esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Study and Discussion
### Discussion of Disturbance Propagation  
To connect the theoretical model and the experimental results further, the pressure of the crowd was imported and visualized in MATLAB R2019b. As depicted in Figure 13a, when the luggage-laden passenger was advancing and causing a disturbance within the crowd, the propagation of the disturbance consistently intensified, displacing surrounding passengers at an angle towards the rear. The peak of the disturbance wave formed on the rear side of the luggage's motion direction, aligning with the direction of the luggage's disturbing force on the surrounding passengers. Consequently, the dissemination of the disturbance caused by the luggage to the surrounding passengers progressed in a manner consistent with the direction of the disturbing force. In addition, a low-disturbance area formed behind the luggage.  
Next, the change in the intensity of the disturbance directly behind the luggage as well as the side rear will be discussed. As shown in Figure 14, points P and S were chosen to represent the front and rear side, respectively. In Figure 15a, when the luggage-laden passenger particle passes through point P, the pressure of P is greater than that of point S, reaching a peak at the 400 th frame. As the luggage-laden passenger particle moves away from point P, the pressure at point P oscillates at the frequency of the disturbance  
Figure 12: Disturbing force of the luggage-laden passenger particle on both sides.  
Figure 13: Contact pressure distribution during luggage retrograding.  
propagation and decays. The pressure at point S reaches the maximum at the 500 th frame and remains at about 1, which is maintained in a low-equilibrium state with a long fluctuation time and a small fluctuation threshold. This is the reason that disturbances at different moments are superimposed at point S, making its fluctuation frequency longer. As it is too far from the trigger point of the disturbance, the initial pressure is less than the intensity of point P, but because of the propagation and superposition of the disturbance, the pressure attenuation speed is slower than that of point P.
# Targeting the Early Endosome-to-Golgi Transport of Shiga Toxins as a Therapeutic Strategy
###### Abstract  
Shiga toxin (STx) produced by _Shigella_ and closely related Shiga toxin 1 and 2 (STx1 and STx2) synthesized by Shiga toxin-producing _Escherichia coli_ (STEC) are bacterial AB\({}_{5}\) toxins. All three toxins target kidney cells and may cause life-threatening renal disease. While _Shigella_ infections can be treated with antibiotics, resistance is increasing. Moreover, antibiotic therapy is contraindicated for STEC, and there are no definitive treatments for STEC-induced disease. To exert cellular toxicity, STx, STx1, and STx2 must undergo retrograde trafficking to reach their cytosolic target, ribosomes. Direct transport from early endosomes to the Golgi apparatus is an essential step that allows the toxins to bypass degradative late endosomes and lysosomes. The essentiality of this transport step also makes it an ideal target for the development of small-molecule inhibitors of toxin trafficking as potential therapeutics. Here, we review the recent advances in understanding the molecular mechanisms of the early endosome-to-Golgi transport of STx, STx1, and STx2, as well as the development of small-molecule inhibitors of toxin trafficking that act at the endosome/Golgi interface.  
early endosome-to-Golgi trafficking; late endosomes; lysosomes; HOPS (homotypic fusion and vacuole protein sorting); autophagy; small-molecule inhibitor; Golgi phosphoprotein 4 (GPP130); tamoxifen; manganese  
We review here recent advances in understanding the molecular mechanisms of the early endosome-to-Golgi transport of Shiga toxins, as well as small-molecule inhibitors that target this process.
## 3 Results  
### Radiation-Shielding Yarn  
The properties of the yarns produced using bismuth oxide and barium sulfate, which are radiation-shielding materials, are shown in Figure 5. It can be seen that the particles of shielding material contained in the yarns are very small and are distributed within the yarns. Therefore, the fibers can be woven while properly maintaining the flexibility and strength of the yarn. In addition, in order to show the shielding performance, the best result was obtained with 5 wt% of the shielding material in the yarn.  
The characteristics of the yarns woven using this process are listed in Table 2. The tensile strength of the yarns containing barium sulfate and bismuth oxide was 3.14 g/d and 4.11 g/d, respectively, which are suitable values for weaving radiation-shielding fibers; the yarn with the bismuth oxide had the higher value. The measured value of elongation at break was higher than that of a typical textile intended for use as a general clothing material; this is because of its high mineral content. In addition, in the twisting process preparatory to the weaving process, weaving was performed after processing under low twist-per-meter (TPM) conditions, and the problem of yarn breakage could be prevented.  
Figure 5: Radiation-shielding fiber yarn analysis: (**a**) results of energy-dispersive X-ray spectroscopy (EDS) analysis of yarn components, (**b**) cross-section SEM images (400\(\times\)), and (**c**) surface SEM images (600\(\times\)).
## 2 Identification of Abnormal Stations and Estimation of Influence Intensity
### Influence Intensity of Passenger Flow at Stations  
After determining the stations affected by the disruption, the law of propagation during the disruption needs to be further analyzed. Therefore, it is necessary to calculate the intensity of the impact on the tap-in passenger flow at abnormal stations due to the disruption (the influence intensity for short) during different time periods. Because the passenger flow data of each station are collected and uploaded every 15 min by the AFC system, many studies have used a period of 15 min to count or predict passenger flow in urban rail transit systems [6; 21; 34; 35; 36]. In this study, the tap-in passenger flow for a period of 15 min at each station was counted.  
Let \(i\) denote a station and \(j\) denote a 15 min period. \(R_{ij}\) represents the influence intensity of the tap-in passenger flow for time period \(j\) at station \(i\) affected by the disruption. \(x_{ij}\) is the tap-in passenger flow for time period \(j\) at station \(i\).  
As described in Section 2.1, the passenger flow at each station fluctuates within a range during normal operation. The lower bound and upper bound of the tap-in passenger flow for a certain period at a certain station can be calculated by Equation (1), which can be represented by \(\lambda_{ij}^{min}\) and \(\lambda_{ij}^{max}\), respectively. When a station is affected by a disruption, the exact amount of passenger flow during normal operation cannot be determined. It may be equal to any value within the fluctuation range. Therefore, the influence intensity of the tap-in passenger flow should be within a certain range \([R_{ij}^{min},R_{ij}^{max}]\), which can be calculated by:  
\[R_{ij}^{\max} =\frac{\left|x_{ij}-\lambda_{ij}^{\max}\right|}{\lambda_{ij}^{ \max}},\forall i\in N,j\in J \tag{2}\] \[R_{ij}^{\min} =\frac{\left|x_{ij}-\lambda_{ij}^{\min}\right|}{\lambda_{ij}^{ \min}},\forall i\in N,j\in J \tag{3}\]  
where \(N\) denotes the set of stations, and \(J\) denotes the set of time periods.  
To enhance the readability and summarize the method, a flow chart of the proposed method is illustrated in Figure 1.
### Existing Research and Limitations
#### 1.2.2 EV Charging Demand Prediction  
Traditional models for predicting EV charging demand include energy consumption models that consider battery characteristics, probabilistic methods based on driving patterns, and spatio-temporal estimation for multi-charging station usage. However, these models typically rely on centralized learning approaches, which pose privacy challenges and are susceptible to data imbalance and non-IID data distributions.  
Recent studies have introduced federated learning (FL) to address privacy issues and data imbalance among vehicles. However, existing FL models, such as FedAvg, are vulnerable to unreliable participants, leading to the potential degradation of the global model.  
To address these limitations, this study proposes the Federated Learning with Qualified Local Model Selection (FL-QLMS) algorithm. FL-QLMS uses a trusted validation dataset to evaluate the performance of each vehicle's local model and selectively incorporates only the top-performing models into the global model update. This approach minimizes the impact of outlier data and dishonest participation, ensuring robust prediction performance, even in non-IID environments.
# Boosting the Transformer with the BERT Supervision in Low-Resource Machine Translation
## 4 Experimental Setting  
### Data and Metric  
In the experiments, we evaluate our approach on six low-resource translation tasks, including German\(\rightarrow\)English (De\(\rightarrow\)En), Romanian\(\rightarrow\)English (Ro\(\rightarrow\)En), Vietnamese\(\rightarrow\)English (Vi\(\rightarrow\)En), German\(\rightarrow\)Chinese (De\(\rightarrow\)Zh), Korean\(\rightarrow\)Chinese (Ko\(\rightarrow\)Zh), and Russian\(\rightarrow\)Chinese (Ru\(\rightarrow\)Zh). The first three tasks (the target language is English) are IWSLT' 14 De\(\rightarrow\)En, WMT' 2016 Ro\(\rightarrow\)En, and IWSLT' 14 Vi\(\rightarrow\)En tasks, which had 160\(K\), 130\(K\), and 600\(K\) parallelsentences in training data sets, respectively. We use _tst_2010, _tst_2011, and _tst_2012 as the test set for De\(\rightarrow\)En translation, _newstest_2016 as the test set for Ro\(\rightarrow\)En translation, and _tst_2014 as the test set for Vi\(\rightarrow\)En translation, respectively. The last three tasks (the target language is Chinese) use the TED parallel corpus [36] containing De\(\rightarrow\)Zh, Ko\(\rightarrow\)Zh, and Ru\(\rightarrow\)Zh. The three training sets contain 140\(K\), 160\(K\), and 130\(K\) sentence pairs, respectively. Each of the three testing sets contains 3000 sentences different from that in the training sets.  
The evaluation metric is case-insensitive BLEU calculated by the multi-bleu.perl script [41]. BLEU is  
\[\text{BLEU}=\text{BP}\times\exp\left(\sum_{n=1}^{N}w_{n}p_{n}\right) \tag{6}\]  
\[\text{BP}=\left\{\begin{array}{l}1,\quad c>r\\ e^{1-r/c},c\leq r\end{array}\right. \tag{7}\]  
where \(r\) is the source sentence, \(c\) is the translation result, \(N\) is the item number of N-gram, \(w_{n}\) is the coefficient of N-gram, and \(p_{n}\) is the precision of N-gram matching.
# Reliability Enhancement Driven by ANN for Lighting Control System in Highway Tunnels
## 2 Research Status of Tunnel Lighting System
### Influences of Color Temperature on Driver Behavior  
#### 2.5.1 Necessity of Considering the Influences of Color Temperature  
In the study of light source characteristics, there are no relevant regulations and standards for the color temperature, color rendering and spectrum of tunnel lighting in various countries.  
In 2002, [PERSON] et al. of Brown University discovered the third type of photoreceptor cells in the retina, these nerve cells have different sensitivities to various light wavelengths and peak value is located at 490 nm, this is called citopic vision. [PERSON] found that lighting color temperature has obvious physiological effects [27]. Studies show that the photobiological effect is sensitive to light of 490 nm wavelength and the corresponding maximum light efficiency is 3850 lm/W. The photobiological effect is closely related to the degree of blue component included in the light source, the relationship between light spectrum and visions are shown in Figure 4[28; 29]. Additionally, [PERSON] found that human visual acuity depends much on the color temperature of the surrounding lighting, and visual acuity is best when the lighting spectrum has a high color temperature [30].  
Based on these research results, the use of lighting driven by light resources with high color temperature will be of great benefit in improving driver visual acuity and reducing lighting energy consumption simultaneously. In order to ensure the safety, comfort and high speed of tunnel driving, it is important to consider the effect of spectral distribution of tunnel lighting on driver vision.  
Multiple research teams have carried out many experiments on the properties of LED lights sources, especially on the relationship between luminance level, color temperature, and CRI with driver recognition on target objects. Two typical datasets are shown in Figures 5 and 6.  
1. With the increase in luminance level and CRI of light sources, driver visual performance relating to target objects is improved, which proves that in practical engineering it is an effective energy-saving method to enhance the CRI of light sources, increasing visual visibility without increasing the power of the light sources.
2. Given a certain luminance level, yellow light sources with low color temperature 3000-4000 K help to increase the photochromic contrast between target objects and background more than white light sources with high color temperature 5000-6500 K. To some degree, this enhances driver visual acuity and visual performance, and eventually contributes to recognizing target objects more easily.  
Figure 4: Photopic, isotopic and citopic.  
Figure 5: Recognition time with CRI and color temperature (luminance: 2 cd/m\({}^{2}\)).
# A Systematic Literature Review on Military Software Defined Networks
## 4 Identified Significant Research Contributions
### Services  
Three articles have a research contribution related to services, and all have a scope related to design and management. In [25], the author proposes an SDN based Network for auto-configuring network services across federated mission networks. Currently, NATO is using standard routing protocols to exchange information about both routes and services. The author suggests interconnecting OpenFlow controllers instead of using routing protocols, while the design and experimental evaluation showed a faster auto deployment of network services using SDN compared with BGP. The article is comprehensive, although not all the requirements of the design become clear. As an example, it is not known how the network controllers exchange information, and what information they have to be preconfigured with. Furthermore, [PERSON] [27] discussed opportunities for applying assurance-driven design to validate the correctness of behavioral requirements for network capability insertion in the Army's network, which is becoming increasingly virtualized with extended variation in the type and number of network resources. The author utilized the CertWare tool in order to automate assurance cases, and provided an example using the claim-argument-evidence (CAE) method in the CertWare tool in order to validate the claim. Finally, [PERSON] et al. [54] proposed an SDN approach for improving the quality of video streaming for military surveillance, in which multiple Unmanned Aerial Vehicles (UAVs) are employed as data providers through an SDN-enabled network. Experiments were performed considering the application of SDN in UAV-based military surveillance scenarios using Mininet-WiFi, Ryu-SDN Framework and the FFmpeg player. A SDN was used for connecting the ground vehicles, using the SDN architecture to enhance video streaming in dynamic networks with a link capacity of 100 Mbps.
## 4 The Accurate and Stable MDTF Fault Identification Framework and Its Experimental Evaluation  
The key aim of this study was to identify defect types of MDTF gearbox systems under variable speed conditions. As mentioned in Section 1, it has been observed that the existing models might not be able to differentiate those fault types due to the similar behavior of different degrees of tooth fault reflected in the vibration spectrum. To address this issue, in this paper, a new gearbox fault diagnosis scheme has been proposed. Figure 6 provides a block diagram of the proposed framework. From Figure 6, it can be seen that the proposed method consists of four main steps: (1) sensors and data acquisition (DAQ), (2) LADT, (3) WVI, and (4) DCNA. The preliminary section covered the main steps of the proposed method. This section will provide the experimental validation of the proposed method.
The variation in temperature during PEF treatment depends mainly on the specific energy input (\(W\)s = kJ/kg) used in the protocol [28; 29]. This nonthermal effect of PEF is due to the short duration of the electrical pulses, usually in the microsecond (\(\upmu\)s) range, and the brief duration of the treatment, typically lasting only a few milliseconds. The electrical energy is delivered in a disruptive way during PEF treatment, and it is not sufficient to cause significant heating of the food product, in contrast to traditional thermal processing methods [30]. Bearing this in mind, in this assay, the treatment applied does not significantly increase the temperature of the must (\(\Delta T<1\)\({}^{\circ}\)C). The approval of PEF as the pretreatment of musts by OIV was achieved in 2020, being included in the _Code of Oenological Practices_ (Resolution OIV-OENO 634-2020) as the consequent corollary of extensive work realized by several authors, both at the laboratory and pilot-plant scale (e.g., FieldFood Project under Horizon 2020, grant no. 635632). However, a more industrial type of approach is needed, allowing the consolidation and proof-check of the feasibility and scalability needed in order to provide security and trust to industry peers. Although the application of PEF as a pretreatment and its impact on wine characteristics (either sensorial or physico-chemical) on fermentation dynamics, yield and the final product have been considerably studied, studies mainly focused on red wine vinification given the importance of phenolic substances on the quality parameters of red wine [7; 16]. Thus, currently, there is a lacuna regarding the investigation of PEFs on white grape varieties, with a small number of publications regarding this topic [8; 31; 32; 33], a few of them performed at a pilot-plant scale and even fewer comprising the assessment of the yield extraction optimization of _cuvee_ (free run and low pressure, associated with high-quality wines) or total must volume. Furthermore, it is crucial to compare this innovative tool with other commonly used biotechnologies in winemaking, such as enzymes, to identify potential similarities and differences in achieving the same objectives. Additionally, our search of relevant databases and scientific journals revealed a lack of information regarding the volume of wine lees produced by using different methodologies to increase must yields. The aim of this study is to investigate the effects of pulsed electric field (PEF) treatment during the vinicification of the Portuguese white grape variety Arinto at a pilot-plant scale, i.e., processing 2 T with a flow of 4 T/h. Specifically, we aim to compare the use of PEF treatment with the traditional use of enzymes during alcoholic fermentation and 3 months after vinicification in terms of various parameters, including total phenols (TP); color intensity (CI); yellow (%Ye), red (%Re) and blue (%Bl) color components; the yield of _cuvee_ juice, wine and lees; volatile acidity; turbidity; pH; total acidity; total dry extract; and tartaric stability (mini-contact test).
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 5 Conclusions  
Based on multi-source data, this study analyzed the grassland degradation on TP in terms of grassland area and grassland quality. Then, the grassland degradation information was verified with 279 grassland observation degradation points. During 2000-2020, the grassland area on the TP expanded, while the rate of grassland degradation exceeded the rate of greening during 2010-2020. Grassland quality degradation preceded grassland area degradation and was more pronounced. Grassland degradation was more severe in typical pastoralist prefectures than in non-pastoralist regions on the TP. The consistency between the observed grassland degradation points and the six vegetation indexes was 56.63% on the TP, and SIF was more effective than other vegetation indexes in monitoring grassland degradation. In summary, this study accurately obtained the spatial and temporal distribution of grassland area and quality changes and verified the consistency of the six indicators on the TP, providing a reference for the study of grassland degradation on the TP.  
Conceptualization, S.W. and L.J.; methodology, S.W., Y.W. and L.C.; validation, T.Z. and D.F.; data curation, S.W.; writing--original draft preparation, L.J. and S.W.; writing--review and editing, L.J., S.W. and A.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Strategic Priority Research Program of the Second Tibetan Plateau Scientific Expedition and Research Program (No. 2019 QZKK0405).  
Data Availability Statement: Data sharing not applicable.  
The author thanks [PERSON] for his assistance with conceptualization and [PERSON] for his assistance with data acquisition.  
The authors declare no conflict of interest.
# Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete
## 4 The Application of Machine Learning in Nano-Modified Concrete
### Random Forest (RF) Algorithm  
The advantage of RF is that training can be highly parallelized and there is an improvement in training speed for samples with large capacity. After training, the importance of each feature for the output can be given, and due to the use of random sampling, the variance of the trained model is small and the generalization ability is high.  
[PERSON] et al. [22] used an RF-based integration method to combine to form a powerful learner. The overall accuracy will be improved by incorporating the weak learner with the smallest prediction error and the strongest correlation, where the best performing RF with an R\({}^{2}\) of 0.97, and a new expression for predicting the 28-day CS of nanocomposites doped with silica content based on the database was developed. RF in predicting the CS of concrete modified with nano-silica and supplemental cementitious materials, performed well in terms of accuracy, which means that in the future, savings can be made in the expenditures related to experimental evaluations [23]. [PERSON] et al. [57] utilized experimental studies and ML techniques to evaluate the CS and resistivity of graphene nanosheet concrete. A Random Forest Regression (RFR) machine learning method was developed using eight input variables, but the RFR model can be used as a reliable predictor without providing a mathematical expression.