esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Impact of Carbon Source Type and Concentration  
For biomass and paramylon production from the mixotrophic cultivation of _E. gracilis_, glucose was determined to be the most effective carbon source as compared with ethanol, lactate, and acetate. Biomass production using glucose or lactate significantly differed from that achieved using ethanol or acetate (\(p\) < 0.05) (Figure 1). It has been widely reported that glucose can be used as an effective carbon source for biomass production from _E. gracilis_ under both mixotrophic and heterotrophic cultivation conditions. Ethanol has also been reported as an efficient carbon source under heterotrophic cultivation [18; 19]. [PERSON] et al. (2008) reported a higher SGR with ethanol than with glucose [20]. Ethanol, as an organic carbon source under the heterotrophic cultivation of _E. gracilis_, participates in respiration, glycolysis, gluconeogenesis, and paramylon synthesis, and is rapidly oxidized to acetate [21]. Under the mixotrophic cultivation of _E. gracilis_, ethanol increased the production of \(\beta\)-carotene, chlorophyll, and tocopherol [22]. Thus, the preferred carbon source for _E. gracilis_ can vary depending on the trophic mode. In accordance with other studies, the higher is the number of carbon atoms in the carbon sources, the higher is the biomass and paramylon production.  
The highest biomass and paramylon production was observed under the GL15 condition (\(p\) < 0.05). There were significant differences in both biomass and paramylon production between GL5 and GL15 conditions. It has been reported that low glucose concentrations result in low SGR, consequently leading to lipid accumulation. [PERSON] et al. (2016) demonstrated a significant decrease in _E. gracilis_ at C/N ratio 10 [23]. In this study, the C/N ratio of GL60 was 12, and the lowest biomass and paramylon production was observed under these conditions. Although glucose is widely preferred for many microalgal species, including _Chorella_ sp. and _Euglen_ sp., the optimal C/N ratio is varied. While the most preferred carbon source for _Chlorella_ sp. HS2 was glucose compared with sucrose, sodium acetate, and lactic acid, the optimal C/N ratio was 21.8 [24]. [PERSON] et al. (2021)  
Figure 4: (**a**) Biomass production, (**b**) COD consumption, and (**c**) T-N consumption using STB and CM medium at an initial pH 8. Sample analyses were performed in duplicate, and the data were represented as average \(\pm\) standard error (\(n\) = 4).  
reported the correlation between biomass productivity and nutrient consumption (BOD\({}_{5}\), COD, NH\({}_{3}\)-N, and PO\({}_{4}\)\({}^{3}\)-P), where nutrient concentrations decreased with an increase in biomass production [5]. In this study, the greater was the consumption of COD and TN, the higher was the production of biomass and paramylon (except for GL5).
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 2 Related Work
### Deep Deterministic Strategy Gradient  
Deep deterministic policy gradient (DDPG) is an actor-critic algorithm for a continuous action space. It combines deterministic policy with experience playback and target networks to achieve stable training. The key formulas and mechanisms are as follows.  
(1) Action generation and exploration.  
The actor network outputs deterministic actions and superimposes OU noise (Ornstein-Uhlenbeck process) for exploration.  
\[a_{t}=\mu_{\theta}(s_{t})+N_{t},N_{t}=\theta(\mu-N_{t-1})\Delta t+\sigma\sqrt {\Delta t}\cdot\xi \tag{7}\]  
where \(\theta,\ \mu,\ \sigma\) is the noise parameter, \(\xi\sim N(0,I)\).  
(2) The target Q value of the critic network.  
\[y_{i}=r_{i}+\gamma\cdot Q_{\theta^{-}}(s_{i+1},\mu_{\theta^{-}}(s_{i+1})) \tag{8}\]  
(3) The loss function and parameter update.  
The critic network is updated by the mean square error:  
\[L(\theta)=\mathbb{E}\Big{[}\big{(}y_{i}-Q_{\theta}(s_{i},a_{i})\big{)}^{2} \Big{]} \tag{9}\]  
The actor network maximizes the Q value by gradient rise:  
\[\
abla_{\theta}\int\infty\mathbb{E}[\
abla_{a}Q_{\theta}(s,a)\cdot\
abla_{ \theta}\mu_{\theta}(s)] \tag{10}\]  
(4) Soft update of the target network.  
\[\theta^{-}\leftarrow\tau\cdot\theta+(1-\tau)\cdot\theta^{-},\phi^{-} \leftarrow\tau\cdot\phi+(1-\tau)\cdot\phi^{-} \tag{11}\]  
where \(\tau\) is the soft update coefficient (e.g., 0.001).
# Local Data Debiasing for Fairness Based on Generative Adversarial Training
## 4 Adversarial Training for Data Debiasing
### Performance Metrics  
The performance of GANSan will be evaluated by taking into account the _fairness enhancement_ and the _fidelity_ to the original data. With respect to fairness, we will quantify it primarily with the inability of a predictor \(Adv\), hereafter referred to as the adversary, in inferring the sensitive attribute (cf. Section 2) using its _Balanced Error Rate (BER)_[22] and its _accuracy sAcc_ (cf., Section 2.2). We will also assess the fairness using metrics (cf. Section 2) such as _demographic parity_ (Equation (1)) and _equalized odds_ (Equation (2)).  
To measure the fidelity \(fid\) between the original and the sanitized data, we have to rely on a notion of distance. More precisely, our approach does not require any specific assumption on the distance used, although it is conceivable that it may work better with some than others. For the rest of this work, we will instantiate \(fid\) by the \(L_{2}\)-norm as it does not differentiate between attributes.  
Note however that a high fidelity is a necessary but not a sufficient condition to imply a good reconstruction of the dataset. In fact, as mentioned previously early experiments showed that the sanitizer might find a \"median\" profile to which it will map all input profiles. Thus, to quantify the ability of the sanitizer to preserve the diversity of the dataset, we introduce the _diversity_ measure, which is defined in the following way:  
\[diversity=\frac{\sum_{i=1}^{N}\sum_{j=1}^{N}\sqrt{\sum_{k=1}^{d}(\bar{r}_{i,k}- \bar{r}_{j,k})^{2}}}{N\times(N-1)\times\sqrt{d}} \tag{5}\]  
in which \(\bar{r}_{i,k}\) represent the \(k\)th attribute of the sanitized version of \(r_{i}\). While \(fid\) quantifies how different the original and the sanitized datasets are, the diversity measures how diverse the profiles are in each dataset. We will also provide a qualitative discussion of the amount of damage for a given fidelity and fairness to provide a better understanding of the qualitative meaning of the fidelity.  
Finally, we evaluate the loss of utility induced by the sanitization by relying on the accuracy \(yAcc\) of prediction on a classification task. More precisely, the difference in \(yAcc\) between a classifier trained on the original data and one trained on the sanitized data can be used as a measure of the loss of utility introduced by the sanitization with respect to the classification task.
###### Abstract  
Chinese, a tonal language with inherent homophonic ambiguity, poses significant challenges for semantic disambiguation in natural language processing (NLP), hindering applications like speech recognition, dialog systems, and assistive technologies. Traditional static disambiguation methods suffer from poor adaptability in dynamic environments and low-frequency scenarios, limiting their real-world utility. To address these limitations, we propose BLAF--a novel MacBERT-BiLSTM Hybrid Architecture--that synergizes global semantic understanding with local sequential dependencies through dynamic multimodal feature fusion. This framework incorporates innovative mechanisms for the principled weighting of heterogeneous features, effective alignment of representations, and sensor-augmented cross-modal learning to enhance robustness, particularly in noisy environments. Employing a staged optimization strategy, BLAF achieves state-of-the-art performance on the SIGHAN 2015 (data fine-tuning and supplementation): 93.37% accuracy and 93.25% F1 score, surpassing pure BERT by 15.74% in accuracy. Ablation studies confirm the critical contributions of the integrated components. Furthermore, the sensor-augmented module significantly improves robustness under noise (speech SNR to 18.6 dB at 75 dB noise, 12.7% reduction in word error rates). By bridging gaps among tonal phonetics, contextual semantics, and computational efficiency, BLAF establishes a scalable paradigm for robust Chinese homophone disambiguation in industrial NLP applications. This work advances cognitive intelligence in Chinese NLP and provides a blueprint for adaptive disambiguation in resource-constrained and dynamic scenarios.  
Chinese NLP; multimodal dynamic fusion; context-aware modeling; homophone disambiguation; neural architecture design 2025157068  
1
## 3 Results
### Substrate Specificity of EstDR4  
The substrate specificity of EstDR4 was determined by using \(p\)NP esters of various chain lengths as substrates (C2-C16) (Figure 3a). The results revealed that EstDR4 preferentially hydrolyzes short and medium chain monoesters. EstDR4 displayed maximum hydrolytic activity toward \(p\)NPC8. Additionally, EstDR4 exhibited high activity toward short chain esters C2 (40.16%) and C4 (57.80%), weak activity toward C12 (11.68%), and negligible activity toward C16 (2.03%). Thus, EstDR4 is best classified as an esterase (EC3.1.1.1) according to the substrate preference.  
We also measured the regioselectivity and enantioselectivity of the EstDR4. The regioselectivity was determined by using \(\alpha\)-NA and \(\beta\)-NA as substrates. As shown in Figure 3b, EstDR4 had a stronger substrate preference for \(\alpha\)-NA compare with \(\beta\)-NA, the hydrolysis rate of EstDR4 on \(\alpha\)-NA was almost twice that of \(\beta\)-NA. (R)-MHM and (S)-MHM were used as the substrates to detected the enantioselectivity of EstDR4 by the pH shift method. The cleavage of the ester bond will generate acid, and the reaction solution added with phenol red will also change color with the change of pH. The darker the yellow, the more acid was generated in the solution. As shown in Figure 3c, EstDR4 displayed a substrate preference for (R)-MHM over (S)-MHM, weak hydrolytic activity toward (R)-MHM and negligible activity toward (S)-MHM, thus, the enantioselectivity of EstDR4 can be confirmed. According to the results shown in Figure 3c, EstDR4 also had a hydrolysis effect on phenyl ester, tertiary alcohols and triglycerides, including PA, 2-MA, TA, GTB and GTO. However, EstDR4 still displayed negligible hydrolytic activity toward long-chain tertiary alcohols and glycerides such as LA and OO.
# Biomechanical Analysis of Two Types of Oseointegrated Transfemoral Prosthesis
## 5 Conclusions  
Loads during different fall scenarios have been experimentally evaluated by a motion capture system and a force plate. Calculated loads could be effectively used for numerical analyses of different kinds of prostheses. On the basis of the obtained results, it can be stated the fall backwards with balance loss, among the scenarios analysed, is the most stressful. OPRA and OPL devices have similar behaviours during falls in terms of peak values of the equivalent stress on bone and prostheses, but OPL implant stresses the distal part of the femur more than the OPRA device. This should be considered if this kind of implant is used in poor-quality bone. Further studies should investigate design optimization of the prosthesis to further limit the risk of bone fracture and implant failure during critical events like falls, thereby improving long-term successful outcomes.  
Conceptualization, A.I.M., F.D.S., T.I. and L.B.; Data curation, A.I.M., F.D.S. and S.D.P.; Methodology, A.I.M., F.D.S., T.I. and L.B.; Software, A.I.M., F.D.S. and T.I.; Validation, F.D.S., T.I, L.B. and S.Z.; Supervision, T.I., V.N., L.B. and S.Z.; Writing--original draft, A.I.M., S.D.P. and T.I.; Writing--review and editing, T.I. and L.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 12: Fall Forward with Balance Loss (FF BL) scenario: von Mises stress distribution on femur using the OPRA (**top**) and the OPL (**bottom**) device.
## 3 Data and Methods
### Methodology  
#### 3.2.1 Analysis of SAR Images  
In this study, Sentinel-1 level-1 Ground Range-Detected (GRD) imagery with VV polarization is utilized on the Google Earth Engine (GEE) platform for inundation mapping along the GBM coastline in Bangladesh. The preprocessing of Sentinel-1 GRD imagery followed a standardized workflow to ensure radiometric and geometric consistency prior to flood detection analysis. Initially, orbit metadata were updated using restituted orbit files to enhance geolocation accuracy. Low-intensity noise and invalid data near scene edges were then removed to reduce boundary artifacts. Subsequently, additive noise present in the sub-swaths--common in multi-swath acquisition modes--was corrected to minimize radiometric discontinuities across the scene. Radiometric calibration was conducted to derive backscatter intensity (\(\sigma^{\circ}\)) by applying sensor-specific calibration constants embedded in the GRD metadata. Finally, terrain correction was applied to convert the data from ground range to \(\sigma^{\circ}\) backscatter in map geometry, incorporating the Shuttle Radar Topography Mission (SRTM) 30-m digital elevation model (DEM) to account for topographic distortions. \(\sigma^{\circ}\) can span several orders of magnitude; it was subsequently converted to decibels (dB) scale using the transformation 10-log\({}_{10}\) (\(\sigma^{\circ}\)) to facilitate analysis and interpretation. On the GEE platform, the Sentinel-1 GRD dataset undergoes a standardized preprocessing workflow comprising orbit metadata updates, elimination of low-intensity noise and invalid edge data, sub-swath additive noise correction, radio-metric calibration, orthorectification, and transformation of the backscatter coefficient (\(\sigma^{\circ}\)) to decibels (dB) units [57]. To reduce speckle noise inherent in SAR imagery, a focal mean filter was applied using a 50-m smoothing radius within the Google Earth Engine (GEE) platform. This spatial averaging technique helps preserve broad-scale features while minimizing high-frequency noise, thereby enhancing the quality of the backscatter signal for subsequent analysis.  
For inundation mapping, a change detection approach was employed, wherein a raster layer representing pixel-wise change intensity was generated by normalizing the post-flood SAR mosaic against the pre-flood mosaic. In this layer, higher values indicate significant surface change, while lower values suggest minimal or no change. A threshold value named \(f_{t}\) was applied; pixels with values exceeding this threshold were classified as inundated (1), and those below it as non-inundated (0). \(f_{t}\) was determined through ML algorithms. The details of \(f_{t}\) determination algorithm was discussed in Section 3.2.2. The methodology of SAR image analysis is shown briefly in Figure 2. To improve classification accuracy and reduce false positives, several auxiliary datasets were integrated, notably using the JRC Global Surface Water dataset to exclude permanent water bodies.
## 6 Real-Time In-Vivo and In-Vitro Corrosion Monitoring  
Real-time corrosion monitoring refers to any process that monitors the corrosion of in-vivo/in-vitro biodegradable metal implants which allows for the results to be obtained immediately during the measurement. It is typically done by combining two different methods of corrosion monitoring. For example, pairing radiography with an image processing software or inspecting blood samples online with mass spectroscopy [2]. Since the corrosion of metals is a real-time dependent process, it cannot be ideally observed using techniques such as implant retrieval, histology analysis, etc. [4]. However, there are ways to evaluate the corrosion behavior of the biodegradable implant in real-time; these can be classified into: electrochemical-, biosensor-, and microdialysis-based monitoring systems.
## 4 Conclusions  
This article discussed the results of a study on the tribological behavior of an SLM-printed Co-Cr-Mo alloy. The microstructure of the printed alloy showed the melt pool boundaries and lack of fusion defects. The microhardness profile reveals a uniform hardness with a slightly higher value through the thickness due to the difference in heat transfer and residual stresses within the material. The coefficient of friction (COF) was studied under different loads, sliding speeds, and lubricated conditions. In dry contact, the COF starts high and maintains the average high value during the test due to adhesion, plowing, and deformation. The main wear mechanism in dry contact was abrasive wear, with fatigue wear and adhesive wear also contributing to the overall material removal and wear rate of the material. In lubricated contact, the COF was significantly lower, showing the effectiveness of lubrication for the alloy. The wear rate was observed to be significantly lower in lubricated contact compared to dry contact. As the applied load increases, the wear rate increases due to higher contact pressure, and as sliding speed increases, the wear rate also increases due to increased friction and heat generation. The obtained results showed that SLM-printed samples have a lower wear rate compared to the samples manufactured by the casting process. The type of lubricant affects the tribological behavior of the alloy, with gear oil providing the best results. Overall, the presented results can guide the selection of appropriate lubricants and operating conditions to minimize wear in Co-Cr-Mo alloy applications.  
Conceptualization, M.R. and Z.M.R.; methodology, M.R. and Z.M.R.; formal analysis, M.R.; investigation, M.R.; writing--original draft preparation, M.R.; writing--review and editing, M.R. and Z.M.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data presented in this study are available upon request.  
The authors declare no conflict of interest.
# Space-Efficient Fully Dynamic DFS
### Existing Results  
All the works described in this section focus on the general DFS tree problem, not the ordered DFS tree problem. Until recently, there were few papers for the dynamic DFS problem, despite of the simplicity of DFS in static setting. For directed acyclic graphs, [PERSON] et al. [1] proposed an incremental algorithm and later [PERSON] and [PERSON] [2] proposed a randomized decremental algorithm. For undirected graphs, [PERSON] and [PERSON] [3] proposed an incremental algorithm. However, these algorithms support only either of insertion or deletion, and do not support vertex updates. Moreover, none of these algorithms achieve the worst-case time complexity of \(o(m)\) per single update though the amortized update time is better than the static DFS algorithm. This means in the worst case the computational time becomes the same as the static algorithm.  
In 2016, [PERSON] et al. [4] proposed a dynamic DFS algorithm for undirected graphs which overcomes these two problems. Their algorithm supports all four types of graph updates, edge/vertex insertions/deletions, and achieves worst case \(O(\sqrt{nm}\log^{2.5}n)\) time per update. They also proposed an incremental (supporting only edge insertions) dynamic DFS algorithm with worst case \(O(n\log^{3}n)\) time per update. Later [PERSON] et al. [5] improved the fully dynamic worst-case update time by a \(\mathrm{polylog}(n)\) factor. [PERSON] et al. also showed in the full version [6] of their paper the conditional lower bounds for fully dynamic DFS problems: \(\Omega(n)\) time per update, under strong exponential time hypothesis, for any fully dynamic DFS under vertex updates, and \(\Omega(n)\) time per update, under the condition the DFS tree is explicitly stored, for any fully dynamic DFS under edge updates. Now the recently proposed incremental dynamic DFS algorithm of [PERSON] et al. [7] has \(O(n)\) worst-case update time and thus meets the lower bound of the incremental setting.  
Recently, [PERSON] et al. [8] conducted an experimental study for the incremental (not fully dynamic) DFS problem. Besides this, [PERSON] [9] proposed a parallel algorithm for the fully dynamic DFS (including vertex updates), which can compute the DFS tree after each update in \(O(\log^{3}n)\) time using \(m\) processors.  
Please note that after the preliminary version [10] of this paper was published, [PERSON] et al. [11] proposed an improved algorithm for the fully dynamic DFS in undirected graphs. This algorithm has worst-case \(O(\sqrt{mn\log n})\) update time and requires \(O(m\log n)\) bits of space.
## 1 Space-Bore X-ray and Gamma-ray Observational Research
### Previous Space-Borne Developments, Other Technologies  
In the field of X-ray and Gamma-ray astronomy and space-borne astrophysics several types of miniature scientific payloads have been developed in the past [8]. A first-generation highly miniaturized X-ray focusing telescope [9; 10] is deployed since 2017 in LEO onboard the Czech VZLUSAT-1 2U satellite [11; 12]. This first generation device consists of a one dimensional (1D) Lobster eye X-ray optics system and the photon-counting semiconductor pixel detector Timepix [13] equipped with a low-Z semiconductor sensor (silicon 300 \(\upmu\)m thick)) as focal-plane X-ray imager. Several other promising payloads are based on various types of detectors and X-ray optics [14; 15]. Additional CubeSat based experiments are either in preparation or under study [4; 6; 16; 17; 18; 19; 20; 21].  
The capability of solid-state detectors equipped with high-Z materials such as CdTe/CdZnTe sensors in high-energy astronomy was demonstrated by the European astronomical satellite, INTEGRAL (INTErnational Gamma-Ray Astrophysics Laboratory [22]) and also by the BAT instrument on board the satellite SWIFT [23]. INTEGRAL is a space mission dedicated to study the Gamma-ray sky. It was launched on 17 October 2002 by a Russian PROTON rocket into an elongated elliptical orbit with a revolution period of three sideral days. Such an orbit significantly decreases the background level caused by Van Allen belt particles and provides long periods of uninterrupted measurements. INTEGRAL is still in use. The imager IBIS (Imager on Board the INTEGRAL Satellite) provides imaging and measurement of celestial Gamma-ray sources. The imager is based on a large array of pixels, all physically distinct. The IBIS detector has two parallel planes of pixels, one on top of the other. Such an approach allows measurement of both low and high energy photons. The upper layer has 16,384 CdTe (Cadmium-Telluride) pixels and the lower layer has 4096 CsI (Cesium-Iode) pixels. IBIS imager provides an angular resolution of 12 arcmin in the energy range 15 keV-10 MeV [22].
# Robust Optimization of Transport Organization for China Railway Express
## 4 Example Analysis  
Based on the 'Plan for the Construction and Development of China-Europe Railway Express (2016-2020)' and available research data, 16 key nodes including Nanjing, Xuzhou, Suzhou, Lianyungang, Nantong, Chongqing, Chengdu, Xi'an, Zhengzhou, Wuhan, Changsha, Yiwu, Hefei, Shenyang, Dongguan, and Lanzhou are designated as primary inland cargo sources. Additionally, five major China-Europe railway hub nodes--Zhengzhou, Chongqing, Chengdu, Xi'an, and Urumqi--are chosen as central cargo aggregation centers.  
To simplify the calculation example, the railway network diagram for the China-Europe railway is structured with Moscow, Russia, serving as the ultimate destination of the railway transportation, depicted in Figure 1.