L-Band SAR Microsatellite Antenna for Earth Observation
###### Abstract  
A compact synthetic aperture radar microsatellite antenna operating in the L-band is presented. To reduce size and weight of the small spaceborne SAR, we utilize a lightweight deployable parabolic mesh reflector and operate at low Earth orbital altitudes. The antenna is a wrap-rib center-fed parabolic reflector with dedicated receiving and transmitting feeds. Antenna requirements are: gain better than 30 dBic, center frequency of 1.275 GHz with bandwidth of 28 MHz and circular polarization with axial ratio better than 3 dB. This work describes the development of a compact Circularly Polarized SAR L-band antenna system and the design considerations suitable for small spacecrafts. Simulation of the parabolic reflector and effects of different structural elements to the main radiation pattern were analyzed, which include ribs, struts, feed blockage, and mesh surface. A research model of the parabolic reflector was constructed, and the reflector surface verification was realized using two different approaches, a laser distance meter along ribs and the other using 3D scanning of the reflector surface. RMS errors were 1.92 mm and 3.86 mm, respectively, both below required 4.55 mm of surface accuracy. Near-field antenna measurements of the deployable reflector mesh antenna was realized for final antenna validation, presenting good agreement with the simulation results. Future work comprises prototyping and testing of the full polarimetric feed assembly.  
microsatellite; L-band; deployable; reflector; synthetic aperture radar; antenna; circular polarization +
Footnote †: journal:
# Allocating Small Transporters to Large Jobs
## 1 Introduction  
In many farming or construction processes, transporter resources are used to carry material to or from the operation site in a cyclic fashion. Examples for such applications are tractors collecting silage from a forage harvester, dump trucks getting filled with excavated earth, or cement or asphalt being fed from trucks to construction sites. Transporters are available on site for a limited amount of processing time, dependent on the loading capacity of the transporter and the processing speed of the main resource.  
After that, they have to unload/reload material in some central hub (e.g., a harvest silo, asphalt producer, etc.) before returning to the processing site. The time of absence depends on the distance between the operation site and the hub, but it is the same for all transporters assigned to the same site. Multiple transporters may be assigned to the same working site, so that processing can continue even when some transporters are on-road toward/from the central hub. If no other transporter is available on site, the main work has to pause until the next transporter arrives back on site. Two transporters may not be used for on-site processing at the same time.  
This kind of transporter setting is referred to by [PERSON] et al. [1] as _capacitated field operations_. Many instances of these types of processes have been studied in the literature from different angles, with different goals, and for different applications, see [1; 2; 3; 4; 5; 6; 7; 8]. Recent reviews are provided in [9; 10]. The important common feature of all these use cases is that the total volume that needs to be processed on site is usually much larger than the capacity of a single transporter. Figure 1 illustrates such a process for the example of farming.  
Since, usually, this type of problem is NP-hard (not solvable in polynomial time unless \(\mathrm{P}=\mathrm{NP}\), see [11]) when viewed as a whole (see [5]), authors turn to mathematical programming formulations (see [2; 5; 12]) or (meta-)heuristics (see [4; 5]).  
In this paper, instead, we do not consider the problem as a whole but instead investigate one specific, important sub-problem, namely the assignment of transporters to operation sites. This problem gives rise to an interesting, new scheduling problem, which, to the best of our knowledge, has not been studied in the literature before.
## 2 Background
### Crowd Selection in Crowdsourcing
#### 2.1.3 Personal-Trait-Based Crowd Selection  
Some studies have based their crowd selection process on crowd workers' personal traits. Personal traits differ from experience, where the latter is more concerned about the knowledge, expertise and performance in the domain of the crowdsourced task. However, personal traits involve cognitive abilities [17; 35], personality [36; 37], trustworthiness [38], punctuality [39] or personal goals (e.g., profit [40]) that are strongly associated with the quality of the contributions. Some personal-trait-based crowd selection approaches can be better suited in cases where historical performance data about crowd workers are not available, such as when a worker is new with no past activities or for one-time crowdsourcing campaigns [35].  
[PERSON] et al. [36] have presented an approach for selecting crowd workers based on their personality. They have used the Japanese TIPI-J test for personality consisting of ten questions and the Big Five traits test to select crowd workers for crowdsourced tasks. Additionally, since teams with matching personalities cooperate more effectively, [PERSON] et al. [37] have proposed an approach for selecting a team for performing crowdsourced tasks, where they have used the DISC group personality test to infer personality compatibility between team members.  
Other studies have placed their focus on cognitive abilities to select an appropriate subset of the crowd. [PERSON] et al. [35] have used five main cognitive tests, Stroop, Flanker, Task Switching, N-Back and Pointing, to measure crowd workers' cognitive abilities. The outcomes of these tests are utilized for crowd selection when crowdsourcing diverse kinds of tasks. Aligning with the aforementioned study is [PERSON] et al.'s [17], which uses attention tests to select crowd workers. Attention stems from cognition and the authors have implemented a web-based attention test called attentiveWeb to measure attention. In addition, another crucial personal trait is trustworthiness that has been considered for crowd selection by [PERSON] et al. [38]. They proposed an evolutionary algorithm that measures trust based on task and reward type to distinguish honest from non-honest crowd workers. Furthermore, [PERSON] et al. [39] have presented a social welfare maximizing mechanism (ESWM) to select crowd workers considering their speed and punctuality in accomplishing tasks.  
Motivation can be treated as a latent personal trait. In fact, incentives are one of the main drivers that motivate crowd workers to participate and perform well in a task [41]. Therefore, studies such as that by [PERSON] et al. [40] have worked on designing incentive mechanisms to stimulate and select a suitable crowd to participate in crowdsourcing tasks. Their mechanism consists of two designed algorithms, one for auctioning and another for updating crowd workers' reputation.
## 2 Related Work
### Privacy-Preserving Techniques in FL  
Several techniques have been proposed to enhance privacy in FL, including homomorphic encryption, secure multi-party computation (SMPC), and differential privacy [28; 29]. Homomorphic encryption and SMPC provide robust privacy guarantees but incur significant computational and storage overhead, rendering them impractical for resource-constrained IoT devices [30]. For instance, fully homomorphic encryption increases latency and energy consumption, making it unsuitable for real-time IoT applications [31]. Differential privacy, while computationally efficient, introduces noise to protect individual data, leading to potential degradation in model accuracy [32; 33].  
In resource-constrained IoT environments, lightweight cryptographic methods such as stream ciphers have gained attention due to their low computational overhead [20]. These methods avoid the heavy resource requirements of traditional block ciphers like AES [34] and offer high throughput for secure data transmission. However, most existing works do not fully address the trade-off between privacy protection and computational efficiency, leaving a critical gap for solutions tailored to heterogeneous IoT systems.
## 2 System Model
### System Throughput
As stated, this research aims to maximize the system throughput under the condition of guaranteeing the reliable communication of CUs and D2D pairs. Thus, we establish the model of system throughput according to the Shannon capacity formula as  
\[R^{B}=R_{D}+R_{C}\]  
\[=\sum_{i=1}^{N}B_{d}log_{2}(1+\gamma_{D\_i})+\sum_{j=1}^{M}B_{d}log_{2}\big{(} 1+\gamma_{C\_j}\big{)} \tag{6}\]  
s.t. \[\gamma_{D\_i}\geq\gamma_{min}^{(D)},\forall\ i\in Y \tag{7}\]  
\[\gamma_{C\_j}\geq\gamma_{min}^{(C)},\forall j\in X \tag{8}\]  
\[p_{D\_j}\leq p_{D\_max},\forall\ i\in Y \tag{9}\]  
\[y_{\text{C\_j}}=d^{-\frac{q}{i}}_{j,i}h_{j,\text{B}}s_{c}+\beta^{\text{CD}}_{ i,i}d^{-\frac{q}{i}}_{i,\text{B}}h_{i,\text{B}}s_{d}+N_{c} \tag{3}\]0  
where \(B\) denotes all possible allocation results;\(B_{d}\) is the bandwidth of sub-channel; \(R_{D}\) and \(R_{C}\) are the throughput of D2D pairs and CUs, respectively. The constraints in (7) and (8) represent the low bound of SINR for D2D pairs and CUs respectively, while (9) and (10) ensure that the transmit power of D2D pairs and CUs are not permitted to exceed the maximum power limit. The optimization problem indicated above is observed to be an NP-Hard problem. The exhaustive search method can perform well on this problem, while the complexity is high enough to limit the utilization in practice. Presently, fairness has not been taken into account in existing literature, as [29; 30]. The situation that one of the D2D pairs may communicate by using few RBs that tend to be occupied by other D2D pairs with better uplink states, and the method without fairness may contribute to a catastrophic result that the D2D pairs with the poor uplink states are bankrupt in the condition of communication for an extended period. As a result, the performance of communication is restricted and degraded. Resource Allocation Based on Weighted Efficiency Interference-Aware for D2D Underlaid Communication  
In this section, the proposed algorithm is introduced, and the details are presented. The transmit power of D2D pairs are obtained, so to traverse all RBs to access the SINR of D2D pairs and CUs, D2D pairs are clustered by their SINR under the condition that the CUs can communicate regularly. The resource allocation is implemented according to the result obtained from clustering.
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
###### Abstract  
Air traffic control (ATC) communication is susceptible to speech noise interference, which undermines the quality of civil aviation speech. To resolve this problem, we propose a speech enhancement model, termed DeCGAN, based on the DeConformer generative adversarial network. The model's generator, the DeConformer module, combining a time frequency channel attention (TFC-SA) module and a deformable convolution-based feedforward neural network (DeConv-FFN), effectively captures both long-range dependencies and local features of speech signals. For this study, the outputs from two branches--the mask decoder and the complex decoder--were amalgamated to produce an enhanced speech signal. An evaluation metric discriminator was then utilized to derive speech quality evaluation scores, and adversarial training was implemented to generate higher-quality speech. Subsequently, experiments were performed to compare DeCGAN with other speech enhancement models on the ATC dataset. The experimental results demonstrate that the proposed model is highly competitive compared to existing models. Specifically, the DeCGAN model achieved a perceptual evaluation of speech quality (PESQ) score of 3.31 and short-time objective intelligibility (STOI) value of 0.96.  
speech enhancement; air traffic control; DeConformer; deep learning; generative adversarial network (GAN) +
Footnote †: journal: _Algorithms_
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 5 Issues and Challenges in 5G and Edge-Based V2X
### Data Availability  
In 5G-based edge computing, the data may be stored, copied, and/or cached at various sites on the network. As the number of vehicles connected to MEC servers increases, in-network caching, as well as data replication is important for data availability. The large number of connected vehicles hinder real-time data availability of critical information. Moreover, a seamless data availability service is not assured when multiple vehicles request the same information concurrently or if the edge servers are interrupted by cyber-attacks.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Items** & **MEC** & **MCC** \\ \hline Architecture & Distributed & Centralized \\ Operators & Mobile operators & Cloud providers \\ Target user & Mobile devices & Internet users \\ Hierarchy & Three-tier & Two-tier \\ Data storage & Short duration & Long duration \\ Server nodes & Large in number & small in number \\ Bandwidth demands & Based on data sent to clouds & Based on data generated by users \\ Location awareness & Yes & No \\ Communication overhead & Medium & High \\ Information access & Localized information & Globalized information \\ Delay allowance & Lesser than 10 ms & Few seconds or greater \\ Connectivity & Intermittent & uninterrupted \\ Service provider & Cisco IOx & Amazon, IBM, Microsoft \\ Environment & Can exist indoors or outdoors & Exists indoor/consumes large space \\ \hline \hline \end{tabular}
\end{table}
Table 6: A detail comparison between Mobile Cloud Computing (MCC) and Multi-access Edge Computing (MEC) [41].
## 3 Data and Method
### Data
#### 3.1.2 Optical Images  
To observe changes in the glacial lake area and its surrounding surface conditions, we utilized 35 Landsat 8/9 images without cloud cover, spanning from January 2021 to September 2023. Band combinations primarily involved bands 3, 5, and 7, with a spatial resolution of 30 m. These images were then employed to delineate the outline of the studied glacial lake and analyze alterations in its area. Additionally, to further scrutinize surface changes, we selected two GF-2 images with a spatial resolution of 4 m captured before and after the GLOF, specifically on 26 December 2020 and 27 October 2023.
# Comparing Augmented Reality-Assisted Assembly Functions--A Case Study on Dougong Structure
## 5 User Study
### Experimental Results
#### 5.3.3 NASA-TLX Score  
As shown in Figure 14, the 3D viewer system has the highest NASA-TLX score (35.1), followed by the AR system (32.1) and the paper form (25.8). However, the one-way ANOVA showed that their differences are not statistically significant, F(2, 45) = 1.91, \(p\) = 0.16. The scores of six dimensions are listed in Table 8. The 3D viewer system has the highest score in mental demand, while the AR system has the highest score in physical demand.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **SS** & **df** & **MS** & **F** & \(p\) \\ \hline Between & 30.875 & 2 & 15.438 & 2.989 & 0.05 \\ Within & 232.438 & 45 & 5.165 & & \\ Total & 263.313 & 47 & & & \\ \hline \end{tabular}
\end{table}
Table 6: The ANOVA result of assembly errors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Media** & **Average** & **Standard Deviation** & **Min** & **Max** \\ \hline \multirow{3}{*}{Part fetching error} & Paper & 2.56 & 1.632 & 0 & 7 \\  & 3D Viewer & 1.31 & 1.014 & 0 & 4 \\  & AR & 1.44 & 0.964 & 0 & 3 \\ \hline \multirow{3}{*}{Position error} & Paper & 0.38 & 0.719 & 0 & 2 \\  & 3D Viewer & 0.50 & 0.632 & 0 & 2 \\  & AR & 0.13 & 0.342 & 0 & 1 \\ \hline \multirow{3}{*}{Orientation error} & Paper & 0.94 & 0.998 & 0 & 3 \\  & 3D Viewer & 0.88 & 0.885 & 0 & 3 \\  & AR & 0.44 & 0.727 & 0 & 2 \\ \hline \multirow{3}{*}{Incomplete assembly} & Paper & 0.38 & 0.619 & 0 & 2 \\  & 3D Viewer & 0.31 & 0.602 & 0 & 2 \\ \cline{1-1}  & AR & 0.31 & 0.602 & 0 & 2 \\ \hline \end{tabular}
\end{table}
Table 7: Experimental result of various errors (units: # of times).  
Figure 13: Numbers of various errors for the three forms.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Mental** & **Physical** & **Temporal** & **Performance** & **Effort** & **Frustration** \\ \hline
**Paper** & 30.18 & 13.68 & 29.21 & 25.65 & 18.94 & 12.73 \\
**3D Viewer** & 47.97 & 19.03 & 30.47 & 44.13 & 22.25 & 17.75 \\
**AR** & 32.88 & 30.34 & 27.28 & 37.88 & 19.78 & 19.28 \\ \hline \end{tabular}
\end{table}
Table 8: NASA-TLX sub-scores of the three forms.