esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In recent years, the internet has developed rapidly. With the popularity of social media, uploading and backing up digital images has become the norm. A huge number of digital images are circulating on the internet daily, and issues related to information security follow. To protect intellectual property rights, digital watermarking is an indispensable technology. However, the common lossy compression technology in the network transmission process is a big problem for watermarking. This paper describes an innovative semi-blind watermarking method with the use of deep convolutional generative adversarial networks (DCGANs) for hiding and extracting watermarks from JPEG-compressed images. The proposed method achieves an average peak signal-to-noise ratio (PSNR) of 49.99 dB, a structural similarity index (SSIM) of 0.95, and a bit error rate (BER) of 0.008 across varying JPEG quality factors. The process is based on an embedder, decoder, generator, and discriminator. It allows watermarking, decoding, or reconstruction to be symmetric such that there is less distortion and durability is improved. It constructs a specific generator for each image and watermark that is supposed to be protected. Experimental results show that, with the variety of JPEG quality factors, the restored watermark achieves a remarkably low corrupted rate, outstripping recent deep learning-based watermarking methods.  
Keywords:invisible digit watermark; JPEG compression; deep learning; convolutional neural network; generative adversarial network +
Footnote †: journal: Computer Science
# Assessment of Novel Modal Testing Methods for Structures Rotating in Water
## 1 Introduction
Most of the previous studies employed the experimental modal analysis technique to extract the modal responses of disks, including their natural frequencies and mode shapes. An experimental modal analysis requires an actuator in order to excite the structure and sensors to measure its response. Subsequently, the frequency response function between the excitation and the measured response can be calculated, as can the mode shapes, the damping ratios and the natural frequencies. The selection of the actuator is a key part of the experiment. The traditional exciters, such as an instrumented hammer or a shaker, are useless in submerged structures because of limitations to accessibility or due to the fact that they might influence the modal parameters. In these specific conditions, exciters such as piezoelectric PZT patches have proven to be useful [26; 27; 28; 29; 30; 31; 32]. These studies demonstrated that the natural frequencies, mode shapes and damping ratios can be extracted from the response of a structure that is excited using PZT patches, even when they are not calibrated. This fact simplifies the experiment since the relation between the voltage and the dynamic force of a PZT patch is unknown and is not linear in the entire frequency range. Although there is an extensive bibliography on modal responses of disk-like structures, experimental investigations which assess novel modal testing techniques using PZT patches or rotating speed ramp-ups as exciters while measuring the responses from on-board and off-board positions in rotating and submerged systems have not been carried out. To deepen the insight in the modal responses of rotating structures also requires further investigations using numerical methods to simulate their modal responses. This article presents a study using different experimental and numerical methods to perform the modal analysis of rotating structures submerged in water. The paper is organized in five sections. First, it starts with the introduction. In the second section, the test rig, the instrumentation and the experimental methodology are presented. In the third section, the numerical models are introduced, and in the fourth section, the results are described and discussed. Finally, the main conclusions are summarized in the fifth section.
# Interpolation Methods with Phase Control for Backprojection of Complex-Valued SAR Data +
## 3 Interpolation Methods
### Nearest Neighbor Interpolation  
Nearest neighbor interpolation is one of the simplest interpolation methods, the principle of which is to estimate a value of the unknown sample by assigning the known data value of the nearest neighbor sample. The nearest neighbor interpolator \(p_{\rm n}(\tau)\) can be described on the real-valued interval \([\tau_{0},\tau_{1}]\), \(\tau_{0}<\tau_{1}\), by the following relation  
\[p_{\rm n}(\tau)=\left\{\begin{array}{ll}y_{0},&\tau_{0}\leq\tau<\frac{1}{2}( \tau_{0}+\tau_{1}),\\ y_{1},&\frac{1}{2}(\tau_{0}+\tau_{1})<\tau\leq\tau_{1}.\end{array}\right. \tag{7}\]  
Here, \(y_{0},y_{1}\in\mathbb{C}\) denote the known data samples.
# _Ku_-Band CMOS Power Amplifier with Three-Stack Power Stage to Enhance Output Power and Efficiency
## 3 Proposed Power Amplifier  
In this study, we took only the advantages of the common-source and stack structures and proposed a structure capable of improving the efficiency of the power amplifier. The driver stage of the power amplifier requires relatively low output power compared with the power stage. Therefore, in this study, the driver stage was implemented in a common-source structure with low power consumption. Through this, it was intended to increase efficiency by reducing the power consumption of the entire power amplifier. In the case of the power stage, ensuring high output power plays a major role, so it was easy to ensure output power by forming a power stage in the stack structure and using high supply voltage.  
As a result, the stack structure was applied only to the power stage, which ensures high power, and the common-source structure was applied to the driver stage to minimize power consumption. Through this, we designed a power amplifier that can improve efficiency while securing high output power.
# Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
## 5 Conclusions  
Our research introduces an optimized regression algorithm-based approach to enhance the hybrid main memory architecture, based on DRAM and PCM technologies specifically designed to accelerate graph-processing applications. By implementing a prefetching mechanism to hybrid memory systems, our proposed model efficiently places the required data into the DRAM buffer, resulting in improved average memory access latency for irregular memory requests from indirect and linked-list-based graph data structures. Conventional prefetching methods often struggle to generate appropriate prefetching candidates for the irregular memory access streams from graph processing workloads by detecting their patterns. To solve the irregularity of memory access patterns, next-lined, regular stride, and optimized linear regression prefetch engines were used to prefetch and switch appropriate prefetch engines. Based on our simulation-based evaluations, the proposed model utilizes the DRAM buffer-based DRAM-PCM hybrid main memory architecture and its optimized prefetching engine outperforms both conventional and other hybrid main memory models that are based on existing conventional prefetchers in terms of improved performance and energy efficiency.  
The proposed scheme demonstrated a 56% and 57% better execution time and energy consumption, respectively, than the conventional DRAM model. Our proposed model surpasses other advanced models with a 12% improvement in execution time and a 9% improvement in energy consumption. Furthermore, it outperforms DynamicR by 3% in execution time and 2% in energy consumption. Moreover, it was also able to improve the hit rate by 2% on average. Thus, the proposed regression algorithm can improve the execution performance for graph-processing algorithms, incorporating key big-data processing characteristics.  
Conceptualization, M.Z. and S.-D.K.; methodology, M.Z., J.-G.K. and B.B.; software, M.Z. and Q.T.; formal analysis, M.Z. and Q.T.; writing--original draft preparation, M.Z.; writing--review and editing, J.-G.K. and S.-D.K.; supervision, S.-D.K. and B.B.; project administration, J.-G.K. and S.-D.K.; funding acquisition, S.-D.K., J.-G.K. and B.B. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2022R1I1A1A0107213811), in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1I1A1A01059737), and in part by Samsung Electronics Co., Ltd (No. IO201209-07887-01).  
Not applicable.  
Data Availability Statement: Data is contained within the article. The data presented in this study are available in Section 4.  
The authors declare no conflict of interest.
# Assessing Abrasion Resistance in Concrete Pavements: A Review
## 4 Factors Affecting Abrasion Resistance
### Influence of Material Properties
#### 4.1.3 Reinforcement  
For tensile strength and crack control, continuous steel reinforcement is placed into the concrete in CRCP. The pavement's resistance to wear and the prevention of surface damage from cracking are also influenced by the positioning and dispersion of reinforcement. Fiber-reinforced concrete has gained popularity in recent years, with uses including reducing the amount of traditional steel in some applications and even for CRCPs, enhancing abrasion and impact resistance, preventing plastic and drying shrinkage cracking, and boosting tensile strength and strain capacity [45].  
A study showed that steel fibers outperform polypropylene fibers in abrasion resistance, and increasing the fiber volume slightly improves resistance by enhancing crack control. Shorter fibers generally provide better resistance due to higher compressive strength and better distribution, while longer fibers offer more significant improvements in abrasion resistance at cold temperatures [46].
###### Abstract  
The ultra-wideband (UWB) multi-input multi-output (MIMO) radar technique is playing a more and more important role in the application of through-wall detection because of its high resolution, lower antenna requirements, and efficient data capturing ability. This paper develops a novel UWB MIMO radar system using a stepped-frequency continuous-wave (SFCW) signal, which is designed to detect human targets behind the regular brick and concrete wall. In order to balance high range resolution and wall-penetration depth, a novel miniaturized Vivaldi antenna with desired bandwidth of 0.5-2.5 GHz was designed, simulated, manufactured, and successfully used in through-wall imaging. To suppress the artifacts in the focused image and reduce the computing complexity, the cross-correlation-based time domain back projection (CC-TDBP) algorithm was developed. In addition, a through-wall imaging model was established, based on which the effects of the wall on the refraction of electromagnetic (EM) waves and the reduction of velocity are compensated. Finally, different experiments were conducted for multiple stationary targets utilizing the designed radar system, and the improved BP-based algorithms are applied to focus the targets behind the wall more accurately. The reconstructed two-dimensional (2D) images illustrate that the designed MIMO radar system can successfully detect and image human targets in the air and behind the wall.  
MIMO radar; through-wall imaging; ultrawideband signal; SFCW; sparse array; back projection algorithm; Vivaldi antenna 2019
# A Blockchain-Based Supervision Data Security Sharing Framework
## 4 Performance Evaluation and Analysis of Proposed Framework
### Analysis of Security Assessment
#### 4.2.3 Data Privacy  
The proposed framework involves data uploaders encrypting and storing supervision files in the IPFS file system, uploading the file addresses returned by the IPFS file system to the blockchain system, and encrypting the keys using the CP-ABE framework. On-chain users are only visible to file addresses. The file uploader restricts the download permission of the data by specifying the policy Pa. Only data users who meet the policy _Pa_ attribute can decrypt the key, thereby decrypting the supervision file and completing data privacy protection.
# Potential of Sub-GHz Wireless for Future IoT Wearables and Design of Compact 915 MHz Antenna
## 3 System Design
### System Architecture and Electromagnetic Model  
In Figure 6, the wrist-worn wireless SpO2 sensor is depicted. From Figure 6a, which shows the exploded view of the sensor, the architecture of the whole system can be observed. In particular, it is characterized by three main units: Sensor Unit (SU), Processing Unit (PU) and Antenna Unit (AU). The SU is composed of the optical device placed in proximity of the wrist to measure the SpO2 level in the blood as explained above. The PU is placed on top of the sensor board and connected to it using a flex cable. This unit controls the SU and the information to be transmitted wirelessly. The AU allows wireless communication from the device either when it is worn on the wrist or in free-space. AU is interconnected to the PU by two standard 6-pin headers. The wrist-worn wireless SpO2 sensor is powered up by a coin cell battery placed between the PU and AU. The overall system is secured into a robust plastic enclosure and will be tied to the human wrist using straps.  
Full-Wave Electromagnetic (EM) simulations of the system have been performed using ANSYS Electronics Desktop [65] in free space and on a phantom human wrist. To perform simulations in the latter case, a heterogeneous voxel-based model of the human arm, comprising bones, muscles, vessels, blood and skin has been used. In particular, in this case the system is placed on the forearm in proximity of the wrist as depicted in Figure 6b. The material used for the electromagnetic (EM) model for the enclosure of the wrist-worn wireless SpO2 sensor is Acrylonitrile Butadiene Styrene (ABS). The AU is implemented using standard low-cost FR4 substrate with copper metallization for radiator and ground plane.  
Figure 5: Working principle of SpO2 Sensor.
# Monitoring and Calibrating Building Materials Drying Kinetics with Capacitance Sensors
## 2 Materials and Methods
### Preparation of Test Samples  
Three different types of building materials were investigated, bricks, stones, and mortars. Samples' dimensions were 4 cm \(\times\) 4 cm \(\times\) 30 cm; the 30 cm length is to accommodate the sensors' 25 cm length. More specifically, two different kinds of bricks were investigated, \(BRI\) and \(BRM\). These bricks are fired, handmade, and predomately used in historic buildings. Traditional bricks were selected on account of their appropriate size, as well as their compactness and absence of holes. The stones selected are of sedimentary type, commonly used in construction works (codes \(SRY\), \(SST\)). Regarding mortars, it was decided to prepare samples with the four different binders most frequently used, one with hydrated lime putty binder (\(ML\)), one with natural hydraulic lime (\(MHL\)) binder, one with lime powder and pozzolan (\(MLP\)), and one with cement binder (\(MC\)). The different mortar binders were chosen to be characteristic for both historical and modern structures. The samples total height was 4 cm. For bricks and stones, the sensor was placed between two samples measuring 2 cm \(\times\) 2 cm \(\times\) 30 cm, secured firmly together (Figure 1b). The sensors during the preparation of the mortars were placed at 2 cm height and width, in the middle of the sample. The different materials used in the investigation are presented in Table 1.
## 4 Experimental Methodology
### Experimental Setup  
We emulated an Earth observation data collection scenario in our lab testbed using virtual machines connected in two topological configurations: 1. Topology 2. These are shown in Figure 2. The satellite h23 is collecting Earth observation data from space and sending them to a ground station (GS) h25 on the Earth.  
We used the Linux traffic control utility NetEm to emulate longer delays, channel attenuation, and the bandwidth characteristics of the space network by configuring the delay, rate limiting, and packet loss for each test scenario differently. The network topology represents the multiple paths between satellites using ISLs and IOLs. We used the Bpgen tool to generate bundles of size 10,000 bytes and sent at rates from 1 bundle/s to 10 bundle/s, and each experiment was run for 180 s and repeated six times to have a statistical average of the network performance metrics. The sending rate implies the number of concurrent users of the system. We used the UDP protocol, since it does not support re-transmissions for lost bundles, for which we needed to calculate the packet losses strictly. We present the performance of the SNN algorithms with different optimization goals under different scenarios in the Results Section.  
Figure 2: Space-based information network of satellites and a GS. (**a**) Topology 1 (**b**) Topology 2.
# Prediction of Leaf Break Resistance of Green and Dry Alfafla Leaves by Machine Learning Methods
## 3 Results and Discussions
### Interpretation of Modeling Results of Green Alfafla
It can be said that the models established with RF, ET, and CB are more successful in the R\({}^{2}\) metric than the models established with the GB and XGB methods. Although the models established with GB, XGB and ET have similar values in the MAPE metric, the model established with the GB method is the most successful model in the MAPE metric. Figure 7 shows the relationship between the actual and predicted breaking stress values. While the x-axis in the graphs shows the observations, the y-axis shows the stress value. Red lines show the actual values, dashed blue lines show the predicted values. The first graph was produced from the model in which we obtained the leaf breaking stress for the dried alfalfa plant using the RF method. The second graph was produced from the model obtained using the ET method. In both graphs, the actual and predicted values almost overlap. This shows that the established models are successful. The metric results shown in Table 2 and the line plots shown in Figure 7 support each other. The R\({}^{2}\) and MAPE values of the model established with the RF method are 0.9499 and 0.2163, respectively. On the other hand, the R\({}^{2}\) and MAPE values of the model established with the ET method are 0.9853 and 0.0969, respectively. These results show that although both models are extremely successful in predicting leaf breaking stress, it can be said that the model established with the ET method makes a more successful prediction. Accordingly, it can be said that the metric results and graphic results support each other. In this study, the leaf breaking stress of dried and green alfalfa plants was predicted using the Extra Trees, Random Forest, Gradient Boost, Extreme Gradient Boosting and Cat Boost methods. For the dried alfalfa plant, the best R\({}^{2}\) (0.985) value was obtained from  
Figure 6: Scatterplots of machine learning models: (**a**) Random Forest and (**b**) Extra Trees. Figure 7: Line plots of the machine learning models: (**a**) Random Forest and (**b**) Extra Trees. the model established using the Extra Trees method. Similarly, the best RMSE (0.0171) and MAPE (0.0969) values were obtained from the model established with the Extra Trees method. For the green alfalfa plant, the best MAPE (0.1447) value was obtained from the model established using the Gradient Boosting method. The best RMSE (0.0616) and R\({}^{2}\) (0.96) values were obtained from the model established with the Random Forest method.
## 3 Results
### Word Similarity and Relatedness Tasks
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Spearman Correlation (\(\rho\))**} & \multicolumn{2}{c}{**std (\(\sigma\))**} \\ \hline
**Models** & **Wordsim353** & **SimLex999** & **Wordsim353** & **SimLex999** \\ \hline AmWord2 Vec & 0.518 & 0.285 & 0.247 & 0.274 \\ fastText & 0.434 & **0.314** & 0.238 & 0.245 \\ \hline AmFliar & 0.444 & 0.288 & 0.183 & 0.208 \\ MultFliarFT & 0.447 & 0.272 & 0.166 & 0.189 \\ MultFliar & 0.173 & 0.231 & 0.085 & 0.109 \\ AMRoBERTa & 0.285 & 0.202 & 0.141 & 0.133 \\ XLMR & 0.182 & 0.183 & 0.075 & 0.065 \\ \hline DeepWalk & **0.523** & 0.191 & **0.279** & **0.308** \\ Role2 Vec & 0.448 & 0.255 & 0.202 & 0.233 \\ \hline \hline \multicolumn{5}{c}{English datasets state-of-the-art} \\ \hline
0.828 [45] & 0.76 [46] & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Spearman correlation (\(\rho\)) and standard deviation (\(\sigma\) ) scores on the Amharic Wordsim353 and SimLex999 datasets. 3. **SimLex antonym annotation**: The ground truth annotation scores for the word pairs 'new' and 'ancient' as well as 'tiny' and 'huge' are near zero. However, the cosine similarity produces a negative result as the word pairs are opposite in meaning. 4. **Pre-trained vs. fine-tuned models**: Finetuning the pre-trained models results in a higher similarity (Table 5). For the word pairs 'professor' and 'cucumber' in WordSim353, the ground truth score is 0.31 (10 is a maximum score). The pre-trained 'Multi-Flair' model results in a wrong (which is higher) similarity score (0.819) but the fine-tuned model results in a smaller score (0.406) near to the ground truth score. We can also observe that the scores for SimLex99 are lower than the WordSim353 scores. The work by [PERSON] et al. [44] indicated that LexSim999 tasks are challenging for computational models to replicate as the model should capture similarity independently of relatedness association. Moreover, the results on both datasets for Amharic are lowered compared with the English datasets. This can be attributed to several reasons such as: (1) As the translations are not perfect, the ground truth annotation scores should not be used as it is for Amharic pairs, and (2) as Amharic is morphologically complex, the embeddings obtained might be different from the correct lemma of the word. Hence, the pairs should be first lemmatized to the correct dictionary entry before computing the similarity scores. Furthermore, the annotation should be done by Amharic experts to calculate the similarity and relatedness scores.
# Research Progress in the Flight Characteristics of Flyers Driven by Different Technologies
## 3 Research Progress in Flyer Velocity
### Investigation of Flyer Velocity
#### 3.1.3 Visar
The main advantage of ORVIS over VISAR is that the time resolution is mostly limited by the streak camera capacity. Furthermore, because of a continuous record of the shock velocity history, the ORVIS approach helps determine the stability of shock propagation in the sample. As illustrated in Figure 11, the laser beam from a laser passes through a collimating lens L1 to the monitored diffuse surface. The diffuse reflection is picked up and collimated by lens L1 and separated from the incoming beam by M1. The collimated return beam is reduced by the telescope and split into two equal parts by the beam splitter. After recombining the two beams at the beam splitter, a straight-line interference pattern is converted into a set of bright spots on the input slit of a streak camera. The cylindrical lens increases the intensity of interference patterns at the camera entrance slit without altering the fringe spacing. Figure 11: Interferometer configuration for ORVIS. Figure 10: Optical configuration of an AFVISAR system. [PERSON] et al. constructed an ORVIS with tens of picosecond temporal resolution and applied it to laser-driven shock experiments [35]. [PERSON] et al. developed an ORVIS to measure velocity with a time resolution of about 300 ps and employed it to measure the flyer velocity of a 25 \(\upmu\)m thickness copper foil for studying the detonation-driven shock waves [36].