esa-sceva/satcom-chunk-collection huggingface dataset:
# Better Word Representation Vectors Using Syllabic Alphabet: A Case Study of Swahili
## 6 Results and Discussion
### Our Model Results (WEFSE)
#### 6.2.1 Qualitative Analysis  
We also used the nearest neighbor test to explore the quality of the WEFSE compared to word2 vec by [PERSON] et al. [24], where we considered the cosine similarity of the Top 5 words for in-vocabulary (_wamakutana_ and _mchezo_) and nonce words (_mulluga_ and _kusorta_) in the small Swahili test file. The results in Table 7 attest to the ability of our word embeddings to associate similar word. WEFSE processed and associated words with the out-of-vocabulary (nonce) words as opposed to word2 vec which returned an error. Indeed, WEFSE associated syntactically and semantically similar words with _wamakutana_ being associated with present tense verbs while _mchezo_ with nouns. This suggests that the word representation vectors from WEFSE are superior because they exploit the composition aspect (syllables) and context of the words. The results demonstrate that words in agglutinative and syllabic-based languages are inadequately represented using word vectors. These results suggest that constructing word embeddings from syllable embeddings is useful in deep learning when training on data that can benefit from contextual information, especially on NLP tasks such as machine translation (common in automated systems using speech), sentiment analysis, parsing and question and answer systems.
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
## 3 Results and Discussion  
### Dielectric Constant Fluctuations of Different Types of Fiber Weaves  
Impedance fluctuations can be attributed to local variations in the Dk and the structural integrity of the stack-up, both of which depend on the laminate fiber weave. The dielectric constant (Dk) is a key parameter of impedance, and its fluctuation significantly affects impedance. Different types of fiber weaves show different characteristics.  
For any particular stack-up configuration, there are two worst-case effective Dk extremes that occur, the highest and the lowest Dk. When all yarn strands align with each other, and the trace lies directly over the center of a low resin, a \"glass peak\" resin, the highest Dk is gotten. When the trace lies directly over the middle of a high resin, a \"glass valley\" region, the lowest Dk is gotten. Due to the  
Figure 1: The graphic design of the Dk test.
# Low-Power Rectennas in Microwave Wireless Power Transmission
###### Abstract  
The advancement of microwave wireless power transfer technology has positioned low-power rectennas as a research hotspot. This paper systematically reviews core technological progress in low-power rectennas, focusing on innovations in rectifier circuit topologies, nonlinear device models, antenna array optimization, and efficiency enhancement strategies. Current technical bottlenecks and future application directions are analyzed, providing theoretical references for space solar power stations, IoTs, and related fields.  
rectenna; rectifier; low-power rectification; Schottky diodes; wireless power transfer (WPT) 2020 IEEE/RSJ Web Conference (CC BY) IEEE 2020
## 1 Introduction  
Aligned with advancements in the automotive industry, the railway sector is increasingly focusing on autonomous driving and predictive maintenance. A key technique is 3D reconstruction of the structural gauge, enabling tasks such as detecting vegetation encroaching on the driving corridor. Large, wooden vegetation can damage trains, while smaller, softer vegetation may obscure sensors and distort data. This challenge was addressed in the recent EU-founded research project BerDiBa (Berliner Digitaler Bahnebetrieb engl. Berlin Digital Rail Operation) [1].  
For such tasks, the camera sensor is particularly valuable due to its cost efficiency and ability to provide dense data. However, this advantage comes at the cost of dimensional reduction, as images are inherently 2D. Traditional methods to address this issue, like Structure from Motion (SfM), require multiple consecutive images and often yield sparse 3D point clouds. Advancements in machine learning have tackled this limitation by enabling pixelwise depth estimation from single images, yielding a denser and more detailed 3D point cloud. However, many of these models predict relative depth to remain independent of specific camera parameters, which limits their usability for tasks like inspecting the structural gauge that is normally defined by metric requirements. For instance, in Germany, legal regulations mandate a vegetation-free zone of 1 m (_Wachstumszuschlag_) beyond the structural gauge to reduce risks from vegetation growth.  
Our evaluation of the latest metric depth estimation model [2] revealed an average Chamfer distance of 1.94 m compared to LiDAR across all 45 scenes in the railway dataset OSDaR23 [3]. This level of accuracy fails to meet the legal requirements for detecting structural gauge clearance from vegetation. To improve accuracy, we leverage the fact that at least one track is typically visible when recording the structural gauge from the driver's perspective. The metric gauge of this track is generally well-known, as it varies only rarely between countries or continents. Building on this fact, we introduce a novel method to measure the gauge in a 3D point cloud obtained by any metric or non-metric depth estimation model (backbone). This allows us to scale the entire point cloud and significantly reduce its discrepancy.  
We extend our method to perform extrinsic calibration of the camera, which is another common issue. The position and orientation of the resulting _ego_-coordinate system align with the standard defined in the OSDaR23 dataset. This makes our method the first to perform extrinsic calibration relative to this specific coordinate system using only a single image, thereby establishing a new benchmark.  
Finally, we evaluate our method on this dataset, using LiDAR point clouds as ground truth. To the best of our knowledge, OSDaR23 is currently the only dataset suitable for this task, as it includes both camera and LiDAR data from railway contexts. The dataset is extensive, featuring 45 unique scenes with approximately 1600 images in total.
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 5 Experiment and Analysis
### Experiment Setup and Datasets  
**Implementation details.** The proposed MSNUNet is an end-to-end trainable model with no pre-trained network, implemented using PyTorch 1.8.0 and a single NVIDIA RTX 3090 GPU. Specifically, we trained the model for a total of 300,000 iterations using the Adam [41] optimizer. Each 10,000 iterations corresponded to one epoch, and the entire training process required 30 epochs in total. The exponential decay rate parameters \(\beta_{1}\), \(\beta_{2}\), and weight decay were set as 0.9, 0.9, and 0, respectively. The initial learning rate was set to \(1\times 10^{3}\), gradually decreasing to \(1\times 10^{6}\) using the cosine annealing schedule [42]. Patch training and full image testing lead to performance degradation and denoised images with patch artifacts, which we addressed using a test-time local converter [43].  
**Noise ratio.** We considered three types of mixed noise: AWGN+SPIN, AWGN+RVIN, and AWGN+RVIN+SPIN. For the first type, the standard deviation \(\sigma\) of the AWGN ranged from 20 to 30 in steps of 5, and the SPIN ratios were set to 15%, 30%, and 40%. For the second type, the \(\sigma\) of the AWGN ranged from 15 to 25 in steps of 5, and the RVIN ratio varied from \(p_{r}\) = 5% to 15% in steps of 5%. For the third type, the \(\sigma\) of the AWGN ranged from 5 to 15 in increments of 5, the RVIN ratio varied from \(p_{r}\) = 5% to 15% in increments of 5%, and the SPIN ratio varied from \(p_{sp}\) = 50% to 30% in decrements of 10%.  
**Training datasets.** We trained the proposed MSNUNet on the DIV2K [44] dataset, which consists of 800 high-quality images for the training set and 100 images for the vali dation set. These images have an average resolution of around 1920 \(\times\) 1080. Additionally, these datasets contain abundant details and intricate textures, making them well-suited for evaluating and comparing the performance of diverse image processing algorithms. The patch size and batch size were set to 256 \(\times\) 256 and 8, respectively. We added three types of mixed noise to the patches for each of the 800 training images. In addition, the same three types of mixed noise used in the training set were applied to the 100 validation images.
# SC-FDE Layer for Sensor Networks in Remote Areas Using NVIS Communications
## 5 Experimental Evaluation  
In this section, we explain the tests that have been carried out to verify the behavior of the modulation depending on the power transmission considering the transmission success.  
In this case, we wanted to evaluate a sweep of each Bit-error rate (BER) value and obtain its probability of occurrence. Moreover, to check how the channel and noise affect each modulation, many measures can be assessed, such as the Error Vector Magnitude (EVM), which measures the quality of the signals by looking at the deviation of the points from the ideal constellation due to the effects of the channel. Other alternatives include the Modulation Error Rate (MER), which is related to the EVM when average power is compared, or more commonly the BER-E\({}_{\text{b}}\)N\({}_{0}\). In our research, we analyzed the BER-E\({}_{\text{b}}\)N\({}_{0}\), a parameter that provides a quick overview of the received signal's performance.  
For the tests, we decided to first check the success rate of the SC-FDE and decided to optimize the transmission power by adopting the clipping technique. In addition, we compared the BER/E\({}_{\text{b}}\)N\({}_{0}\) with the OFDM, FSK, and QAM from the study performed in [20].
## 3 Results
### Water and Fertilizer Use Efficiency, Soil Total Enzymatic Activity, and Fertility Index  
The water and fertilizer use efficiencies of different mulching treatments on cabbage are shown in Figure 7. The water use efficiency, as well as fertilizer bias productivity of WCM treatment, was much lower than the other five mulching treatments. In this study, treatments covered with black biodegradable mulch (BBM) and permeable mulch (BPM) had the highest water utilization and fertilizer bias productivity, and they were not significantly different. The highest water and fertilizer use efficiency was found for black biodegradable mulch (BBM) and permeable mulch (BPM), followed by white biodegradable mulch (WBM), black and white combination ground mulch (WBCM) treatment, and black common mulch (BCM), and the lowest water and fertilizer use efficiency was found for common mulch (WCM) from a combination of two indicators: water use efficiency and fertilizer bias productivity. The overall fertility indices of the two common mulch soils were significantly higher than the other mulch types, and the overall enzyme activity indices of the soil covered with black and white mulch and black biodegradable mulch were significantly higher than the other mulch treatments.
## 6 Choosing the Optimal Model
### Analysis of Simulation Results
#### 6.1.1 Net2  
The analysis of the results for the Net2 model demonstrated very high effectiveness, particularly for the ADL indicator, with an accuracy of 94%. The model exhibited high precision at 92% and recall at 96%, suggesting an excellent ability to detect positive cases. [PERSON]'s Kappa value was 0.93, indicating a high level of agreement between actual results and predictions.  
Table 6 presents the percentage share of correct and incorrect outcomes obtained using the Net2 model for predicting the indicators. The Net2 model achieved its best performance for the ADL indicator, with a classification accuracy of 94.44%, while the RMA1, RMA2, and RMA3 indicators had lower effectiveness (44.44%, 44.44%, and 38.89%, respectively).  
Figure 8 presents the confusion matrices for the Net2 model, allowing for visualization of its effectiveness in distinguishing correct and incorrect classifications for each of the indicators. The analysis of the confusion matrices shows that the Net2 model achieved its best performance for the ADL indicator, with an accuracy of 94% and an F1-score of 94%. The high [PERSON]'s Kappa value (0.93) indicates that this model has very high agreement between actual results and predictions, making it highly useful in clinical practice.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMA1** & **RMA2** & **RMA3** & **ADL** \\ \hline Correct result & 44.4 & 44.4 & 38.9 & 94.4 \\ Incorrect result & 55.6 & 55.6 & 61.1 & 5.6 \\ All cases & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Prediction accuracy for the Net2 model for simulation data. The best result is marked by blue color.
## 5 Conclusions  
In order to improve the frequency resolution of hearing-impaired individuals, a piecewise-linear frequency shifting algorithm in digital hearing aids was proposed. In this algorithm, the sensitivity of the hearing-impaired person in the confused frequency range was improved by frequency stretching. The adjacent frequency ranges were compressed to avoid overlap. The comparison between the original spectrograms and the processed spectrograms demonstrated that the distortion of the spectrogram in the compressed region was low. The audition in a silent room by a normal hearing person showed that the processed words were clear. The SDS for Chinese monosyllabic words and the SRT for Chinese spondaic disyllabic words were tested for six hearing-impaired subjects with V-shaped audiograms. The results showed that better SDS and SRT were achieved in the tests using the processed vocabularies than the original ones. This concluded that the speech recognition of the subjects in the noisy environment had been effectively improved. The results of this research will assist with the much needed development and implementation of new signal processing algorithms in commercially available digital hearing aids that are able to operate in real time within 2-8 ms time delay, which will lead to new hearing aids being developed with both amplitude compression and frequency shifting capabilities.  
The work was supported by the National Natural Science Foundation of China under Grant No. 61375028, 61673108, and 61571106, Qing Lan Project of Jiangsu Province, Six Talent Peaks Project in Jiangsu Province under Grant No. 2016-DZOX-023,China Postdoctoral Science Foundation funded project under Grant No. 2016M601696, Jiangsu Planned Projects for Postdoctoral Research Funds under Grant No. 1601011B, and the open fund of Guangdong lighting and audio video engineering research center under Grant No. KF201601 and No. KF201602.  
The authors contributed equally to this work. [PERSON] drafted the main manuscript and conceived the experiments. [PERSON] assisted in the design of the experiments. [PERSON] contributed ideas, was involved in experimental discussions, and also revised the manuscript. [PERSON] designed the experiments. [PERSON] and [PERSON] performed the experiments and analyzed the data.  
The authors declare no conflict of interest.
# Analysis of Cyclone Spinning Effect with Different Guide Vane Heights
## 4 Analysis of Cyclone Spinning Effect
### Mechanical Efficiency  
The greater the intensity of the spiral flow generated by the cyclone, the greater the loss of additional mechanical energy. The ratio of the rotational kinetic energy of the water flow is defined after the action of the cyclone to the additional mechanical energy loss during the action of the cyclone, which is the mechanical efficiency of the cyclone.  
The rotational kinetic energy of the water flow through the outlet section of the cyclone per unit time is \(E_{t}\), and the additional mechanical energy loss is \(\Delta E-\Delta e\); then, the mechanical efficiency \(\eta\) of the cyclone can be expressed as follows:  
\[\eta=\frac{E_{t}}{\Delta E-\Delta e} \tag{12}\]  
The \(\eta\) of cyclones with different guide vane heights is shown in Figure 11. It can be seen from Figure 11 that (1) with the increase in guide vane height, the mechanical efficiency \(\eta\) decreases from \(\eta\) = 0.28-0.30 when the relative height of the guide vane H/R = 0.4 to \(\eta\) = 0.16-0.18 when the height of the guide vane H/R = 0.8. The results show that the mechanical efficiency is significantly affected by the height of the guide vane, and the larger the height of the guide vane, the lower the mechanical efficiency, and the smaller the rotational kinetic energy of the spiral flow generated by consuming unit additional mechanical energy. (2) When the Reynolds number Re increases, the mechanical efficiency \(\eta\) increases slightly, and when the Reynolds number Re increases from 105,419 to 210,838, the \(\eta\) increases by 0.01-0.02, and the rotational kinetic energy of the spiral flow generated by the additional mechanical energy consumed per unit increases.
# Leaching Behavior of Cesium, Strontium, Cobalt, and Europium from Immobilized Cement Matrix
## 3 Results
### Precipitation in Leachate
#### 3.2.3 Water-Soluble Ions  
During the leaching tests, the major elements (i.e., Ca, Na, K, and Si) contained in the solidified cement specimens doped with nuclides were ionized and diffused from the porewater in the specimens to the leachant. The cumulative concentrations of major elements (Ca, Na, K, and Si) and nuclides (Cs and Sr) leached from the solidified cement specimens over 90 days are shown in Figure 9. Fe and Mg were not observed in the leachate because their concentrations were below the detection limit during the test period.  
The cumulative concentrations of Cs and Sr with the square root of time are shown in Figure 9a,b, respectively. The cumulative concentration was proportional to the reaction time, suggesting that the leaching process was controlled by diffusion [6]. However, there was a difference in the temporal trends between the cumulative Cs and Sr concentrations. The cumulative concentration of Cs steadily increased with time (Figure 9a). However, the cumulative concentration of Sr significantly increased in the early period (<5 days) but only gradually increased in the later period (>5 days) (Figure 9b). These results suggest that surface wash-off and diffusion might be the dominant processes behind Sr leaching during the early and later periods, respectively. These results suggest that the precipitation of Ca(Sr)CO\({}_{3}\) previously discussed in Sections 3.2.1 and 3.2.2 affected the cumulative concentrations of Sr and Ca in the later period. However, Cs, which was not observed in the precipitates, was continuously released even after the early period.  
Figure 8: Example micro-XRF element maps of precipitates in leachates: Solidified cement specimens doped with (**a**) Cs and (**b**) Sr.  
The temporal trends in cumulative concentrations of Na and K were similar to those for the cumulative concentration of Cs (Figure 9a,c,d). The cumulative concentrations of these elements constantly increased with time. The differences in the leaching rates of alkali-metal cations (Na, K, and Cs) were attributed to their periodic properties, such as their different ionic radii and bonding strength within the cement matrix. [PERSON] et al. reported that Na has a stronger ability to transport into the surface cavities and thus reside in the silicate channel for a longer period than K due to its smaller hydration shell and stronger Na\({}^{+}\)-O\({}_{\text{s}}\) connections [6].  
In addition, the temporal trends in the cumulative concentrations of Sr and Ca were identical (Figure 9b,e). The cumulative concentrations of Sr and Ca significantly increased in the  
to the similarities in their charges and ionic radii [69; 70]. [PERSON] [71] also reported that the behavior of Sr in cement may reflect the behavior of Ca.
## 2 Analytical Fragility  
### Methodology of Fragility Assessment  
Fragility analyses, also known as fragility curves, are representations of conditional probability that indicate the probability of exceeding a pre-defined level of damage as subjected to an input seismic intensity parameter [12]. The fragility function represents the ability of a system to withstand a specified event, and can be expressed as  
\[Fragility=P[LS|IM=x], \tag{1}\]  
where \(P[LS|IM=x]\) represents the probability that a ground motion with \(IM=x\) causes the structure to collapse with a given pre-defined limit state (\(LS\)). \(IM\) can be expressed in terms of the peak ground acceleration/velocity/displacement or spectral acceleration at the fundamental period [9]. There are four common methods for fragility assessment: (1) professional judgement; (2) quasi-static and design code consistent analysis; (3) damage data associated with past earthquakes (the so-called empirical fragility analysis); and (4) numerical simulation of seismic responses based on structural dynamic analysis or the so-called analytical fragility analysis [22]. In the absence of adequate damage data, the empirical fragility curve will be inaccurate. Thus, an analytical fragility analysis in which the limit states are defined based on ASCE/SEI 41 [20] for steel building structure can be a suitable tool for assessing a container crane. The definition of limit states is discussed in detail in Section 2.3.  
Increasing \(IM\) levels were generated to cover the entire range of each fragility curve. For each \(IM\) level, 20 ground motions created by matching the \(IM\) level with the design response spectra of the Korean Building Code (KBC) [21; 23] were used as input parameters for the FE model. Time-history analyses were conducted to obtain the critical portal drift. Figure 2 illustrates the framework of this study.  
Figure 2: Methodology of fragility curve development.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 6 Performance Evaluation
### RSU Allocation
#### 6.2.2 Impact of Traffic Condition  
To analyze the impact of vehicle distribution, we will compare the connectivity of these strategies under the vehicle distribution variation. Here, we have the number of vehicle expectation \(\mu=100\) with variance \(\sigma\) from 0 to 30 in Figure 10. When \(\sigma=0\), the traffic flow is uniformly distributed among all the regions. In this situation, the performance gap among RSU allocation strategies is minor. As the variance increases to \(\sigma=10\) and \(\sigma=15\), we can see that both our proposed strategy and Top-K strategy have greatly improved connectivity, and our proposed strategy obviously has better performance. Meanwhile, the performance of the uniform distribution strategy deteriorates as the variation increases. Furthermore, as the variance continues to increase at \(\sigma=20\) and \(\sigma=30\), the connectivity begins to converge in this period, and the performance gain between our proposed strategy and Top-K strategy is minor. We can conjecture that when the traffic density is low and uniform, both our proposed bidirectional allocation strategy and Top-K strategy would make similar decisions to allocate the RSUs in the region with higher vehicle density.  
Figure 9: Connectivity under different allocation strategies. (**a**) number of RSUs \(m=10\); (**b**) number of RSUs \(m=20\); (**c**) number of RSUs \(m=40\).