esa-sceva/satcom-chunk-collection huggingface dataset:
# CNN Attention Enhanced ViT Network for Occluded Person Re-Identification
## 4 Results and Discussion
### Ablation Study and Visualization
#### 4.4.2 Spatial Attention Enhancement Module  
We conducted several ablation studies on the SAEM of AET-Net to verify its efficacy. To explore the effect of the weighted hyperparameter \(\lambda\) on SAEM, we gradually increased the parameter \(\lambda\) on the Occluded-Duke dataset to enhance the effect of the spatial attention feature, and the results are shown in Figure 6a. SAEM performance is best on the Occluded-Duke dataset when \(\lambda\) takes the value of 0.9. The results in SAEM ablation studies on the Market-1501 and Occluded-Duke are illustrated in Table 5, corresponding to index-1 to index-3, and it can be seen from the results that under both the occluded and non-occluded datasets, SAEM shows a slight performance improvement. The results testify that the global feature classification ability extracted by the model is improved under the effect of spatial attention.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**Market-1501**} & \multicolumn{2}{c}{**Occluded-Duke**} \\  & & **mAP/\%** & **Rank-1/\%** & **mAP/\%** & **Rank-1/\%** \\ \hline \multirow{2}{*}{CNN-Based} & HOReID [2] & 84.9 & 94.2 & 43.8 & 55.1 \\  & HACNN [38] & 82.8 & 93.8 & 26 & 34.4 \\ \hline \multirow{2}{*}{Transformer} & ViT-based & \multirow{2}{*}{86.8} & \multirow{2}{*}{94.7} & \multirow{2}{*}{53.1} & \multirow{2}{*}{60.5} \\  & (baseline) & & & & \\ \end{tabular}
\end{table}
Table 4: Comparison results of Transformer architecture and CNN-based architecture.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multirow{2}{*}{_SAEM_} & \multirow{2}{*}{_MFTM_} & \multirow{2}{*}{\(\lambda\,\mathrm{1}\,\delta\)} & \multicolumn{2}{c}{**Market-1501**} & \multicolumn{2}{c}{**Occluded-Duke**} \\  & & & & **mAP/\%** & **Rank-1/\%** & **mAP/\%** & **Rank-1/\%** \\ \hline \(1\) & & & & 86.8 & 94.7 & 53.1 & 60.5 \\ \(2\) & \(\surd\) & & & 86.4 & 94.5 & 51.5 & 58.1 \\ \(3\) & \(\surd\) & & \(\surd\) & 87.2 & 94.7 & 53.9 & 61.6 \\ \(4\) & & \(\surd\) & & 87.0 & 94.1 & 52.3 & 59.9 \\ \(5\) & & \(\surd\) & \(\surd\) & 87.5 & 94.8 & 54.4 & 62.4 \\ \(6\) & \(\surd\) & \(\surd\) & & 87.4 & 94.7 & 53.5 & 61.2 \\ \(7\) & \(\surd\) & \(\surd\) & & 87.5 & 94.8 & 54.5 & 64.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation study of aet-net on Market-1501 and Occluded-duke datasets. \(\lambda\)\(\delta\) denotes the use of the attention-weight hyperparameter.
# Experience Replay Optimisation via ATSC and TSC for Performance Stability in Deep RL
## 6 Conclusions  
The significant finding of our study is that a dual memory with an alternating transition selection strategy can improve the stability of the deep deterministic policy gradient (DDPG) algorithm across multiple buffer sizes. The experience replay optimisation (ERO) and dual-memory experience replay (DER) methods are combined to formulate the experience replay optimisation via alternating transition selection control (ERO-ATSC) with transition storage control (TSC) and the ERO-ATSC without TSC frameworks. The frameworks are further evaluated on different buffer sizes, sampling ratios, and experience retention strategies. The findings reveal that a sampling ratio of 9:1 and a retention strategy of F2F1 (first replay buffer uses enhanced FIFO and the second buffer uses FIFO) produce the steadiest average performance across the four buffer sizes. Though both versions of ERO-ATSC with TSC have competitive results and demonstrate the highest performance among the evaluated algorithms, the reward version proves superior in reward accumulation. In future, we hope to extend the proposed framework (ERO-ATSC with TSC) to automatically predict the reward threshold.  
Conceptualisation, R.S.O. and D.L.; Funding acquisition, D.L.; Supervision, D.L.; Writing--original draft, R.S.O.; Writing--review and editing, R.S.O. and D.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding and the APC was funded by the Vellore Institute of Technology, Vellore, India.  
Not applicable.  
Experiments were not conducted on existing datasets but on simulated environments of the OpenAI Gym platform. The analysed data can be generated when the algorithm is implemented on the same environments using the specified system specifications. The corresponding author can be contacted for any further clarification if the need be.  
The authors declare no conflict of interest and have no relevant financial interest in the manuscript.
# Depth Image Super Resolution Based on Edge-Guided Method
## 2 Proposed Method
### Edge Recovery Using Sparse Coding
#### 2.1.2 Edge Map Recovery  
Once we get LR-HR dictionary pair \(\left\{\mathbf{A}_{l},\mathbf{A}_{h}\right\}\), high-quality edge map \(\mathbf{E}_{h}\) can be represented by a sparse linear combination of HR dictionary atoms. Before starting reconstruction, we first process the input LR image \(\mathbf{I}_{l}\) to obtain an LR edge map \(\mathbf{E}_{l}\). The process can be divided into the following three steps:  
(1) The input image \(\mathbf{I}_{l}\) is interpolated to the same size as the desired HR image using bicubic interpolation algorithm, producing an LR image \(\hat{\mathbf{I}}_{l}\).  
(2) Shock filter [27] is applied to suppress zigzag effect produced by up-sampling interpolation.  
(3) Canny operator is used to extract edge \(\mathbf{E}_{l}\) from \(\hat{\mathbf{I}}_{l}\).  
Then, the HR edge map \(\mathbf{E}_{h}\) can be reconstructed from \(\mathbf{E}_{l}\) using the LR-HR dictionary pair \(\left\{\mathbf{A}_{l},\mathbf{A}_{h}\right\}\). The process is described in Algorithm 2.
## 3 A Trust Anchor for a Home Network's CA  
We have so far only elaborated on the certification scheme and the establishment of trust. However, one major problem inside home networks lies within the fact that home networks are no safe place _per se_. This is because their users are normally inexperienced and do not care much about security. Therefore, the chance that malware is resident inside the home network is not to be neglected. Whenever a certification authority is run in such an unsafe environment, the private signing key of the CA is endangered.  
For instance, if malware succeeded in stealing a Home CA's private signing key (later referred to as the _Home Key (HK)_), the attacker would be able to sign certificates for his own entities that look as if they had been signed by the legitimate Home CA. If default access rights to services within another home network are granted to members of the compromised home network, the attacker can access those services. Theft of data, abuse of services, _etc._ can be the consequences, see Figure 4. Without protection against malware, identifiers issued by the Home CA are less trustworthy as it can not be guaranteed that the identifier is legitimate.  
A technique that makes it difficult or even impossible that the Home Key can be stolen is needed. Again Smart Cards would be a candidate technology, but because of the reasons listed above, we believe that they need a strong framework to be useful. A technology often used is the so called _Trusted Platform Module (TPM)_ which has been specified by the _Trusted Computing Group_.  
Of course, malware could also steal the private key and certificate of an end entity. The protection methods we describe can also be applied with minor changes to their private keys. However, in this article, we focus exclusively on the protection of the Home CA's key.
# Numerical Simulation-Based Study of Controlled Particle Deposition Technology for Wafer Surfaces
## 3 Results and Discussion
### Simulation Results and Analysis
#### 3.1.2 Nozzle-to-Wafer Distance  
Based on the dimensionless constant _h/D\({}_{w}\)_ proposed by [PERSON] et al. in deposition experiments--where \(h\) represents the nozzle-to-wafer distance and _D\({}_{w}\)_ denotes the wafer diameter--it has been observed that when _h/D\({}_{w}\)_ exceeds 2.0, the deposition rate is inversely proportional to the nozzle-to-wafer distance [21]. As an essential variable, airflow height directly influences particle-substrate interactions and deposition performance. To further investigate the spatial distribution characteristics of particle deposition, this study systematically examines the comprehensive effect of incident height on deposition behavior. The deposition distribution contours for different nozzle-to-wafer distances are illustrated in Figure 5.  
A nozzle-to-wafer distance of 5 mm was selected based on the design of impactor-type samplers, where inertial forces dominate and the nozzle height is typically 1.2 to 1.8 times the nozzle diameter. Simulation results show that the deposition rate is high at this height. However, the deposition spots are overly concentrated, which causes excessive particle aggregation and reduces deposition uniformity. In contrast, at a nozzle-to-wafer distance of 50 mm, deposition spots become scattered and fail to form a coherent deposition pattern. This is because the contact probability between particles and the wafer surface decreases significantly, with most particles being carried away by the airflow, resulting in a sharp decline in deposition efficiency.  
Compared to the nozzle-to-wafer distances of 5 mm and 50 mm, a height of 15 mm yields the best deposition uniformity, with a relatively high DPM concentration. Simulation results show that at this height, aerosol streamlines exhibit optimal adherence to the wafer surface, achieving a balance between inertial and diffusive forces. This balance leads to significant advantages in both deposition efficiency and concentration distribution. Based on a comprehensive comparison of the simulation results at different nozzle-to-wafer distances, 15 mm is identified as the optimal nozzle-to-wafer distance.
# Building Damage Assessment Based on Siamese Hierarchical Transformer Framework
## 2 Methods
### Two-Stage Framework  
Both tasks of change detection and damage assessment are based on the detection of the changed features of the image pairs, where the siamese structure is widely used for the network. However, compared with the goal of change detection, which is to find the regions of change between two temporal images, the building damage assessment task has some key differences. To fit the building damage assessment task, we use a two-stage framework and split the task into building localization and damage classification instead of directly applying the siamese network. The reasons are as follows.  
* Firstly, instead of simply detecting changes, the damage level of each building also needs to be classified from the changes in the building damage assessment task. Therefore, locating buildings from the pre-disaster images provides more precise localization results and guides the damage classification in Stage 2.
* Secondly, the intact buildings are also required to be detected. In other words, the unchanged building objects need to be located and classified as the level of no damage, which is not required in the change detection task.
* Thirdly, a two-stage framework helps resolve the offset noise between two temporal images. In the building localization, only the pre-disaster image is used to ignore the offset noises from the post-disaster image. Moreover, the model trained in Stage 1 is transformed into the siamese network for classification, and the weights are shared. This strategy allows the network to process the pre-disaster and post-disaster images using the same approach, which helps overcome the offset noises.
###### Abstract  
The ground-motion simulation of regional-specific earthquake scenarios is crucial for the seismic design of key facilities. Herein, we considered parameter uncertainty in ground-motion simulations and the impact of multiple faults when determining the maximum credible ground-motion parameters of large earthquakes at a near-field dam. The source models of the Daiu-Lijiang, Xiaozhongdian-Daju, and Longpan-Qiaohou faults were established based on geological and geophysical data. Although the method for identifying asperity is not yet mature and still faces many difficulties, it provides an opportunity to identify the non-uniform slip distribution on the rupture plane by earthquake scenarios. A multi-scheme stochastic finite-fault simulation method was then used to estimate the minimum; mean; maximum; and 50 th-, 84 th-, and 95 th-percentile values of the peak ground acceleration and pseudo-spectral acceleration response spectra. The results showed that the Longpan-Qiaohou fault can generate the largest ground-motion parameters compared with the other two faults. Moreover, this result was supported by the statistical analysis of the results of twelve thousand simulations of these three faults. Thus, it can be concluded that the maximum credible ground-motion parameters are represented by the 84 th-percentile pseudo-spectral acceleration response spectrum of the Longpan-Qiaohou fault. This finding will benefit the seismic safety design of the target dam. More importantly, this multi-scheme method can be applied to other key facilities to obtain reasonable ground-motion parameters.  
ground-motion simulation; multi-scheme stochastic finite-fault simulation method (multi-SFFSM); maximum credible earthquake; asperity
# Preparation and Characterization of CPCM for Thermal Energy Storage Unit
## 3 Results and Discussion  
### SEM Test Results  
To show the EG porous structure before and after LA absorption, Figure 3a gives a 6000-times SEM image of EG with 100 mesh, Figure 3b gives that of EG with 200 mesh, Figure 3c gives that of CPCM3 synthesized by 100-mesh EG and LA. EG shows a worm-like porous structure with many cavities in \(\upmu\)m sizes to house LA [20; 21; 22]. Compared to the EG with 100 mesh, the hole arrangement of the EG with 200 mesh is more uniform, and the hole diameter is smaller, which results in a larger specific surface area and porosity. After LA is adsorbed, the porous structure of EG is not changed, and cavities are mostly filled by LA accompanied by some spaces.
# Carbon Emission Analysis of RC Core Wall-Steel Frame Structures
## 3 Results
### Impact of Material Manufacturing Processes
This reduction is attributed to the EAF process mainly utilizing natural gas as fuel, which results in lower carbon emissions compared to the BF-BOF process which utilizes coal and petroleum. In the scenario where the BOF process incorporates 30% recycled scrap steel, the carbon emissions of the model decrease by 22.2%. Furthermore, when the EAF process utilizes 80% recycled scrap steel, the carbon emissions of the model decrease by 56.4%. Compared to applying different admixtures in concrete, different steel manufacturing processes and recycled scrap steel significantly reduce carbon emissions in the construction of high-rise buildings. This is primarily due to the substantial contribution of carbon emissions of steel to the total carbon footprint. Therefore, in the production of construction materials, prioritizing more efficient manufacturing processes of steel can significantly reduce carbon emissions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Production Process** & **Steel Type** & \begin{tabular}{c} **Carbon Emission Factor** \\ **(kgCO2e/t)** \\ \end{tabular} \\ \hline BF-BOF & \begin{tabular}{c} Large-scale steel \\ Medium- and small-scale steel \\ Steel bar \\ \end{tabular} & \begin{tabular}{c} 2380.00 \\ 2350.00 \\ 2340.00 \\ \end{tabular} \\ \hline EAF & \begin{tabular}{c} Large-scale steel \\ Medium- and small-scale steel \\ Steel bar \\ \end{tabular} & \begin{tabular}{c} 1650.00 \\ 1620.00 \\ 1610.00 \\ \end{tabular} \\ \hline BF-BOF with 30\% recycled scrap & \begin{tabular}{c} Large-scale steel \\ Medium- and small-scale steel \\ Steel bar \\ \end{tabular} & \begin{tabular}{c} 1707.00 \\ 1677.00 \\ 1667.00 \\ \end{tabular} \\ \hline EAF with 80\% recycled scrap & \
\begin{tabular}{c} Large-scale steel \\ Medium- and small-scale steel \\ Steel bar \\ \end{tabular} &
\begin{tabular}{c} 522.00 \\ 492.00 \\ 482.00 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Carbon emission factors of steel with different manufacturing processes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Manufacturing Process** & \begin{tabular}{c} **Carbon Emission in** \\ **Production Stage** \\ **(tCO2e)** \\ \end{tabular} & \
\begin{tabular}{c} **Carbon Emission in** \\ **Transportation Stage** \\ **(tCO2e)** \\ \end{tabular} &
\begin{tabular}{c} **Total** \\ **(tCO2e)** \\ \end{tabular} \\ \hline BF-BOF & 12,804.057 & 312.033 & 13,116.09 \\ EAF & 10,028.908 & 312.033 & 10,340.941 \\ BF-BOF with 30\% recycled scrap & 10,245.598 & 312.033 & 10,557.631 \\ EAF with 80\% recycled scrap & 5740.734 & 312.033 & 6052.767 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Carbon emission utilizing steel with different manufacturing processes.
## 2 Material and Methods
### Equipment and Measurements
In this way, each condition was set up, and the signal was recorded without the influence of another component during the measurement of the insulator in question. The ultrasound equipment was positioned with the captor directed with the focus towards the insulator, considering the ultrasound equipment is directional [21]. Equivalent fault detection applications based on acoustic detection are presented in [28; 46]. The microphone used to record the ultrasonic signal is the M500 model from Petterson. The equipment's microphone is based on an advanced high-speed electret. The frequency range evaluated in this experiment is 10 to 210 kHz, which corresponds to PD's frequency range. In order not to lose signal, the connection is made digitally through a universal serial bus (USB) 2.0 interface, using a USB on-the-go (OTG) cable to connect the device. The complete equipment specifications are shown in Table 1.  
This capture of the ultrasound signal was later subdivided into 10 files of 5 s each. Whereas measurement of partial discharge (PD) can assist in fault identification the ultrasound shows promising equipment [25]. The use of ultra high frequency for PD detection can be used for insulation defect classification [47] Radio frequencies are widely used to identify patterns and solve problems in different fields of engineering [48; 49; 50]. Partial discharges occur in the insulator's vicinity, so the direction in which the ultrasound detector is positioned can be decisive for fault location. As the sound signal propagates in several directions, there may be a misinterpretation of which component is defective when several components are nearby. The exact determination of which component is defective depends on the operator's experience. Specific applications for detecting faults in insulators using ultrasound are promising. According to [PERSON] [49] using an ANN to classify different types of defects based on the characteristic extracted from the signal, it can result in an accuracy greater than 95%. [PERSON], [PERSON] and [PERSON] [28] obtained an accuracy of 85% for classification of common discharges in insulators that are installed outdoors using an ANN-based algorithm. The methodology used in this paper is quantitative [51], by comparing the accuracy obtained for the selected cases, which are described in Section 2.1.
# A Novel Reconfigurable Vector-Processed Interleaving Algorithm for a DVB-RCS2 Turbo Encoder
## 1 Introduction
### Error Correction  
Wireless communication channels are inherently susceptible to various noise sources and interference, which can significantly degrade the integrity of transmitted information. As data transfer rates escalate to meet the demands of modern applications, there is an imperative need for encoding schemes that not only facilitate rapid data transmission but also ensure robust error detection and correction mechanisms. Forward Error Correction (FEC) techniques address this need by enabling the receiver to identify and correct errors without necessitating retransmission, thereby enhancing data reliability and reducing latency.  
Turbo Codes (TCs), introduced in the early 1990s, marked a significant milestone by approaching the theoretical limits of channel capacity as defined by [PERSON]'s seminal work on information theory [1; 7]. By incorporating redundant bits and leveraging iterative decoding processes, TC allows for effective error correction even in noisy environments. The fundamental principles of TC have been instrumental in shaping subsequent developments in error-correction methodologies.  
In recent years, the landscape of FEC has evolved to meet the stringent requirements of next-generation wireless systems, including 5G and 6G networks [8]. Emerging coding schemes such as Low-Density Parity-Check (LDPC) codes and Polar codes have gained prominence due to their superior error correction capabilities and suitability for high-throughput applications. These codes offer enhanced performance in terms of bit error rates and decoding efficiency, which makes them integral to modern communication standards.  
Moreover, innovative approaches like the Error Correction Code Transformer have been proposed to unify various decoding strategies within a single framework [9]. This Transformer-based architecture demonstrates the potential to handle multiple linear block codes, including LDPC and Bose-Chaudhuri-Hocquenghem (BCH) codes, thereby offering flexibility and efficiency in decoding processes. Such advances are pivotal in addressing the diverse error correction needs of contemporary wireless communication systems.  
In addition, adaptive hybrid FEC schemes have been developed to dynamically respond to fluctuating channel conditions. For example, combining Reed-Solomon (RS) and LDPC codes with adaptive algorithms like Kalman filters enables real-time adjustments to coding parameters, optimizing error correction performance in scenarios such as video transmission over unreliable networks [10; 11].  
These progressive developments in error correction techniques underscore the ongoing commitment to improving data integrity and transmission efficiency in the face of evolving communication challenges [12].