esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Discussions
### Results Discussion  
In this study, we investigate the performance of the proposed JPCRA scheme from two aspects. Firstly, the impact of parameter settings on the max-min achievable rate \(R_{\min}\) is investigated. We verify that the performance growth of \(R_{\min}\) is curved by the interferencethreshold of PU and the maximum transmit power constraint that echoes the cognitive two-way relay network should guarantee the performance of the primary network primarily. Then, the performance of the JPCRA scheme is compared with the MASR scheme, showing that the JPCRA scheme is preferable to enhance the poor channel quality. Last but not least, the superiority of the JPCRA scheme is verified compared with three traditional optimization schemes (FT-JoPSPA, FPS-JoTPA, FPA-JoTPS), which shows that the proposed JPCRA scheme achieves high performance in the whole range of adjustable SNR (changing \(Q\), or \(P_{1,\max}\), or \(\sigma_{u}^{2}\)).
## 2 The Sentinel-2 Mission  
Sentinel-2 (S2) is a satellite mission part of the European Commission's _Copernicus_ programme, with the goal of monitoring vegetation, soil and inland and coastal water areas for supporting agro-ecosystems applications [3]. Developed by the European Space Agency (ESA), S2 mission consists of a constellation of two satellites (S2A and S2B) that enables a global revisit time below 5 days. S2's optical instrument-the MultiSpectral Instrument (MSI)-covers a wide swath (290 km) with high spatial resolution (10-60 m) in 13 spectral bands from the visible and NIR (VNIR) to SWIR spectral range. Further mission technical characteristics are summarized in Table 1 and Figure 1 for band configuration.  
The S2 MSI data is freely available from Copernicus Open Access Hub. From mid 2018 onwards, two reflectance products are provided: L1C and L2A. The L1C product refers TOA reflectances (i.e., TOA radiance normalized by incident solar irradiance). The L2A product refers to bottom-of-atmosphere (BOA) reflectance, which is achieved by means of the Sen2 Cor atmospheric correction scheme (version 2.4.1) [5]. Sen2 Cor processing scheme is based on state-of-the-art algorithms that include cirrus cloud correction and scene classification [43; 44]. Sen2 Cor relies on the Dark Dense Vegetation algorithm for the retrieval of aerosol type (rural/continental by default) and optical thickness value at 550 nm (AOT\({}_{550}\)) [45]. The Atmospheric Pre-corrected Differential Absorption (APDA) algorithm is implemented for the retrieval of columnar water vapor (CUV) [46]. Derivation of surface reflectance is achieved from the atmospheric inversion of a set of look-up tables generated with the libRadtran atmospheric RTM [47]. Sen2 Cor achieves uncertainties around 0.03 for the AOT\({}_{550}\) and 0.3 g\(\cdot\)cm\({}^{-2}\) for the CWV, which are propagated to absolute errors of \(<\)0.05 in surface reflectance [48; 49; 50]. These errors, nevertheless, should not hamper the retrieval of biophysical variables from L2A reflectance data, e.g., as successfully demonstrated by [51; 52].
## 5 Discussion
#### Limitations and future research  
This study showed that VCs derived from mushroom FBs may promote plant growth. Commercial mushroom FBs may have different VCs and VCs amounts, when different companies are suppliers, because they use different substrate compositions and strains. Even in such cases, this protocol can provide a certain assessment of the effect on the plant. Future research could also address this by monitoring VC emissions qualitatively and quantitatively by GC-TOF-MS and other methods, as well as by monitoring gene expression and metabolic profiling on the plant side.  
Additionally, the role of minor volatile compounds remains unclear. While key compounds like 1-octen-3-one and 3-octanol were identified using HS-SPME-GC-TOF-MS, secondary volatiles might not have been fully characterized. Advanced techniques such as GC\(\times\)GC-TOF-MS and metabolic profiling could help determine their contributions. Improving VC delivery systems is also crucial.
## 2 Experimental Research
### Acquisition of Signals  
To facilitate the diagnosis of piston rod crack propagation, the experiment used the monitoring of the AE signal from the crosshead and TDC signal. To continuously monitor the AE signal at the crosshead position, an AE transducer was mounted on the side of the crosshead, as shown in Figure 6. This placement was necessitated by the limited space within the crosshead chute. Before the sensor installation, the surface was ground to remove any existing paint. In addition, to ensure the durability and longevity of the sensor, the exterior was shielded with aluminium foil tape for electromagnetic protection and coated with silicone to provide a barrier against oil exposure. Following the results of prior research, it has been observed that, when fatigue cracks propagate within metal plates, the AE waveforms exhibit distinct frequency peaks lying between 70 and 300 kHz [50; 51; 52]. Consequently, considering the principles of the sampling theorem, as well as the sampling frequency parameter employed in preceding crack surveillance investigations, it was determined that an optimal sampling frequency of 1 MHz was chosen to record the AE data [46].  
An optical sensor was mounted on the compressor frame to establish the correlation between the piston position and the angle of rotation of the crankshaft and flywheel, as shown in Figure 7. The TDC signal was picked up by the sensor and approximated by a specially designed nut mounted on the flywheel. When the TDC was reached during the working cycle, an impulse signal was generated which set the crank angle to 0\({}^{\circ}\). The presence of a flywheel in the reciprocating compressor was assumed to maintain a constant angular velocity of the crankshaft and increase its moment of inertia. The remote optical sensor had an accuracy of less than \(\pm\)0.2%.
# Analytical TCP Model for Millimeter-Wave 5G NR Systems in Dynamic Human Body Blockage Environment
## 3 TCP in Dynamic Blockage Environments
### Modeling Assumptions  
The intrinsic TCP behavior is determined by the bandwidth-delay product (BDP) of a connection. When the buffer size at NR BS is larger than BDP, TCP is capable of fully exploiting the available bandwidth [16]. However, owing to the extremely large bandwidth and typical RTT in the range of 30-200 ms [17], BDP in 5G NR systems is expected to be large. Furthermore and as shown in [11], operating with large buffer sizes may lead to the bufferbloat effects, negatively affecting latency at the air interface. When the buffer size is negligible compared to the BDP, TCP congestion control leads to \"saw-tooth\" behavior (see Figure 1) affected by the RTT \(D\), where the slope of the rate increment is \(\alpha=1/D\). When blocked and non-blocked durations \(T_{0}\) and \(T_{1}\) are long enough and/or the RTT is rather small (depending on, e.g., the environment), the TCP source can almost achieve its maximum ergodic rate [18] of 3/4 of the link rate averaged over blocked and non-blocked intervals.  
Based on the abovementioned discussion, in what follows, we take the following critical assumptions: (i) negligible buffer size compared to the BDP, (ii) blockage events causing bandwidth drops but no complete outage, and (iii) constant bandwidth available to the TCP session while in the blockage and non-blockage states. We assess the accuracy of the model in Section 4.  
The no-outage assumption may narrow the applicability of the model. If an outage actually occurs during a blockage event, the TCP behavior is also affected by timeouts when the outage interval duration is higher than the RTT time. This should almost always lead to an outage for the practical values of RTTs. The reason is that TCP utilizes the exponentially-weighted moving average (EWMA) smoothing algorithm for setting the value of timeout, as discussed in detail in [19], while the outage intervals caused by a blockage event are on order or 300-1000 ms and higher for a practical density of blockers in the environment, as highlighted in [20].  
When either \(T_{0}\) or \(T_{1}\) are small and/or the RTT is large, TCP throughput may deviate from its maximum rate. While the connection properties define the RTT, \(T_{0}\) and \(T_{1}\) are affected by the environmental conditions, chiefly by the density and speed of blockers [20]. Below, we first characterize the TCP throughput under general \(T_{0}\) and \(T_{1}\), and then provide estimates of \(T_{0}\) and \(T_{1}\) as functions of propagation and environmental conditions.
Moreover, wavelet transform remains weak in the selection of an appropriate mother wavelet, decomposition level, and respective frequency band, which is necessary information for fault analysis and detection [6; 10]. Again, the maxima of these methods are troublesome as they require features such as the mean, median, minimum, maximum, peak-to-peak, kurtosis, skewness, standard deviation, absolute mean, and root-mean-square (RMS) value for describing the actual bearing condition [11]. One finds it difficult to choose the exact features for analyzing the particular signal used in the classification [12]. Therefore, many ML/DL-based algorithms have been used ubiquitously for the ease of selecting the exceptional patterns present in the data, which are challenging for a human being to identify. Machine learning, which is a subfield of artificial intelligence, can generate insights in data, even if they are not specifically instructed regarding what to search for in the data [13]. Many ML-based methods have been proposed and implemented to develop a knowledge-based architecture for the prior diagnosis of bearing faults to prevent catastrophic failure and reduce operating costs. ML-based algorithms such as artificial neural networks, principal component analysis, support vector machines, k-Nearest Neighbors, and singular value decomposition are broadly used in bearing fault detection and diagnosis. A comprehensive review of such approaches can be found in [14]. However, the problem-solving approach of ML is not satisfactory. ML algorithms first divide the problem statement into different parts and then combine the result. Alternatively, deep learning algorithms are widely accepted and implemented. Deep learning is a subfield of machine learning that defines both higher- and lower-level categories with greater accuracy. Deep learning techniques provide better efficiency and accuracy [15]. The efficient working ability of these algorithms with a huge amount of data, their end-to-end problem-solving approach, and the pleasing result has attracted many scholars [16]. DL-based algorithms are ubiquitously used in almost every field.
# Development of Anti-OSCAR Antibodies for the Treatment of Osteoarthritis
## 3 Results
### In Vitro Functional Characterization of Anti-OSCAR Antibodies  
Our previous study showed that chondrocytes express OSCAR when they are treated with collagen [14]. Moreover, such OSCAR expression induces the chondrocytes to express OA-associated ECM-degrading enzymes such as MMP3 and MMP13. This phenomenon was used to identify which of the seven anti-OSCAR antibody clones could inhibit the pro-catabolic activities of OSCAR. Thus, the murine ATDC5 cell line, which differentiates into chondrocytes in vitro [34; 35], was cultured with each antibody and then treated with collagen. ATDC5 expression of _Oscar_, _Mmp3_, and _Mmp13_ was then examined by quantitative RT-PCR. The expression of _Epus1_ was also assessed: _Epus1_ (endothelial PAS domain-containing protein 1) encodes hypoxia-inducible factor-1\(\alpha\), which is known to upregulate MMP expression in OA (Figure 1B). The positive control inhibitor used in this experiment was hOSCAR-Fc, which served as a decoy receptor. Compared to the isotype control antibody (hIgG1), hOSCAR-Fc and five of the seven anti-OSCAR antibodies (B4, B12, C11, D2, and D11) blocked the collagen-induced downstream signaling activity of OSCAR in ATDC5 cells. By contrast, the A1 and H10 antibodies enhanced OSCAR expression and activity and were consequently excluded from the study (Figure 1C). We also excluded the B12 and C11 antibodies because of their low expression. The D2 antibody was also excluded due to stability issues that caused antibody aggregation upon storage at 4\({}^{\circ}\)C for several days. Consequently, only the B4 and D11 antibodies were selected for testing in further experiments.  
To confirm that B4 and D11 can inhibit the OSCAR functions in chondrocytes, we exploited another recent finding. Specifically, when primary articular chondrocytes are cultured with the OSC peptide, which bears the minimal triple helix motif recognized by OSCAR [19], and then with IL-1\(\beta\), they start expressing not only Oscar but also Tnfsf10. The latter gene encodes TRAIL, and its upregulation by OSC promotes chondrocyte apoptosis [14]. Thus, primary murine chondrocytes were cultured with OSC or the GPP10 control peptide and either B4, D11, hOSCAR-Fc, or hIgG, followed by IL-1\(\beta\). Indeed, OSC + IL-1\(\beta\) increased OSCAR and TRAIL expression, and this was effectively blocked by B4, D11, and OSCAR-Fc (Supplementary Figure S1A).  
OSCAR is best known for its ability to act as a co-stimulatory receptor of osteoclastogenesis [16; 20]. Thus, to further confirm that B4 and D11 can inhibit OSCAR functions, we also tested them in the osteoclast differentiation assay. This assay involves co-culturing osteoclast progenitor cells with primary osteoblasts, which induces the progenitors to differentiate into mature osteoclasts [28]. The hOSCAR-Fc positive control, B4, and D11 all inhibited osteoclast differentiation in a dose-dependent manner (Supplementary Figure S1B,C).
# A Mongolian-Chinese Neural Machine Translation Method Based on Semantic-Context Data Augmentation
## 1 Introduction
In MNMT, some simple DA techniques, such as insertion, deletion, modification, and other methods to expand the parallel corpus, often result in a large semantic difference between the generated enhanced sentences and the original sentences, and the diversity of the generated training samples is limited. [PERSON] et al. [21] adopted a new preprocessing technique of generative adversarial networks and mixed morphological noise for the problem of unknown words in Mongolian-Chinese NMT. [PERSON] et al. [22] proposed a word segmentation method based on BERT DA, significantly improving the performance of Mongolian-Chinese NMT. Although some studies have been devoted to improving the quality of Mongolian-Chinese NMT, there are few studies on the DA of Mongolian-Chinese materials, especially on the semantic DA in Mongolian-Chinese NMT. Therefore, in response to the scarcity of corpora and semantic inconsistency in Mongolian-Chinese NMT, we propose a method based on semantic-context DA, which does not generate explicit training samples, but uses semantic vector fusion to directly integrate enhancement into the training process. This method can significantly improve the quality of MNMT. Specifically, we initially trained a semantic encoder using contrastive learning to map source and target sentences into the same semantic space. Post training optimization, we sampled K source sentence vectors from the semantic space where source and target sentences intersect, and then fused them with the vectors output by the original encoder. Finally, the fused semantic rich vector was input into the decoder for decoding operation. We evaluated our framework on Mongolian-Chinese translation tasks, which is a much better method than using the baseline model. The main contributions of this paper are as follows:  
(1) Aiming to address the problem of data sparsity and missing semantics in MNMT, this paper proposes a semantic-context DA technique and use different DA methods to generate pseudo-parallel corpora. (2) We train an additional semantic encoder using the method of contrastive learning to extract the semantic information of the source and target in the Mongolian-Chinese NMT process. (3) The model proposed in this paper shows significant performance on both the Mongolian and Chinese low-resource datasets as well as the datasets generated through DA.
## 3 Results and Discussion
### Scanning Electron Microscopy  
Figure 4 shows the external and internal morphological characteristics of neat PLA/PCL and representative composite filaments. The external surface is characterized by a smooth texture, while in the case of HAp/BT/20-20 some tiny particles are observed (Figure 4a-c). The cross-section of the respective filaments also exhibits a homogenous structure (Figure 4d-f). In the case of pure PLA/PCL filaments, the cross-section images taken at higher magnification show the presence of droplets dispersed in the continuous phase (Figure 4g). The droplets can be attributed to PLA particles distributed in the PLA matrix [44]. Figure 4h,i show a homogenous distribution of particles in the polymeric matrix. A representative cross-section image of the PLA/PCL/20-20 composite filament is depicted in Figure 4j and the respective EDX mapping images for Ca and Ba are shown in Figure 4k,l. It can be observed that HAp and BT exhibit good dispersion in the polymeric matrix.  
Figure 5 shows SEM images and digital photos (insert) of PCL/PLA and PCL/PLA/20-20 fabricated scaffolds. The neat PLA/PCL samples show a smooth surface while in the  
Figure 4: SEM images: External surface (**a–c**) and cross-section images (**d–i**) of neat PLA/PCL, PLA/PCL/10-10, and PLA/PCL/20-20 filaments, respectively. Image (**j**) shows a cross-section detail of the PLA/PCL/20-20 filament while images (**k**) and (**l**) show the elemental mapping for Ca and Ba, respectively.  
case of composites the surface exhibits a rough structure due to the presence of the fillers. The existence of roughness is beneficial since the performance of a biomaterial depends on its surface properties and topography [10]. It has been reported that surface properties such as roughness and surface energy enhance cell cellular proliferation and adhesion [45].  
A regular array of square macropores is observed in both cases. The dimensions of the pores and the printed lines were measured from twenty SEM images using the image analysis program ImageJ. It was found that in the case of PLA/PCL objects, the mean pore size was 850.4 \(\pm\) 48.5 \(\upmu\)m while the mean diameter of the printed lines was measured at 393.3 \(\pm\) 51.8 \(\upmu\)m. Similar results were taken in the case of composite specimens. In the case of the PLA/PCL/10-10 objects, the values were 822.3 \(\pm\) 19.6 \(\upmu\)m and 397.2 \(\pm\) 58.0 \(\upmu\)m, respectively. The line width is close to the nozzle diameter which is 0.4 \(\upmu\)m. However, the mean pore size is lower than the theoretical design in the 3D pattern, which is about 1.1 \(\upmu\)m. This can be attributed to the deformation of the polymeric melt due to the low melting point of PCL. However, optimization of parameters such as flow rate and printing speed can lead to more precise printed pores in comparison with the designed 3D pattern.
## 5 Proposed Adaptive Beaconing System
### Characterization of Node Distribution
#### 5.2.2 Rules and Inference Engine
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rule Number** & **CBT** & **SINR** & **PDR** &
\begin{tabular}{c} **Preliminary Congestion** \\ **Rank** \\ \end{tabular} \\ \hline
1 & Small & Small & Large & Small \\ \hline
2 & Small & Small & Medium & Small \\ \hline
3 & Small & Small & Small & Small \\ \hline
4 & Small & Medium & Large & Small \\ \hline
5 & Small & Medium & Medium & Small \\ \hline
6 & Small & Medium & Small & Small \\ \hline
7 & Small & Large & Large & Small \\ \hline
8 & Small & Large & Medium & Medium \\ \hline
9 & Small & Large & Small & Medium \\ \hline
10 & Medium & Small & Large & Small \\ \hline
11 & Medium & Small & Medium & Medium \\ \hline
12 & Medium & Small & Small & Medium \\ \hline
13 & Medium & Medium & Large & Medium \\ \hline
14 & Medium & Medium & Medium & Medium \\ \hline
15 & Medium & Medium & Small & Big \\ \hline
16 & Medium & Large & Large & Medium \\ \hline
17 & Medium & Large & Medium & Big \\ \hline
18 & Medium & Large & Small & Big \\ \hline
19 & Large & Small & Large & Medium \\ \hline
20 & Large & Small & Medium & Big \\ \hline
21 & Large & Small & Small & Big \\ \hline
22 & Large & Medium & Large & Big \\ \hline
23 & Large & Medium & Medium & Big \\ \hline
24 & Large & Medium & Small & Big \\ \hline
25 & Large & Large & Large & Big \\ \hline
26 & Large & Large & Medium & Big \\ \hline
27 & Large & Large & Small & Big \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Set 3’s fuzzy rules (1). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rule Number** & \begin{tabular}{c} **Preliminary** \\ **Congestion** \\ **Rank** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Neighbors** \\ \end{tabular} & **Mobility** &
\begin{tabular}{c} **Final** \\ **Congestion** \\ **Rank** \\ \end{tabular} \\ \hline
1 & Small & Small & High & Very Small \\ \hline
2 & Small & Small & Medium & Very Small \\ \hline
3 & Small & Small & Low & Small \\ \hline
4 & Small & Medium & High & Very Small \\ \hline
5 & Small & Medium & Medium & Small \\ \hline
6 & Small & Medium & Low & Small \\ \hline
7 & Small & Big & High & Small \\ \hline
8 & Small & Big & Medium & Medium \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Set 3’s fuzzy rules (2). Depending on the fuzzy values and utilizing IF/THEN rules, the final network congestion rank (output) can be determined. The final network congestion rank is either Very Small, Small, Medium, Big, or Very Big.
# Domain Adaptation for Satellite-Borne Multispectral Cloud Detection
## 10 Conclusions and Future Study
### Acronyms  
The following acronyms are used in this manuscript. Note common nouns are in lower case, whereas names are capitalised:  
ACC accuracy  
BN batch normalisation  
CNN convolutional neural network  
CPGA contrastive prototype generation and adaptation  
CPU central processing unit  
DANN domain-adversarial neural network  
DNN deep neural network  
DUA dynamic unsupervised adaptation  
EO Earth observation  
FISH Fisher-Induced Sparse uncHanging  
Fmask Function of mask  
FP false positive  
GPU graphics processing unit  
NDCI normalised difference cloud index  
ReLU rectified linear unit
# A Vision-Based Micro-Manipulation System
## 5 Discussion  
### Image Enhancement  
The adoption of Fuzzy Automatic Contrast Enhancement (FACE) as an image enhancement technique in this study has demonstrated remarkable effectiveness in refining microscopic images of yeast cells. FACE, incorporating fuzzy clustering and optimizing entropy through a UCEV, autonomously improves image quality without the need for human-defined parameters. This automated enhancement yields a more refined pixel distribution, significantly enhancing image contrast while avoiding common issues such as color changes and visual artifacts. Comparative analyses underscore FACE's superiority in terms of enhancement quality compared to other methods.