esa-sceva/satcom-chunk-collection huggingface dataset:
# Protein Kinases in Mediating Phage-Bacteria Interactions
## 3 Bacteria-Encoded STKs in Phage-Bacteria Interactions
### Mycobacterium smgematis StpK7: A Pro-Phage Kinase [30]  
In _M. smgematis_, the STK StpK7 (MSMEG_1200) is uniquely exploited by the TM4 phage to counteract host defenses. StpK7 resides within a BREX (bacteriophage exclusion)-like gene island (_MSMEG_1191_ to MSMEG_1200) and phosphorylates to inhibit a transcription factor (MSMEG_1198) in this locus, silencing this host defense system. This phosphorylation sustains TM4 phage adsorption to host cells by maintaining normal glycolipid surface receptors and suppresses bacterial cell death, thereby enabling phage replication. However, the exact activating signal of StpK7 remains unknown.  
Figure 1: Bacteria-encoded (**A**) and phage-encoded (**B**) Serine/Threnine Kinases (STKs) in mediating phage-bacteria interactions. (**A**) Briefly, TM4 phage infection of _M. smgematis_ activates StpK7, inhibiting the defense island BREX thereby facilitating TM4 infection [30]. Phage CNPx derived protein PacK activates _Staphylococcus_ StpK (Stk2) to inhibit essential host proteins, aborting phage infection [31]. _Streptomyces coelicolor_ STK PglW phosphorylates PgIZ to activate PgIX, methylating phage DNA to prevent its infection [32]. _Enterococcus_ STK IreK phosphorylates GntR to express the type VIIb secretion system (T7 SS), mediating contact-dependent killing [33]. _Pseudomonas_ KKP (kinase-phosphatase, PfkAB, PfpC) module balances prophage activation and antiphage defense [34]. (**B**) Briefly, T7 phage STK Gp0.7 reprograms transcription and translation for viral proliferation, while prophage 933W encoded Stk, activated by HK90-derived Orf41, blocks HK90 infection [41]. JSS1_004 phosphorylates a few phage-defense systems (Dnd, CRISPR-Cas), facilitating the infection of phage JSSI in Salmonella [42]. See main text for details. Gram-positive and negative-bacteria cell envelopes are colored in purple and red, respectively. The STK names are in bold; phage names and their proteins are in red font; prophases and their encoded proteins are in purple. Dotted and solid arrows indicate indirect/unclear and direct effects, respectively. (Created in BioRender. ZHANG, Y. (2025) [[https://BioRender.com/pjeuujn](https://BioRender.com/pjeuujn)]([https://BioRender.com/pjeuujn](https://BioRender.com/pjeuujn)) (accessed on 1 June 2025)).
###### Abstract  
A novel graphene-oxide-derived material was synthesized after modification of graphene oxide with sodium hydroxide and used for the dispersive solid-phase extraction (d-SPE) of different elements (Pb, Cd, Ba, Zn, Cu and Ni) prior to their determination by inductively coupled plasma atomic emission spectrometry (ICP-AES). The prepared nanomaterial was characterized by X-ray diffraction (XRD), nitrogen adsorption, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Fourier transform infrared (FTIR) spectroscopy. Full factorial design and [PERSON]'s type desirability function were used for the optimization of the d-SPE procedure. Pareto charts illustrated the effects of each of the examined factors and their interactions on the determination of the elements. Under the optimum conditions, detection limits (LODs) for the elements ranged between 0.01 and 0.21 ug g\({}^{-1}\), intra-day repeatability (\(n=5\)) was lower than 1.9% and inter-day repeatability (\(n=5\times 3\)) was lower than 4.7%. Relative recovery values ranged between 88.1 and 117.8%. The method was validated and successfully applied for the determination of trace elements in poultry, pork and beef samples from the local market. The proposed method is simple, rapid, sensitive and the novel sorbent can be used at least ten times.  
graphene oxide; metals; meat samples; ICP-AES; dispersive solid-phase extraction; experimental design +
Footnote †: journal: Applied Physics Letters
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
## 2 Early Endosome-to-Golgi Transport of STx and STx1
### GPP130--The Endosomal Receptor for STxB/STx1B  
STxB/STx1B are soluble proteins that are contained within the endosomal lumen and cannot directly access cytosolic trafficking factors that mediate early endosome-to-Golgi trafficking. Instead, the toxins rely on the host protein Golgi phosphoprotein 4 (GPP130), which serves as the endosomal sorting receptor for STxB/STx1B (Figure 2A) [10; 19]. GPP130 is a single-pass transmembrane protein that constitutively cycles between the _cis_-Golgi and early endosomes (Figure 2A) [24; 25; 26]. STxB/STx1B directly binds the luminal domain of GPP130 with a K\({}_{\text{d}}\) of 150 nM [19]. Depletion of GPP130 inhibits the early endosome-to-Golgi transport of STxB/STx1B (Figure 2B) [19]. This effect is rescued by expression of full-length GPP130, but not by GPP130 mutants that lack the binding site for STxB/STx1B or that cannot cycle between the Golgi and early endosomes [19]. Furthermore, the cytosolic domain of GPP130 interacts with the SNARE syntaxin 5 [27]. Syntaxin 5 is required for the early endosome-to-Golgi transport of STxB/STx1B (see below), and the GPP130-syntaxin 5 interaction is critical to support STxB/STx1B transport [27]. Overall, binding of STxB/STx1B to its endosomal receptor, binding of the endosomal receptor to a critical cytosolic trafficking factor, and cycling of the receptor itself are necessary for toxin transport (Figure 2A). GPP130 is the first, and to date only, endosomal receptor identified for an AB\({}_{5}\) toxin.
# Preparation and Modification of Biochar Materials and their Application in Soil Remediation
## 2 Preparation and Modification of Biochar  
### Preparation of Biochar  
The preparation methods of biochar are mainly divided into pyrolysis [12], hydrothermal carbonization (HTC) [13], and microwave carbonization [14]. Different preparation methods affect the physical and chemical properties of biochar, such as yield, ash, specific surface area, pore structure, type and number of functional groups, and cation exchange capacity. Compared with the pyrolysis, HTC does not require drying step and has a higher biochar yield [15]. The advantages of microwave carbonization are controllable process, no hysteresis, rapid heating, and energy efficiency [16; 17]. However, biochar prepared through HTC and microwave contained high concentrations of organics, which are not actually considered soil remediation material.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 4 A Closer Look at the Time-Based Functions
### Offline Modeling Functions
#### 4.3.2 Model Management  
Model management is essentially a model training process done in the cloud layer in the training and update function. Based on the recommendation from optimization and design functions, the training process for models can be done. All trained models are deployed to subsequent layers as per the control architecture. Based on both real- and near-real-time monitoring and analysis of sensor data, training functions can be used for training at an initial level to deploy the model in sublayers. If the predicted result does not match the current standards, the retraining function starts again to improve modeling accuracy.  
Model management focuses on using data analytics to improve control decisions. In situ monitoring and control data from multiple builds as well as part development lifecycle data are aggregated for analysis. The data can be used for training to correlate process settings with process signatures, microstructure properties, and mechanical properties. The resulting models are used for build-plan generation. Machine learning can also be used to train models, which are used in real- or near-real-time process monitoring and control.
# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 2 Architectures for Coordinated Control of DDEV Chassis
### Centralized Architecture  
Corresponding to decentralized architecture, centralized control architecture represents another philosophy of system integration, with its core objective being to achieve optimal vehicle-level control through a global information fusion and a unified decision-making mechanism. Its typical structure is shown in Figure 2. In a centralized control architecture, sensory information from all the subsystems of the vehicle chassis is gathered into a single central controller. This central processing unit is solely responsible for global state assessments, control decision-making, and the generation of control commands, and it dispatches these commands to the various actuators. Under this architecture, information achieves a high degree of centralization and sharing, the control logic typically adopts a top-down design approach, and the central controller possesses the highest control authority over the entire chassis system.  
Although centralized control architectures exhibit a high degree of integration and theoretical optimal control potential, some studies indicate that they still face numerous constraints in practical engineering applications, making their widespread adoption difficult [39; 40]. Specifically, due to the lack of relative independence among various parts of the control system, the development of overall vehicle control functions is highly dependent on deep collaboration and coordination among electronic control system suppliers. Furthermore, global integrated control places extremely high demands on the data processing capabilities of the central processor, leading to a huge computational load on the ECU.  
Figure 2: Centralized architecture.  
At the same time, this control architecture also has deficiencies in terms of flexibility for functional expansion and systems' fault tolerance capabilities.
## 4 Model Formulation and Specification
### Disaggregate Consideration Model Specification  
The binary consideration of trains by an individual \(i\) is denoted by the variable \(Y_{1i}\) (1 if considered, 0 otherwise). The underlying continuous propensity to consider trains in the choice set is represented by \(U_{1i}\), which comprises a systematic component \(V_{1i}\) and a random component \(\epsilon_{1i}\). The systematic component is specified using the linear in parameter form in Equation (3) below, where FLMs, WLKs, TSCs, and PERWs represent the vector of variables on first- and last-mile connectivity, walkability, train service characteristics, and personal and work characteristics explained earlier, and \(\beta_{1},\ \beta_{2},\ \beta_{3},\beta_{4}\) denote their corresponding vector of coefficients.  
\[Y_{1i}=\left\{\begin{aligned} & 1,\ if\ train\ is\ considered\\ & 0,\ otherwise\end{aligned}\right. \tag{1}\]  
\[U_{1i}=V_{1i}+\epsilon_{1i} \tag{2}\]  
\[V_{1i}=\beta_{0}+\beta_{1}FLM_{i}+\beta_{2}WLK_{i}+\beta_{3}TSC_{i}+\beta_{4} PERW_{i} \tag{3}\]  
The probability of considering trains can be derived as follows:  
\[P(Y_{1i}=1)=P(U_{1i}\geq 0)=P(V_{1i}+\epsilon_{1i}\geq 0)=P(V_{1i}\geq- \epsilon_{1i})=1-\mathrm{F}_{e}(-V_{1i}) \tag{4}\]  
where \(\mathrm{F}_{e}\) (.) represents the cumulative density function of the error term. The corresponding log-likelihood LL for an individual can be written as follows:  
\[LL=\sum_{i=1}^{n}Y_{1i}*logP(Y_{1i}=1)+(1-Y_{1i})*logP(Y_{1i}=0) \tag{5}\]  
The log-likelihood is maximized to estimate the vector of coefficient \(\beta\) in view of its desirable consistency and asymptotic efficiency properties. To account for the heterogeneity due to captivity-based segments in consideration decisions, each explanatory variable interacts with a segment-level indicator. The final specification is obtained by using the best possible combinations of generic and segment-specific variables (and their effects), based on their interpretability and overall goodness of fit. The above log-likelihood equation is estimated for alternative discrete choice models, including probit, Cauchy, log-log, and complementary log-log models, and the best model is identified based on goodness-of-fit.
# Modeling a Pre-Touch Reaction Distance around Socially Touchable Upper Body Parts of a Robot
## 4 Results and Implementation
### Data Analysis Results
#### 4.1.4 Summary  
Based on analysis for the pre-touch reaction distance around the upper body parts, we confirmed that the minimum comfortable distance around the hands is smaller than the minimum comfortable distance around the shoulders and elbows. Moreover, the results showed that gender and angle factors did not show a significant effect. These results exhibited a similar phenomenon with a past study about pre-touch reaction distance around the face [16]. The parts factor, only for the distance around the hands, showed a significant difference in the shoulder and elbow distances. The movement speed showed a weak impact in the minimum comfortable distance, and the acclimation effect also showed a significant difference.
# Boosting the Transformer with the BERT Supervision in Low-Resource Machine Translation
## 3 Approach  
### Latent Representation Using the BERT  
In the Transformer, the encoder turns each input token into one embedding vector and generates the hidden representations through the attention mechanisms, and the decoder maps the hidden representations in the latent space into the target sentence. In previous works, the Transformer was trained end-to-end without any restriction on the latent space. Only the dimension of the latent representation was treated as a hyperparameter and optimized through the validation experiments. In a low-resource translation task, end-to-end training of the Transformer will very likely lead to ineligible representations. Since the BERT has achieved great success in language understanding tasks, we argue that it can be treated as a good representation of the semantic space. A good representation will benefit the model convergence and final performance. Thus, this paper selects the BERT asthe latent representation of the encoder and makes the source sentences and their target sentences align in this specific space. The reasons are as follows:  
First, previous works have shown that language model pre-training is effective for improving many natural language processing tasks. [PERSON] et al. [40] used the BERT as the encoder and the GPT2 as the decoder to form an encoder-decoder framework. It proved that an ideal encoder is good for the performance. Second, the BERT is the first fine-tuning-based representation model that produces contextual embeddings and achieves state-of-the-art performance on an extensive suite of sentence-level tasks. It is a subtle and accurate representation of the word in a sentence. Third, the BERT is a variant of the Transformer architecture, which is similar to the encoder in the Transformer.  
By adding the BERT constraint on the latent representation, we make it possible to train the encoder and the decoder of the Transformer independently. Let \(x\) be the source sentence, and \(y\) be the corresponding target sentence, then the learning target of the encoder on \(x\) is the output of the BERT on \(y\) in the encoder training. The decoder of the Transformer is to map the latent representation into the target language. In decoder training, the input is the representation of \(y\) from the BERT, and the output is \(y\).  
We introduce the BERT of the target language to generate the training data set from the parallel corpus for the encoder and the decoder training. That is, the training data sets for the encoder and the decoder training are in the form of (\(x\), the BERT(\(y\))) and (the BERT(\(y\)), \(y\)), respectively. We describe the training algorithm in the following section.
# Changes in Sprint Momentum in Elite Rugby Union Players over a Three-Season Period
## 3 Results
### Effects of Changes in Body Mass and Sprint Speed on Changes in Momentum  
In backs, the linear mixed-effects modelling analysis revealed a significant effect of speed changes on momentum changes, with a 1 m/s improvement of speed leading to a momentum increase by 108.2 kg\(\cdot\)m/s (95% CI 11.7-204.7 kg\(\cdot\)m/s, \(p\) = 0.034, ES = 1.06, moderate). Moreover, in backs, a significant effect of body mass changes on momentum changes was observed, as follows: 1-kg increases of body mass induced a 6.1 kg\(\cdot\)m/s (95% CI 3.64-8.57 kg\(\cdot\)m/s, \(p\) = 0.002, ES = 1.44, large) increase in momentum. No significant effect of changes in players' body mass on changes in sprinting speed was observed (\(p\) = 0.763).  
In forwards, there was a significant effect of speed changes on momentum changes, with a 1 m/s increase in sprinting speed inducing a 99.5 kg\(\cdot\)m/s increase in momentum (95% CI 7.25-191.72 kg\(\cdot\)m/s, \(p\) = 0.036, ES = 1.39, large). Furthermore, a significant effect of changes in body mass on changes in momentum was observed. A 1-kg increase in body mass involved a 5.39 kg\(\cdot\)m/s increased momentum (95% CI 3.76-7.01 kg\(\cdot\)m/s, \(p\) < 0.001, ES = 1.37, large). No significant effect was observed between changes in players' body mass and changes in sprinting speed (\(p\) = 0.808).
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 4 Experiments
### MOTS Dataset  
MOTS [1] extends the MOT benchmark MOT17 [41] to pixel-level accuracy by providing instance mask annotation for pedestrians based on the MOT17 video sequences. MOTS uses frame-by-frame segmentation masks to provide more accurate annotation. The dataset contains four training videos and four test videos, for a total of 5906 frames. The dataset contains 556 tracked pedestrians, 59,163 are detected, and instance segmentation results are obtained for both the training and test sets. MOTS is a pixel-level multi-object tracking and segmentation performance evaluation dataset.
## 4 Results
### Threshold-Based--Experimental Results  
As previously introduced, the setup built and reported in Figure 2 was employed to test the actual functioning of the threshold-based method. Practically, two common intralogistic operations, i.e., sorting and orientation, were selected as handling tasks for the system control experimental validation. In the first case, the threshold condition was the ID of the package, while in the second case, the orientation angle was the controlled variable. These two operation outputs are presented in the following subsections.
## 3 Methodology
### Mean Teacher Framework Integration  
During a traffic classification task, acquiring a substantial number of labeled data is both labor-intensive and time-consuming. The Mean Teacher framework represents a semi-supervised learning approach that enhances model performance through the interaction between the teacher and the student. The parameters of the teacher are updated via an exponential moving average (EMA), whereas the student is trained by using conventional gradient descent methods. By maintaining the stability of the teacher, it effectively guides the learning process of the student.  
Specifically, during the training part, the student is updated on account of a composite loss function, which comprises both the classification loss and the consistency loss. Consistency loss quantifies the discrepancy between teacher and student predictions for the same input data. The objective is to ensure that the student model's predictions are closely consistent with those of the teacher model, thereby stabilizing the training process and enhancing generalization capabilities.  
Given an input sample \(x\), let \(f_{s}(x)\) express the prediction generated by the student model, and let \(f_{t}(x)\) express the prediction generated by the teacher model. The consistency loss \(L_{\text{cons}}\) can be defined as  
\[L_{\text{cons}}(x)=d(f_{s}(x),f_{t}(x)) \tag{11}\]  
where \(d(\,,)\) is a distance metric. In our implementation, we used \(MSE\) as the distance metric:  
\[L_{\text{cons}}\ (x)=\frac{1}{N}{\sum_{i=1}^{N}}(f_{s}(x_{i})-f_{t}(x_{i}))^{2} \tag{12}\]  
Here, \(N\) is the count of output dimensions.  
The teacher model's parameters are updated by using the EMA of the student model's parameters. The EMA calculation is given by the equation below:  
\[\theta_{t}^{\prime}=\alpha\theta_{t-1}^{\prime}+(1-\alpha)\theta_{t} \tag{13}\]  
where \(\theta_{t}^{\prime}\) is the teacher model parameter whose time step is t, \(\theta_{t-1}^{\prime}\) is the previous time step, \(\theta_{t}\) is the parameter of the student model when the time step is t, and \(\alpha\) is the smoothing coefficient, set to 0.9 in this study. We chose \(\alpha=0.9\) because it strikes a balance between stability and responsiveness. A lower \(\alpha\) (e.g., 0.5) would make the teacher model too sensitive to changes in the student model, potentially leading to instability. Conversely, a higher \(\alpha\) (e.g., 0.99) might result in overly slow updates and reduce the guidance of the teacher model for the student model. After the training process is complete, we use the teacher model for the validation part.
## 7 Future Work  
In regard to future research in this field of study, the main concern in soil moisture retrieval will be to obtain quantitative soil moisture estimates from dielectric constant values and advanced research to determine more accurate dielectric constant. Researching the effect of land classification types on the dielectric constant can be achieved using analysis that is similar to that used in this study. In addition, the separation of coherent and incoherent scattering components in delayed Doppler maps is a challenging domain of research due to the combination of electromagnetic scattering theory and data analysis. Regardless of the difficulty, the separation of scattering components will contribute to not only GNSS reflectometry, but also electromagnetic theory. Moreover, the results of this work will maximize the benefit of a GNSS-R instrument by investigating the effect of incident angle on scattering. Observing RHCP reflected GNSS signals in the nadir direction will also require accurate dielectric constant values.  
The GNSS Lab at York University, Canada, is developing a Field Programmable Gate Array (FPGA)-based GNSS-R receiver, which is funded by the Canadian Space Agency. Its general objective is to teach students to develop their knowledge of space. Its scientific objective is to estimate the soil moisture content onboard various platforms and it is scheduled to be flown on an unmanned aerial vehicle (UAV) and an aircraft. With these experimental experiences, the GNSS Lab will be designing and developing a receiver for use on a nanosatellite in low Earth orbit. At present, a prototype GNSS-R receiver has been built using commercial off-the-shelf parts. Outdoor testing will be conducted over longer periods, and across various soil types, vegetation, and topography. The results from this GNSS-R receiver will contribute to organizing the calibration method for the instrument on land surfaces and local soil moisture modelling. It is then expected that this process will provide quantitative soil moisture data, not only for local regions, but also global places, and will induce positive interactions with various fields, such as climate research and the agricultural industry.  
Conceptualization, J.L.; Data curation, N.G.K.; Investigation, J.L.; Methodology, J.L.; Project administration, S.B.; Resources, S.B.; Supervision, S.B. and R.S.K.L.; Validation, J.L. and N.G.K.; Visualization, J.L.; Writing--original draft, J.L.; Writing--review & editing, S.B., R.S.K.L. and N.G.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Canadian Space Agency (CSA), grant number 18 FAYORA09.  
The data presented in this study are openly available in PO.DAAC, reference number [39].  
This work is supported by the Canadian Space Agency's Flights and Fieldwork for the Advancement of Science and Technology (FAST) funding program.  
The authors declare no conflict of interest.