esa-sceva/satcom-chunk-collection huggingface dataset:
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 5 Conclusions and Outlook  
In this paper, we have reviewed optical frequency combs and their use as light sources for spectral superchannels. We have described several methods for utilizing the phase coherence of the subchannels provided by frequency combs, both analog methods for comb regeneration and digital joint-DSP methods. In addition, we have discussed practical challenges when implementing comb-based superchannels, both general and specific to experiments. The work summarized in this paper shows the potential for both performance improvements and power consumption savings made possible by joint processing in comb-based fiber-optical systems. In addition, the phase-locked carriers from an optical frequency comb also reduce the need for guard bands between subchannels and thus enables an increased spectral efficiency compared to using free-running lasers.  
Applications where these approaches could be advantageous include short-reach links with higher order modulation formats where the transmission distance is limited, since in this case, the phase estimation part of the DSP plays a bigger role in the power consumption. Joint processing also has the potential to lessen the linewidth requirements on integrated comb sources, which might pave the way for their usage in compact superchannel transceivers.
## 4 Experiments
### Datasets and Evaluation  
We conduct our experiments against two benchmarks: IWSLT-8 and OPUS-100. For IWSLT-8, we collect eight English-centric language pairs (including English to/from Farsi, Arabic, Hebrew, Dutch, German, Italian, Spanish and Polish) from the IWSLT2014 ([[https://wit3.fbk.eu/2014-01](https://wit3.fbk.eu/2014-01)]([https://wit3.fbk.eu/2014-01](https://wit3.fbk.eu/2014-01)) (accessed on 1 March 2023)) following [PERSON] et al. [42], resulting in a total of 2.7 M sentence pairs, as illustrated in Table 2. For the OPUS-100, the dataset, created by [PERSON] et al. [21], consists of 110 M sentence pairs and covers 100 languages (198 language pairs, either into or from English), whose size ranges from 0.6 k to 1 M.  
At the pre-processing stage, we employ the langdetect library ([[https://github.com/Mimino666/langdetect](https://github.com/Mimino666/langdetect)]([https://github.com/Mimino666/langdetect](https://github.com/Mimino666/langdetect)) (accessed on 1 March 2023)) to retain sentences predicted as the desired language, remove sentences longer than 250 tokens and with a source/target length  
Figure 3: One update in the augmentation and substitution fine-tuning stages. The dash arrows in the augmentation step mean that CLOS(HS) and CLOS(SS) are only optional in the substitution fine-tuning stage.  
ratio exceeding three. Then subwords are generated via byte pair encoding (BPE) [43] with 30 k merged operations for IWSLT-8 and 64 k merged operations for OPUS-100. We train many-to-many MNMT systems throughout our experiments, by adding corresponding language tokens at the beginning of the source and target sentences.  
We adopt case-sensitive BLEU [44] for the translation evaluation with the toolkit SacreBLEU (Signature: BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a+version.1.4.1) [45] for its ease of use and is independent of language. We also use METEOR [46] and TER [47] to test the translation performance, as METEOR correlates well with human evaluation compared to BLEU, and TER is very intuitive. All statistical results were computed via paired bootstrap resampling [48]. In the IWSLT-8 experiments, we use tst2013 and tst2014 as the development and test sets for eight English-centric language pairs, respectively. For non-English directions, we extract the test set from the FLORES-101 benchmark [49], which is widely used in multilingual translation evaluation. In the OPUS-100 experiments, all the development and test sets are public and available ([[https://opus.nlpl.eu/opus-100.php](https://opus.nlpl.eu/opus-100.php)]([https://opus.nlpl.eu/opus-100.php](https://opus.nlpl.eu/opus-100.php)) (accessed on 1 March 2023)), including all English-centric supervised language pairs and 15 non-English zero-shot language pairs of Arabic, Chinese, Dutch, French, German, and Russian. Rather than providing results for each language pair, we report the supervised and zero-shot average results, respectively, due to the large number of translation directions.
## 2 Principle  
Considering that VLC transmission systems typically transmit sequential data, this paper proposes the use of three algorithms--Transformer, Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (BiGRU)--to construct differentiable channel models for end-to-end systems. These algorithms are well suited for handling sequential data. However, existing algorithm structures cannot be directly applied to VLC channels, and there is currently no research on channel modeling for end-to-end VLC systems using OFDM. Therefore, we need to design specific neural network structures and parameters tailored to fit the DCO-OFDM VLC channel. By leveraging the strengths of these algorithms, which are adept at processing sequential data, we can develop channel models that are capable of being updated through backpropagation. This capability is crucial for integrating these models into end-to-end systems and achieving joint optimization of the entire communication process. Figure 2 shows the structural principles of the three algorithms.  
Figure 1: Illustrates various configurations of VLC transmission systems and end-to-end system frameworks: (**a**) DCO-OFDM-based VLC transmission system. (**b**) Block diagram of an end-to-end system based on traditional numerical analysis channel modeling. (**c**) Block diagram of an end-to-end system based on DL technology channel modeling.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Melt Pool Defect Modeling
#### 3.1.1 Thermo-Mechanical Simulations of LPBF Defects Using FEM
A melt pool computational code with adaptive meshing called LPBFSim was created [53]. The code was tested with various process parameters for single and multiple tracks and complex part-level models.
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 4 Discussion
### Soil Moisture  
Soil moisture retrieval methods from single polarisation, active, spaceborne, microwave observations were described by [PERSON] et al. in [56]. [PERSON] et al. [57] applied ENVISAT ASAR data for soil moisture retrieval over bare soils. The research on application of radar data for soil moisture assessment has been published for inland wetlands at High Latitudes applying C-band SAR (Synthetic Aperture Radar) data--[PERSON] et al. in [58], [PERSON] et al. in [59], and [PERSON] et al. in [60]. They stated that C-band backscatter may be enhanced by inundation or high levels of soil moisture, what has been presented in our study. [PERSON] et al. applied ENVISAT ASAR [35] and Sentinel-1 [61] images for soil moisture assessment over vegetated areas. [PERSON] and [PERSON] [62] conducted a comprehensive literature review of SAR soil moisture retrieval methodology, its limitations and potential. [PERSON] et al. [63] presented the potential for surface soil moisture retrieval via the Change Detection method using Sentinel-1 data. [PERSON] et al. [64] presented the evaluation of CCI (Climate Change Initiative) soil moisture product using ground based observations. Soil moisture for the Biebraz Wetland has been investigated by authors in a previous study using microwave data acquired by ERS-1/2 SAR VV, ENVISAT ASAR IS2 HH, and ALOS PALSAR HH [25; 43; 44; 65; 66]. In the present study Sentinel-1 IW VV and VH data have been applied for the assessment of soil moisture (there was no S1 acquisition in HH polarisation). The best results have been found applying VH polarization. In general, the results are consistent with the literature, however most of the research presented in the literature was done for other than wetlands areas. The models developed by the authors will be validated and corrected using S1 and in-situ data acquired in the year 2016.  
For soil moisture change detection, the image differencing method was used. Differencing involved the subtraction of backscattering coefficient values between two different date images.  
Figure 14 presents a soil moisture difference map created by applying data from the dates: 10 July 2015 and 11 June 2015. The advantage of this technique is that, in cases where vegetation surface roughness doesn't change in time, the difference in backscatter between two dates can be related solely to a change in the soil moisture content. Soil moisture and vegetation represented by LAI were observed as having influence on the value of \(\sigma^{\circ}\) calculated from Sentinel-1 VH data. Soil moisture is in simple correlation with \(\sigma^{\circ}\), but the dependence of LAI has a polynomial character. The dependence of LAI on backscatter is associated with low or high soil moisture. That's why the research on the influence of soil moisture and vegetation cover should be continued under different soil-vegetation conditions.
###### Abstract  
Performing the high-precision Global Navigation Satellite System (GNSS) applications with low-cost antennas is an up-and-coming research field. However, the antenna-induced phase biases, i.e., phase center corrections (PCCs), of the low-cost antennas can be up to centimeters and need to be calibrated in advance. The relative field antenna calibration method is easy to conduct, but the classical procedure entails integer ambiguity resolution, which may face the problem of low success rate under the centimeter-level PCCs. In this contribution, we designed a relative field calibration method suitable for the low-cost GNSS antennas. The triple-differencing operations were utilized to eliminate the carrier-phase ambiguities and then construct PCC measurements; the time-differencing interval was set to a relatively long time span, such as one hour, and the reference satellite was selected according to the angular distance it passed over during a time-differencing interval. To reduce the effect of significant triple-differencing noise, a weight setting method based on the area of a spherical quadrilateral was proposed for the spherical harmonics fitting process. The duration of the data collection with respect to GPS and BDS was discussed. The performance of the proposed method was assessed with real GPS and BDS observations and a variety of simulated phase patterns, showing that calibration results could be obtained with millimeter-level accuracy. The impact of cycle slip and elevation mask angle on the calibration results was also analyzed.  
GNSS; low-cost antenna; antenna calibration; relative field calibration; triple-differenced measurement 2023
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 1 Introduction
This need stems from the unpredictable nature of the wireless environment, which unlike its wired counterpart is more prone to link failures due to dead sensor nodes. A typical scenario  
Figure 2: WSN Topologies. where our approach can be of particular interest is if we consider a monitoring application targeted at the structural integrity of buildings, with WSN nodes deployed for data collection purposes over key city constructions such as roads, buildings, and bridges. When one critical event is detected (e.g., a dangerous flexure of a column), the monitoring application should trigger an alert to be delivered immediately to the WSN's data collection points (base station). In this case, it is particularly important to increase resiliency against link failures and ensure a long lifetime, which is what the SCMC technique presented in this paper achieves. The remainder of the paper is organized as follows: Section 2 presents related work, Section 3 presents the approach, Section 4 describes simulation results, and Section 5 concludes the paper.
###### Abstract  
A polyacrylamide-based molecularly imprinted hydrogel (MIH) doped with positively charged gold nanoparticles (Au NPs) has been synthesized via a free radical polymerization of acrylamide (AM) aqueous solution containing positively charged Au NPs as a Raman active substrate, New Red colorant as a template molecule, \(N\),\(N^{*}\)-methylenebis(acrylamide) as a crosslinking agent, and potassium persulfate as an initiator. The Au NPs-doped MIHs were subsequently explored as a Raman active substrate for the sensitive and selective detection of New Red colorant via surface-enhanced Raman spectroscopy (SERS). The logarithmic intensity of the characteristic peak of New Red at 1572 cm\({}^{-1}\) was proportional to the logarithmic concentration of New Red with a detection linear range of \(1.64\times 10^{-6}\) to \(1.64\times 10^{-4}\) M and a limit of detection (LOD) of \(1.64\times 10^{-7}\) M. The recoveries ranged from 86.3% to 100.6% with a relative standard deviation (RSD) in the range of 2.3% to 7.7%. The RSD and recovery rates for the detection of New Red spiked in a sports drink sample were 1.8% to 7.7% and 91.0% to 97.1%, respectively. These results showed that SERS combined with MIHs as Raman active substrates could provide a sensitive, selective, and effective approach for the detection of the New Red colorant in beverage matrix.  
molecularly imprinted hydrogels; surface enhanced Raman spectroscopy; detection; New Red; positively charged Au nanoparticles +
Footnote †: journal: Applied Sciences
## 2 Descriptions of Multiobjective DWTA Problems
### Objective Functions of the DWTA Model  
#### 2.3.1 Expected Damage of Targets  
The first objective is very common in WTA--that is, maximizing the damage to hostile targets, which is also the most basic operational intention of WTA. The expected damage formula at stage \(t\) is as follows:  
\[D_{t}\big{(}X^{t}\big{)}=\sum_{j=1}^{T(t)}v_{j}(1-\prod_{s=t}^{S}\prod_{i=1}^{ W(t)}\big{(}1-p_{ij}(s)\big{)}^{x_{ij}(t)}) \tag{3}\]  
where \(t\) and \(s\) are parameters related to the time window, \(W(t)\) and \(T(t)\) represent the remaining number of weapons and targets in stage \(t\), (\(W(1)=W\), \(T(1)=T\)) respectively. \(v_{j}\) is the threat value of target \(j\). \(p_{ij}(s)\) means the kill probability of weapon \(i\) destroys target \(j\) in stage \(s\), and the kill probability matrix is \(P(s)=\big{[}p_{ij}(s)\big{]}_{T\times W}\). \(X^{t}=[X_{t},\ X_{t+1},\cdots,X_{s}]\) with \(X_{t}=\big{[}x_{ij}(t)\big{]}_{W\times T}\) is the decision matrix at stage \(t\), and \(x_{ij}(t)\) is a binary variable, which is represented by  
\[x_{ij}(t)=\left\{\begin{array}{ll}1,\ \text{if weapon $i$ is assigned to target $j$}\\ 0,\ \text{otherwise}\end{array}\right. \tag{4}\]
## 2 Data Preparation  
Reviewing the published studies on the rock EM prediction, the used rock samples are generally different, resulting in a loss of accuracy in predicting other rock sample properties from original empirical formulas or other models. To overpass this limitation, an integrated rock database consisting of 120 rock samples (e.g., granite and traverine) was established using the data-driven technology. The EM values of these rock samples was investigated by [PERSON] et al. [3], [PERSON] et al. [21] and [PERSON] [52]. The reason for choosing these data is that the EM value of each rock is determined jointly by the porosity (Pn), P-wave velocity (Vp), Schmidt hammer rebound number (SHRN) and point load index (PLI). Before generating the prediction models, a correlation analysis needs to be conducted by outputting the correlation coefficient (CC) to determine the final variables used in the EM prediction. As illustrated in Figure 1, the scatter plot at the lower right shows the distribution of the four input variables and of one output variable. The histograms in the diagonal showed the data range of all variables, and the number at the upper left represents the CC between the two corresponding variables. If the number value is negative, the correlation between the two variables is negative. Otherwise, the correlation is positive. The absolute number value is used to evaluate the correlation between any of two variables. If the CC between two input parameters (or between each input and output parameter) is very high (or very low), one of them needs to be removed to increase the prediction efficiency. The results showed that the CC between the four variables is not high; Pn and EM have the highest CC value (\(-0.651\)). Especially, the low correlation between PLI and EM is caused by the diverse sources of databases used to predict EM in this paper, while the PLI is beneficial to accurately predict the rock EM [3]. Therefore, the Pn, Vp, SHRN and PLI are used as input variables to predict the EM (output variable). Their detailed information is shown in Table 1.  
Figure 1: Correlation analysis results of four input variables and EM.
## 3 Results
### Properties of Individual Proteins in Bulk Solution  
Zeta potential measurements, as well as protein size determination, were carried out on single protein solutions using different initial protein concentration (0.5, 1, 2 g:L-1). This kind of characterization was carried out in order to find differences between the two proteins, in terms of aggregation state and charge density, in conditions in which they both bore positive charge as the membrane. In particular, pH around 3 was analyzed, since both proteins (IP: 4.4 and 5.4, respectively) are positively charged as the membrane; this will promote low membrane/proteins interaction and then irreversible fouling prevention. In Figure 2, the trend of zeta potential of the two proteins varying the pH around 3 and by using a concentration of 1 g\(\cdot\)L\({}^{-1}\) was reported as an example, since a similar trend for the other two concentration was obtained.  
BLG is positively charged and did not change its value of zeta potential for all the analyzed pH values (16 mV) and initial protein concentration tested (Figure 2). On the contrary, although ALA bore always positive charge, its zeta potential at pH 3 was 63% lower (10 mV) compared to that for BLG at pH 3 (16 mV), and it dropped further at pH 3.17. A further decrease of ALA zeta potential at around 3.2 was observed, reaching about 50% of BLG value (8 mV) from 3.25-3.50. In Table 1, proteins' size and molecular weight were reported at pH 3.0, 3.2, and 3.4. At these pH values, the difference in zeta potential between the two proteins is most representative. As it is possible to see, ALA is present as a monodisperse monomer at all the pH values analyzed, while BLG is present as monodisperse monomer at pH 3, as a monodisperse monomer and dimer at pH 3.2, and as polydisperse monomer and dimer at pH 3.4. The higher polydispersity in the last case is a clear demonstration of the increase of protein aggregation state, which means a higher presence of dimers [31]. Comparing the results between the two proteins (Figure 2 and Table 1), at pH 3, both proteins are present as monomer and have about 16 and 10 mV of zeta potential, respectively; while at pH 3.4, ALA is still present as monomer, while BLG is present as polydisperse dimer solution. In addition, in this last case, both proteins are positively charged but BLG showed a higher charge density (16 mV) with respect to ALA (8 mV) and to the situation observed at pH 3. So, in order to study the effect of protein aggregation state and charge on the UF separation performance, these two values of pH were considered for further investigation.  
Figure 2: Zeta potential measurement of pure BLG and ALA solutions within pH range 3.0–3.5: ionic strength 0.1 M.
# Recommendation System for a Delivery Food Application Based on Number of Orders
## 5 Experiments and Results
### Analysis of Real Application  
The main advantage of this proposal is that it could be applied in a real food-delivery application. Unlike other restaurant-recommendation systems, whose input data are ratings or reviews obtained from surveys [15; 18; 25; 29] or platforms such as TripAdvisor, Zomato, and Yield [14; 16; 19; 20; 21; 22], our proposal directly uses the number of orders that is stored in the application daily. This allows us to have the training data available all the time, and no questionnaires or ratings are needed from customers. To the best of our knowledge, this is the first time a recommendation system for a delivery food application is based on the number of orders.  
The model's training could be executed periodically, e.g., weekly. It consists of calculating customers' preferred restaurants and the nearest clients. This is commonly executed in a server and does not affect the application's functioning. Once we have the model, the restaurant recommendation is calculated almost instantly. In this sense, it is cost-efficient because the profits of successful restaurant recommendations can absorb the implementation cost.
# Multispectral Fusion Approach for Traffic Target Detection in Bad Weather
## 2 Related Works
### Computer Vision for Traffic Surveillance Systems  
With the development of automatic driving and intelligent transportation technology, traffic surveillance has become increasingly important, with the advantages of being unmanned and highly efficient. At present, traffic target recognition based on RCNNs has become the mainstream, and the several abovementioned kinds of detector have been applied in the field of traffic identification. As for the actual application effect [6], the single-stage detectors (YOLO, SSD, etc.) identify quickly but with low accuracy. Moreover, YOLO has trouble identifying small goals. On the contrary, two-stage detectors (faster RCNN, etc.) possess higher accuracy but are also slower.  
Another application of traffic surveillance is multi-target tracking (MOT), which is used for tracking multiple moving targets at the same time. The core of detection tracking is to correctly associate detection boundary boxes between video frames. In most cases, the correlation measures are based on appearance similarity and action consistency. In terms of association strategy, it can be divided into offline global optimization and online association. The offline global methods always use network flows [18] or probabilistic graph models [19] to solve MOT problems. This method has a strong tracking ability for targets that have been blocked by other objects for a long time. Unlike the offline global approach, the online approach [20; 21] is frame based and focuses on establishing the right connection between each pair of frames, which makes it less time consuming. At present, a majority of online tracking methods have a strong anti-interference ability towards false detection signals, short-term block and lost tracing objects. But these methods do not work well for objects that have been blocked by other objects for a long time.
## 3 Results and Discussion
### Hydrogen Production Performance of C. populeti FZ10
#### 3.2.1 Hydrogen Production Using Defined Carbohydrates
A similar trend was observed between cell protein and cellulase activity, where the maximum cellulase activity of 0.57 \(\pm\) 0.01 U/mL occurred with a cultivate time of 55 h. Consistent with the increase in cellulase activity, the cellulose promptly hydrolyzed after 20 h of incubation, and then remained at a constant level at 65-80 h. At the end of fermentation, 72 \(\pm\) 2.4% of cellulose was degraded by _C. populeti_ FZ10. As with the degradation of MCC, both butyrate and acetate were the main metabolites (Figure 4c), indicating that the hydrogen production by _C. populeti_ FZ10 from MCC was a butyrate-type fermentation. Several studies have found that butyrate-type fermentation is favorable for efficient hydrogen production; the high hydrogen yield is usually associated with a high production of butyrate and acetate during the fermentation process [16].