esa-sceva/satcom-chunk-collection huggingface dataset:
# Getting Sugar Coating Right! The Role of the Golgi Trafficking Machinery in Glycosylation
## 3 Membrane Trafficking Machinery
### Golgi-Associated Retrograde Protein Complex (GARP)
Although the COG dysfunctions are associated with CDG, the connection between GARP function and Golgi glycosylation was discovered only recently. A case study of a 6-year-old patient carrying a mutation in VPS51 showed abnormal glycosylation testing, and her phenotype had substantial similarity with that seen in CDGs. Additionally, a significant reduction in the level of N- and O-glycosylated protein TGN46 were found in the patient's fibroblasts. Moreover, immunostaining of LAMP2, a lysosomal protein in VPS51 mutated patient fibroblasts, demonstrated enlarged lysosomes, and immunostaining of the Mannose-6 phosphate receptor showed dispersed cytoplasmic staining in the patient fibroblasts, which is in contrast to bright juxtanuclear staining in control fibroblasts [172]. Consistently, deletion of the GARP complex subunits in tissue culture cells revealed defects in N- and O-glycosylation. These defects in the GARP complex alter the total level of the _cis_-Golgi protein GPP130, _medial_-trans_-Golgi TMEM165, and the TGN-resident TGN46 Golgi proteins. In addition, lysosomal glycoprotein LAMP2, showed hypermobility on the SDS-PAGE of GARP deficient cells, indicating protein misglycosylation [143; 182]. Misglycosylation of these Golgi glycoproteins can occur as a consequence of the reduction in Golgi enzymes that are responsible for N- and O- glycosylation [9]. Indeed, the protein level of the N-glycosylation Golgi enzymes \(\alpha\)-1,3-mannosyl-glycoprotein 2-\(\beta\)-N-acetylglucosamine/myltransferase (MGAT1), B4 GALT1, ST6 GAL1 and the O-glycosylation enzyme GALNT2 was reduced in GARP subunit depleted cells. Furthermore, a retention using selective hooks (RUSH) assay showed that B4 GALT1 is not retained at the Golgi complex in GARP-KO cells but missorted to the endolysosomal system. The Golgi-resident enzyme ST6 GAL1 was also not found to be retained in the Golgi in GARP impaired cells. This study also demonstrated that this defect in Golgi enzymes only occurs in GARP-deficient cells and not in EARP-deficient cells. The malfunction, mislocalization, and reduced stabilization of Golgi enzymes associated with a glycosylation defect were found to be rescued upon expression of the missing GARP subunit. Taken together, GARP plays a crucial role in normal Golgi glycosylation by mediating the maintenance of the Golgi glycosylation machinery [182]. While neurodevelopmental disorders are common in GARP mutated patients, it is crucial to investigate and characterize the role of the GARP complex in different cell types. The mechanisms of GARP function need to be further uncovered to elucidate how GARP is involved in the misglycosylation of proteins.
# Slow Pyrolysis as a Method of Treating Household Biowaste for Biochar Production
## 3 Results and Discussion
### Biochar Yield  
The mass yield of biochar (Y\({}_{\text{m}}\)) obtained from the slow pyrolysis of the analyzed biowaste is presented in Table 2.  
Analysis of these results reveals a consistent trend across all tested biowaste types: as the pyrolysis temperature increases, the biochar yield decreases. This trend has also been observed in studies involving green waste [34], and it aligns with theoretical principles. At lower pyrolysis temperatures, carbonization reactions dominate, favoring solid char formation. As the temperature increases, devolatilization reactions intensify, leading to the release of more volatile compounds. This results in the increased production of bio-oil and gas, which were not evaluated in the present study [16].  
Among the different biowaste types studied, autumn garden biowaste consistently produced the highest biochar yields at all pyrolysis temperatures, whereas kitchen biowaste produced the lowest. However, these results must be interpreted considering the ash content in the raw materials. The autumn garden biowaste contained 43.85% ash, spring garden biowaste 17.75%, and kitchen biowaste 6.81%. Since ash is non-combustible and remains in the solid fraction post pyrolysis, it contributes to a higher biochar yield on a dry basis. Therefore, the high ash content in autumn garden biowaste partially explains its greater biochar yield.
## 5 Experimentation, Results, and Analysis
### Human Evaluation
For Oscar and AoANet, the _p_-values are below 0.05, indicating that DCAT still performs better but with a slightly lesser degree of statistical significance. These statistical results, combined with our qualitative and quantitative analyses, reinforce the superiority of the DCAT model in generating contextually rich and linguistically accurate image captions.
# Energy-Efficient and Real-Time Wearable for Wellbeing-Monitoring IoT System Based on SoC-FPGA
## 1 Introduction  
Over the last decades, wearable monitoring systems have been researched, developed and progressively enhanced to support healthcare needs and fit for real-time bio-signals processing, including heart rate measurement and emotional state recognition. As a result, wearable devices are becoming more portable, user friendly, accurate and reliable, which minimizes disturbance to the user's daily routine. Moreover, combined with access to wireless Internet, these devices are being used in remote subject monitoring. The main advantages are real-time requirements, security, privacy, diversity of services, etc. This paper proposes a wearable solution that can assist different groups of people, as it can provide remote healthcare tracking, overcoming the state-of-the-art systems.  
The system uses a ZYNQ7010 system-on-chip (SoC) field-programmable gate array (FPGA) to take advantage of high processing speed and reconfigurable logic at low cost. This kind of device is useful to create flexible and customized hardware solutions with high performance and low power consumption. It is intended to perform signal processing tasks locally and online, instead of transmitting the collected raw sensor data to be processed by an external server, as conventional systems do. By performing the computations locally, at the node, the required bandwidth and power consumption are minimized. Furthermore, this architecture offers parallel computation, which is suitable to handle multiple biometric signals at a time. Such functionalities overcome the limitations of conventional wearable solutions that use general-purpose processors. The proposed system intends to measure aperson's heart rate using photoplethysmography (PPG) and to assess the emotional state via electroencephalography (EEG). The concept of the proposed system architecture is sketched in Figure 1.  
The system has a dual-core ARM processor and a programmable logic area, where dedicated hardware cores are implemented. Both hardware and software are fully programmable, permitting to adapt the system to other applications and to upgrade already implemented applications. An I2C interface is used to communicate with the PPG digital sensor and a XADC (analog to digital converter from Xilinx) core is used to read the analog data from an ECG sensor.
# Advanced Micro/Nanocapsules for Self-Healing Coatings
###### Abstract  
The concept of intelligence has many applications, such as in coatings and cyber security. Smart coatings have the ability to sense and/or respond to external stimuli and generally interact with their environment. Self-healing coatings represent a significant advance in improving material durability and performance using microcapsules and nanocontainers loaded with self-healing agents, catalysts, corrosion inhibitors, and water-repellents. These smart coatings can repair damage on their own and restore mechanical properties without external intervention and are inspired by biological systems. Properties that are affected by either momentary or continuous external stimuli in smart coatings include corrosion, fouling, fungal, self-healing, piezoelectric, and microbiological properties. These coating properties can be obtained via combinations of either organic or inorganic polymer phases, additives, and pigments. In this article, a review of the advancements in micro/nanocapsules for self-healing coatings is reported from the aspect of extrinsic self-healing ability. The concept of extrinsic self-healing coatings is based on the use of capsules or multichannel vascular systems loaded with healing agents/inhibitors. The result is that self-healing coatings exhibit improved properties compared to traditional coatings. Self-healing anticorrosive coating not only enhances passive barrier function but also realizes active defense. As a result, there is a significant improvement in the service life and overall performance of the coating. Future research should be devoted to refining self-healing mechanisms and developing cost-effective solutions for a wide range of industrial applications.  
self-healing; coatings; capsules; corrosion; inhibitors +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Digestion Time (DT\({}_{80}\))  
Digestion time is an indicator for the efficiency of an anaerobic digestion process. For a given loading rate of biomass, the degree of fermentation completion is one of the main factors in the process of anaerobic digestion. It is not economical to run anaerobic digesesters with a long hydraulic retention time, since very little biogas is produced in the last stage. Digestion time (DT\({}_{80}\)) was defined as the number of days required to generate 80% of potential biogas [12; 51]. The co-digestions with Treat-CSA, Treat-CSB, Treat-CSC, and Treat-CSD took 40, 41, 50, and 51 days, respectively, to generate 80% of potential biogas (Figure 4). DT\({}_{80}\) of the co-digestors fed with Treat-CSA and Treat-CSB were smaller than those of digesters fed with Treat-CSC and Treat-CSD, meaning that digestion time can be shorten, if feedstock is properly treated. Although the digestors fed with swine manure only or both swine manure and RCS showed much shorter DT\({}_{80}\) (i.e., 10 and 39 d for swine manure only and mixture of swine manure and RCS, respectively) than others, they produced much less methane since most lignin and celluloses in CS were not transformed for biogas production.  
Figure 4: Digestion time (DT\({}_{80}\)) of digestion.  
Figure 3: Methane yield per gram VS of RCS and treated CSs.
## 1 Introduction
RSRP data that can trigger different TTT parameters in the actual system are collected, and used as the input of digital twins to predict the reward value under the assumed handover parameters. The virtual rewards from digital twin act on the DQN together with the actual system reward values. Compared with the ordinary DQN, it has faster convergence speed, and the final convergence effect is also better and more stable. * Digital twin with LSTM network as the core is established. The RSRP temporal series between UE and each base station before triggering the handover event is used as LSTM input to predict whether the handover failure and ping-pong occur. Unbiased estimation of handover failure rate and ping-pong rate based on this method is proposed. With the aid of LSTM-based digital twin, the enhanced DQN achieves 2.7% higher effective handover rate than the ordinary DQN, and 10.9% higher than random parameter strategy. The following paper is organized as follows. Section 2 introduces the system model and the optimization problem considered in this paper. Section 3 introduces the DQN handover parameter optimization algorithm to obtain the optimal strategy for selecting parameters according to signal fading conditions. Section 4 discusses the digital twin of the DQN, the enhanced method uses LSTM prediction as the core to build a digital twin. Section 5 gives the experimental results and analysis, and Section 6 concludes this paper.
## 4 Results and Discussion
### Classification Performance
#### 4.6.2 Particle Size Distribution  
It can be seen from Figure 17 that the particle size distribution curves (PSDCs) of the final products under the different guide cones were clearly different. Each CHL produced its own PSDC. CHL2 (\(d\) = 658 mm) had a PSDC that was significantly different from the PSDCs of the other cases. The final product under CHL2 had a larger size range, ranging from 0 to 600 \(\upmu\)m, whereas the final products in the other cases were narrower, from 0 to 400 \(\upmu\)m. The results show that the CHL could adjust the flow field to achieve the best classification efficiency for the classifier. Therefore, when the PSDC is considered as a constraint condition in producing quartz-based artificial stone, producers can choose a particular CHL structure to achieve the best classification efficiency and satisfy the required PSDC according to each specific product's formula. This provides a foundation for structural improvement to achieve the required classification performance of micron air classifiers and satisfy the required particle size distribution curve.
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 5 Performance Analysis
### Security Analysis  
Since encrypt-based technologies can be easily applied to the proposed RR-DLS method, eavesdropping attack on wireless channels between users and other entities can be ignored. We focus on collusion attack and inference attack from passive and active attackers.
# Research on an Equivalent Algorithm for Predicting Gas Content in Deep Coal Seams
## 4 Equivalent Algorithm for Predicting Coal Seam Gas Content
### Calculation Formula for Gas Content in Equivalent Layers of Coal Seams  
The outcomes of the algorithmic analysis pertaining to the residual gas content and the influence quantity of basic gas content within the aforementioned coal seam have been incorporated into Equation (17), thereby establishing the fundamental calculation formula for predicting the equivalent coal seam gas content, which can be formulated as:  
\[Q_{mh}=k_{f}K_{mh}q_{dj}+q_{mc}\]  
or  
\[Q_{mh}=k_{f}q_{dj}*\prod k_{ij}+q_{mc} \tag{18}\]  
As the equivalent calculation for predicting gas content necessitates the utilization of pertinent parameters aimed at forecasting gas emissions during the extraction of equivalent layers, in the aforementioned equation, it is essential to:  
\[q_{dj}=T_{min}\times\left(Q_{cj}+Q_{cz}\right)/A_{c} \tag{19}\]  
where \(T_{min}=1440\times 30\), min, the number 1440 is the minutes of one day, the number 30 is the monthly average days, \(Q_{cj}\) denotes the average absolute gas emission from an equivalent layer during mining, m\({}^{3}\)/min, \(Q_{cz}\) is gas increment of equivalent layer in a certain depth of mining, m\({}^{3}\)/(min\(\cdot\)100m), \(A_{c}\) denotes the average monthly production of coal seam mining face, t.  
From Equations (18) and (19), we can obtain:  
\[Q_{mh}=k_{f}\left\{T_{min}\left(Q_{cj}+Q_{cz}\right)/A_{c}\right\}\times\prod k _{ij}+q_{mc} \tag{20}\]  
where \(Q_{cz}=q_{ct}\times(H_{c}-h_{c})/100\) (m\({}^{3}\)/min), where \(q_{ct}\) denotes the gas gradient of equivalent layer extraction, m\({}^{3}\)/min.100 m, \(H_{c}\) denotes the mining depth, m; \(h_{c}\) denotes the starting depth of mining, m.  
Equation (20) represents the corresponding algorithm utilized for forecasting the gas content within coal seams. Within this formulation, all parameters, excluding the residual gas content specific to the coal seam, can leverage the pre-existing database of the equivalent layer.  
Due to the division of equivalent layers and the establishment of their respective databases, primarily centered on the distribution patterns of exploration boreholes, the geological context surrounding coal seam occurrence, and the historical documentation of gas emissions during mining operations, these endeavors are not constrained by advancements in mining engineering. Consequently, the prediction of gas content within deep coal seams,grounded on the theory of equivalent layers, transcends any limitations imposed by the pace of mining engineering progress.  
The prediction outcomes pertaining to the gas content within the corresponding strata of deep coal seams have been consolidated to ascertain the distribution pattern of gas content within these coal seams.
# Implementing a Circadian Adaptation Schedule after Eastward Flight in Young Male Athletes
## 2 Materials and Methods
### Procedures  
Upon arrival to Australia, participants were randomly assigned to a control group or an intervention group. A researcher met with each participant and provided them with study materials (i.e., urine collection container and self-report diaries). Immediately prior to each main sleep period, participants voided their urine (i.e., pre-sleep void) and provided ratings of subjective jet lag and sleepiness. Participants then collected all subsequent urine passed during the sleep period in a 2 L collection container, including a final void upon waking (i.e., post-sleep void). Participants recorded the clock times for the pre- and post-sleep void using labels on the urine container. Each morning, participants recorded their bedtime, get up time, and rated their subjective sleep quality. A researcher collected the urine containers from participants each morning and transferred a 1 mL aliquot of urine into a 1.5 mL tube (prepared with 10 mg of boric acid as a preservative; concentration = 250 g of boric acid per litre of water).  
Participants in the intervention group were given a paper schedule of when to seek or avoid light during the eight days following arrival. After an 8.5 h eastward time zone change, the human circadian system can adapt via phase advance or phase delay [3]. A phase delay schedule was chosen as it was more conducive to the timing of daylight hours for the destination time zone. The estimation of CBTmin was formulated under the assumption that participants' nocturnal sleep periods were typically from 23:00 h to 07:00 h and therefore, CBTmin will occur at approximately 04:00 h [3]. The light exposure schedule advised participants to seek light in the three hours preceding their estimated CBTmin (arrival CBTmin = 04:00 h GMT; 14:30 h ACST) and to avoid light in the three hours following their estimated CBTmin. Light blocking surpasses were provided for periods allocated to avoiding light. The estimated CBTmin for each participant was adjusted 1.5 h later each experimental day to accommodate for a phase delay (i.e., day 1 CBTmin = 14:30 h ACST; day 2 CBTmin = 16:00 h ACST; day 3 CBTmin = 18:30 h, etc., Figure 1). At 12:00 h on each day, electronic reminders (via SMS) of when to seek/avoid light were sent to participants in the intervention group. Baseline measures of overnight urine, bedtime, get up time and subjective ratings could not be collected prior to travel. Instead, baseline measures were collected approximately 30 days after arrival over a 3-day period.  
Figure 1: Light exposure protocol. White bars = period to seek bright light; black bars = period to avoid light.
## 6 Discussion
### Future Direction  
We present the future direction of research after taking into account the public questions that are mentioned above.  
1. Identifying more fine-grained behaviors of applications in tunnel is extremely important. Tunnel traffic is frequently mixed with a variety of applications and noise, and so when more and more new applications/services emerge, the question of how to classify them accurately will be extremely vital.
2. The tunnel application scenario calls for distinguishing malicious tunnel traffic from legitimate tunnel traffic, which can generally be divided into three types. In type one, normal traffic is sent by the agent, and the sending function is limited to one protocol for passengers; whereas type two contains suspicious traffic that encapsulates some  
Figure 8: Number of three challenges solved by tunnel detection.  
(any) types of passenger protocols without causing a threat; in type three, malicious traffic is transmitted by external network attackers to achieve multiple-stage behaviors within the internal network. iii. The universality of machine learning models needs to be explored further. Machine learning provides a new idea for detecting and identifying tunnel traffic, but in machine learning, selection of datasets determines how well the model will train, and feature extraction determines whether the model will be overfitted, all of which determines how well it will generalize. Semi-supervised and unsupervised learning methods may provide new ideas for the classification of unlabeled datasets. iv. Balance and exploit the capability of traditional methods and machine learning methods. Although traditional methods are gradually being omitted by more researchers, machine learning methods still fail to be implemented at scale without incurring significant collateral damage from false positives in real time and traditional methods can deal with it quickly. Therefore, for some specific situations, traditional methods do achieve better results. v. There is a need to identify which device and cluster generate tunnel traffic. As internet devices are continuously updated, different types of tunnel traffic will be generated by the same device. By generating device fingerprints based on traffic characteristics, it is difficult to identify IoT devices. In the cloud market, detecting tunnel traffic and locating the cluster from which it originates has become more challenging due to the popularity of cloud services.  
Conceptualization: Z.S. and H.S.; methodology, Z.S. and H.S.; validation, F.K. and Y.H.; investigation, F.K. and Y.H.; writing--original draft preparation, Z.S., H.S., Y.H. and G.H.; writing--review and editing, Y.H. and G.H.; project administration, Z.S. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by the National Key R&D Program of China, grant number 2019 QY1305.  
We would like to thank the anonymous referees for their helpful comments and suggestions.  
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.