esa-sceva/satcom-chunk-collection huggingface dataset:
# Thai Rice Vinegars: Production and Biological Properties
###### Abstract  
Four types of traditional Thai rice--polished, black fragrant, glutinous and black glutinous rice--were separately used as raw material for vinegar production. During alcohol fermentation, using enriched baker's dried yeast (_S. cerevisiae_) as a starter culture gave the highest ethanol content over 7 days of fermentation. The conversion of ethanol to acetic acid for vinegar production by _Acetobacter pasteurianus_ TISTR 102 was performed for 25 days. The highest amount of acetic acid was detected with glutinous rice fermentation (6.68% \(w/v\)). The biological properties of Thai rice vineegars were determined, including the total phenolic content, antioxidant activity, antibacterial activity and cytotoxicity. Black glutinous rice vinegar exhibited the maximum total phenolic content of 133.68 mg GAE/100 mL. This result was related to the antioxidative activity findings, for which black glutinous rice vinegar exhibited the strongest activity against both ABTS** and DPPH** radicals. Cytotoxicity against the human colon cancer cell line (HT-29) provided an IC\({}_{50}\) value of 74.02 ug/mL and weak activity in a mouse fibroblast normal cell line (I.929) with an IC\({}_{50}\) value of 171.06 ug/mL. Glutinous rice vinegar was the most effective vinegar for inhibiting pathogenic bacterial growth of both Gram-negative and Gram-positive bacteria. These results suggested that the value of total phenolic content corresponded to the anticancer activity and antioxidant activity results, while antibacterial activity depended on the acidity of rice vinegar.  
rice vinegar; fermentation; total phenolic compounds; antibacterial activity; antioxidant activity
## 3 Characteristics of VANETs  
Since VANETs are used in so many monitoring and safety applications, they have a number of hardware and communication device characteristics [14] that affect VANET communication. The following is a list of the most significant characteristics that affect VANET communication [15]:  
* Estimate of movement: The movement of vehicles is limited by the urban structure, such as sidewalks, crossings, and roads, thereby possible vehicle activities can be predictable [16].
* Power constraints: Because every vehicle is fitted with prolonged battery life, the VANETs do not have any power limitations.
* Variable network density: Network density depends upon the traffic in roadside scenarios; in rural areas the density is low, whereas in traffic jams and highly populated urban areas, the density is high.
* Mobility: In VANETs, vehicles usually drive at high velocity. A slight delay in V2V transmission can also lead to several problems.
* Variable Network Topology: Due to the extreme mobility of vehicles, the topology of VANETs varies rapidly. This makes VANETs susceptible to attacks and the detection of malicious vehicles is difficult [17].
* Real-time restrictions: In VANETs, the communication of data has a fixed time threshold range. This is intended to provide ample time for the recipient to make determinations and take necessary actions quickly.  
Figure 3: VANET Components.  
* Processing and storage capacity: In VANETs, it is common to manage vast quantities of data between vehicles and infrastructures. Therefore, the capacity to compute and store is a daunting problem.
* Volatility: It is common for the interactions between two nodes in VANETs to arise only once because of their versatility. The links between nodes will stay within a few wireless hops for a restricted duration of time. Thus, the protection of personal contacts at VANET will be difficult to ensure.
* High processing capacity: Compared to other mobile nodes, operational vehicles can utilize much higher processing, networking, and sensing abilities [18].
* Conventional mobility: Vehicles have motions that are more convenient than traditional MANETs. Vehicles travel only on highways. From GPS technology, roadway information is available.
* Wide scale: With several participants, VANETs could span a whole road network. Its area of coverage can vary from a neighborhood to a whole town.
## 5 Case Study
### Optimization of Timetable to Address Varying Passenger Demands
In summary, the optimization model and the multi-strategy method demonstrate versatility, effectively addressing lower passenger demand (with larger headways) and higher passenger demand (with smaller headways). In particular, the use of an unpaired operation strategy can flexibly improve the allocation of train capacity. While the implementation of express/local trains can shorten travel time, it also facilitates the allocation of capacity to required stations through skip-stopping behavior. Although two case analyses were conducted using the Shanghai Suburban Railway, the TUOS-ELS, the MILP model established, and the optimization procedure designed in this paper are all applicable to urban rail transit lines that exhibit the tidal passenger flow phenomenon, such as metro lines, suburban rail lines, and tram lines.
## 4 Results and Discussion  
This section presents the results of the experiment, involving the evaluation of three segment sizes (250, 150, 50), two labeling approaches (S1 labeling and S2 labeling), two stages (movement disorder detection, movement disorder classification), and two models per stage (random forest, HARDenseRNN), encompassing a total of 24 models (\(3\times 2\times 2\times 2\)). It is important to note that this study uses recall as the primary metric for model evaluation to ensure that our models accurately identify as many anomalous segments as possible, thus ensuring accurate classification of critically anomalous segments.
# Coal Dust and Methane as a Hazard in Coal Preparation Plants
## 1 Introduction
Methane is emitted during all dry processing operations, yet crushing and grinding common in cooking coal processing plants (and to a lesser extent in thermal coal processing plants) significantly contributes to its release. Conveyor belts, especially at transfer points, also release methane from the material they transport. Other places where methane accumulates in coal processing plants are all sorts of bunkers for raw coal, concentrate, and occasionally coal tailings. The increasing depth of coal mining, which has reached an average of 780 m in Poland and can exceed 1000 m in some cases, has led to a rise in methane emissions in processing plants. The average annual increase in mining depth for Poland is 7-8 m, and the relative methane content has grown from approximately 10 m\({}^{3}\)CH\({}_{4}\)/Mg in 2003 [13] to around 15 m\({}^{3}\)CH\({}_{4}\)/Mg in 2022 [14]. To assess the risk of Explosive Atmospheres, areas are categorized into three zones: zone 0 (where Explosive Atmospheres are continuous or frequent), zone 1 (where they may occur during normal operations), and zone 2 (where they are unlikely but may persist for short periods if they do occur). The aim of this study is to analyze and compare the total coal production with coal dust explosion indicators and methane hazard at workplaces in coal preparation plants. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Dust Fraction**} & \multicolumn{4}{c}{**Silica Content in Dust \%**} \\ \cline{2-5}  & **\textless{}2** & **2 to 10** & **10 to 50** & **\textgreater{}50** \\ \hline Inhalable (\textless{}100 \(\upmu\)m) & 10 & 4 & 2 & 2 \\ Respiable (\textless{}7 \(\upmu\)m) & - & 2 & 1 & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Maximum allowable dust concentrations at workplaces, mg/m\({}^{3}\)[7]. Data were collected from all of the polish coal preparation plants from 2003 to 2022. New indexes were derived based on the yearly production and occurrence of hazard zones, which allow an assessment of the trends in relation to the total coal production.
## 3 Experimental Results
Through the network analyzer, it can be observed that after a spatial loss and with an improvement in transmission gain due to the STP, the penetration coefficient at an operating frequency of 12.5 GHz is \(-43.7\) dB for the \(-24^{\circ}\), \(-40\) dB for the \(0^{\circ}\), and \(-40.2\) dB for the \(+24^{\circ}\), as shown in Figure 13d-f, respectively. The measurement results validate (Table 1) that at an operating frequency of 12.5 GHz, the improvement is 9.1 dB for the \(-24^{\circ}\) direction, 10.1 dB for the \(0^{\circ}\) direction, and 10.4 dB for the \(+24^{\circ}\) direction. Adding the STP indeed improves losses along the path. However, there are discrepancies between actual measurement results and simulation results due to factors such as the loss incurred by splitting the board, as well as errors in controlling the distance and angle during measurements. The purpose of the STP is to address the issue of high propagation loss in high-frequency applications. First, high-gain large array antennas are used to achieve high-throughput broadband communication performance. Secondly, a Rotman lens is utilized to realize beamscanning and generate different directional main beams, thereby improving the directivity of signal reception in different directions. Lastly, a filter coupler is employed as a relay device to effectively transmit high-frequency signals propagated from outdoor to indoor environments under non-line-of-sight conditions, ultimately achieving intelligent relay control through ICs.
## 4 Characterization Framework
### Service Mesh and Sidecar Pattern  
Before the service mesh ecosystem was introduced, sidecar proxies had emerged and started to be used. Sidecar proxies encapsulate service discovery, communication protocols, load balancing, and fault tolerance mechanism to abstract them from the developer [8; 27]. With the service mesh structure built on the sidecar proxy pattern, a fully integrated service-to-service communication infrastructure is provided and the security, reliability, and observability features are managed by the platform layer [9; 28].
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
###### Abstract  
The massive development of virtualized infrastructures, Internet of Things (IoT), and Wireless Sensor Network (WSN) in recent years has led to an increase in quality requirements for the management and reliability of underlay communication networks. Existing converged networks must therefore guarantee specific quantitative and qualitative parameters of different network communication services to meet customer requirements. However, the quality of the services operated is very negatively affected by an unpredictable failure of a communication link or a network node. In such situations, communication is typically interrupted for a period that is difficult to predict, and which can lead to significant financial losses and other negative effects. Internet Protocol Fast Reroute (IP FRR) technology was developed for these reasons. The paper presents the proposal of the new Enhanced Bit Repair (EB-REP) IP FRR mechanism, which offers significant improvements over its predecessor, the B-REP mechanism. The B-REP offers protection against a single failure and only for selected critical IP flows. The EB-REP provides advanced protection against multiple failures in a protected network domain and the protection can be provided for all network flows. The EB-REP calculates alternative paths in advance based on link metrics, but also allows the construction of alternative paths independently of them. The construction of alternative FRR paths uses a standardized tunneling approach via a unique field Bit-String. Thanks to these features, EB-REP is an advanced contribution to solving IP FRR-related problems, which enables the use of EB-REP in many network deployments, but especially in network solutions that require reliable data transmission.  
rapid network recovery; IP Fast Reroute; IPFR; EB-REP; BIER; reliability +
Footnote †: journal: Applied Physics Letters
## 2 Related Work
Probabilistic models are used in [22] to model the daily habits of the elders as a two-layer tree, the first layer containing the activities, and the second layer containing the probabilities of performing an activity. The proposed approach is not able to recognize the activities that have been performed by multiple persons in the house. In [34], an approach that combines frequent pattern mining with complex event processing to extract the social routines of a person is proposed. The approach considers, as features, the time interval in which social activity takes place, as well as specific context information such as: whether it is a working day or a weekend day, whether it is a rainy or sunny day, etc. A limitation of this approach is that it is not able to provide information about the sociability level and detect other behavioral patterns (i.e., physical activity and mobility patterns). [PERSON] et al. [33] use the sensors data to build behavioral patterns that include the room's occupancy and sleep computed using a density function, house entry, and exit computed as average times and frequency in a day. A drawback of this approach is that it was tested only on two persons monitored over a period of 25 days. Analyzing the above state of the art, we did not find any relevant approaches to address the problem of daily routine detection using approximation methods such as bio-inspired heuristics. Existing solutions use classifiers or rule-based models that can identify rigid sequences of daily life activities as routines lacking flexibility and failing to detect gradual or contextual changes. They need large amounts of annotated data and the finetuning of many adjustable parameters to obtain satisfactory results. The solution proposed in this paper is based on an approximation method that successfully identifies more than one routine, capturing several variations of recurrent daily activity patterns. These routines slightly differ in terms of the sequence of activities that are performed as well as their duration. The metaheuristic-based solution is more suitable than a classifier to provide good results with smaller amounts of data as the search spaces are personalized. Moreover, we must consider that the data monitored a long time ago (for example, a year ago) may not be relevant for learning the current routine of a person as the routine may change over time. In contrast with deterministic approaches, our solution can provide good solutions in a reasonable time without going through the entire search space and features only few parameters that need to be tuned.
## 2 Background
### Principle of FP64 Computation  
According to the definition of the IEEE binary floating-point arithmetic standard (IEEE 754), the FP64 format utilizes 8 bytes for data encoding and storage, partitioned into three segments. The highest bit that expresses sign is abbreviated to S. The middle 11 bits, which express an exponent, are abbreviated as \(E\), while the remaining 52 bits express the fraction of a number, approximating the significant digits of the number, denoted as \(M\)[18; 19]. The actual value of a normalized FP64 number \(N\) can be expressed as follows:  
\[N=(-1)^{S}\times(1.M)\times 2^{E-1023} \tag{1}\]  
Based on this storing format, the execution process of FP64 multiplication can be split up into five steps, as shown in Figure 3, described as follows:  
1. Unpack the operands into the FP multiplier. The sign, exponent, and mantissa of each operand are separated. Then, the mantissa is converted to the significand by adding hidden bit 1 at the top digit.
2. XOR the sign bits of two operands to obtain the sign bits of the product.
3. Multiply the significands of two operands to obtain the product, which is the key step that restricts the overall computational speed.
4. Add the exponents of two operands. The sum is shifted with the normalization of the product.
5. Pack parts as products. Remove the hidden bit, and reassemble the sign, exponent, and mantissa of the results.  
Figure 3: The standard data format of FP64 and the regulation of FP64 multiplication.  
Figure 2: (**a**) The structure of an SRAM cell. (**b**) The structure of an SOT-MRAM cell.
## Materials and Methods
### Extraction of Depth and Surface Reflectance  
Using the PS surface reflectance composite, a normalized difference water index (NDWI) was applied to mask out terrestrial regions (i.e., [45]):  
\[NDWI=\frac{\rho(Green)-\rho(NIR)}{\rho(Green)+\rho(NIR)} \tag{1}\]  
The NIR band was then used to remove sunlight effects in the visible bands (blue, green, and red) to estimate sea surface reflectance (\(\rho_{m}(\lambda)\)) [30; 46]:  
\[\rho_{m}(\lambda)=\rho(\lambda)-\ \rho(NIR) \tag{2}\]  
Below-surface remote sensing reflectance (\(r_{rs}(\lambda)\)) was derived from sea surface reflectance (\(\rho_{m}(\lambda)\)) as [47]:  
\[r_{rs}(\lambda)=\frac{\rho_{m}(\lambda)/\pi}{0.52+1.7(\rho_{m}(\lambda)/\pi)} \tag{3}\]  
Figure 4: Final shallow area (<30 m depth) seamless, non-cloudy, four-band image composite of 38,642 PS Dove scenes for the Caribbean Basin that were acquired between 1 October 2017 and 15 September 2019 and normalized using a MODIS color target with seamless removed.  
Finally, satellite-derived bathymetry (\(H\)) was calculated from below-surface remote sensing reflectance (\(r_{rs}(\lambda)\)) using an adaptive bathymetry estimation algorithm developed for PS imagery [36]:  
\[H=m_{0}\frac{\ln(1000*r_{rs}blue)}{\ln(1000*r_{rs}green)}-m_{1} \tag{4}\]  
This algorithm measures water attenuation differences between green and blue bands to quantify the bathymetry. Both \(m_{0}\) and \(m_{1}\) were determined based on water column conditions. Previous studies using this method to create satellite-derived bathymetry have reported accuracies of RMSE = 1.22 \(-\) 1.86 m [36].
## 3 Results  
The process of identifying and selecting studies followed a systematic approach. Initially, a total of 225 records were identified through a comprehensive database search. This included both peer-reviewed articles and relevant publications related to the topic of endoscopic treatment for spondyloscitis.  
The post hoc power analysis and effect size (Cohen's d) calculations provide robust and clear evidence supporting the statistical and clinical significance of the study results. Key findings include the following:  
1. Power Analysis: All analyzed data sets demonstrated near-perfect power (e.g., 0.977 in one case), indicating almost zero probability of Type II errors. This confirms that the sample size was sufficient to detect true differences between pre- and postoperative data.
2. Effect Size ([PERSON]'s d): The results indicate very large effect sizes (e.g., d = 1.460 for [PERSON], d = 1.149 for [PERSON], and d = 3.505 for [PERSON]). These values exceed standard thresholds for interpretation, signifying substantial, clinically meaningful changes in patients' outcomes.
3. Pooled Standard Deviation (SD_pooled): The pooled standard deviations were computed as a measure of variability. For example, for [PERSON]'s data (CRP), SD_pooled = 3.93, providing a robust basis for calculating effect size and power.  
After removing 102 duplicate records, the remaining 123 records underwent screening based on their titles and abstracts. At this stage, 99 records were excluded, with 15 articles disqualified for being in non-English languages and 84 articles found to be unrelated to the scope of this review.  
Following this, 24 full-text articles were assessed for eligibility, and one article was excluded for not focusing fully on endoscopic treatment.  
Ultimately, 23 studies met the inclusion criteria and were included in the final systematic review (Figure 1). These studies exclusively focused on fully endoscopic techniques for treating spinal infections. This selection process ensured that the review maintained high relevance and focused on the most pertinent data available regarding endoscopic management of spondyloscitis.
# Multiport Energy Management System Design for a 150 kW Range-Extended Towing Vessel
## 3 Rule-Based Expert Control Strategy for the EMS of RETV
### Statement of 150 kW RETV Operation Conditions
#### 3.1.3 Cruise  
In the cruise stage, the RETV reaches and maintains peak speed with rated power. The diesel generator system is active at this stage as it can provide constant power at high speed [43; 44; 45]. When the operating speed exceeds 87% of its peak rotating speed, the RETV is regarded as working in the cruise stage. Since the battery performs poorly when discharging in the high-power demand period, the diesel generator system directly drives the PMSM alone [46; 47]. Supercapacitors address sudden power demand while transitioning to peak speed with rated power. The battery and supercapacitors may be charged due to their SoC levels. The energy flow of the EMS under cruise is shown in Figure 10.
## 2 Materials and Methods
### Processing
#### 2.5.2 Radiometric Indices  
Three different radiometric indices were extracted from each S2 image as features for the crop classification: the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), and the Bare Soil Index (BSI).  
The first two indices are widely used in studies on vegetation and are calculated using Blue (Band 2), Red (Band4), and NIR (Band8) channels of S2 (Equations (1) and (2)):  
\[\text{NDVI}=(\text{NIR}-\text{Red})/(\text{NIR}\text{Red}) \tag{1}\]  
LAI = 3.618 \(\times\) EVI \(-\) 0.118 where EVI = 2.5 \(\times\) (NIR \(-\) Red)/(NIR 6 \(\times\) Red \(-\) 7.5 \(\times\) Blue 1)  
In particular, the LAI, defined as the total one-sided area of leaf tissue per unit ground, is a biophysical indicator used to represent the dimension of the crop canopy and its variation over time. LAI measurements have been widely adopted for crop monitoring and modeling applications, being a key state variable associated with processes including light interception and the soil-crop water balance [41].  
The BSI [42] is a specific index to identify bare soil in agricultural contexts. It combines NDVI with the Normalized Difference Build-Up Index and allows better identification of exposed soil surfaces and uncultivated areas based on soil characteristics. It is calculated using Blue (Band2), Red (Band4), NIR (Band8), and SWIR (Band11) channels of S2 (Equation (3)):  
\[\text{BSI}=((\text{SWIR1}\text{ Red})-(\text{NIR}\text{ Blue}))/((\text{SWIR1} \text{ Red})+(\text{NIR}\text{ Blue})) \tag{3}\]  
NDVI and BSI provide a good way to distinguish bare soil from vegetation, while LAI and NDVI can better analyze the crop's phenological stages to distinguish various crop types.  
Then, for each crop, multi-temporal profiles of indices' mean values were included in the decision tree to properly analyze crops' seasonal behaviors.
# Improved MSRN-Based Attention Block for Mask Alignment Mark Detection in Photolithography
###### Abstract  
Wafers chips are manufactured in the semiconductor industry through various process technologies. Photolithography is one of these processes, aligning the wafer and scanning the circuit pattern on the wafer on which the photoresist film is formed by irradiating light onto the circuit pattern drawn on the mask. As semiconductor technology is highly integrated, alignment is becoming increasingly difficult due to problems such as reduction of alignment margin, transmittance due to level stacking structure, and an increase in wafer diameter in the photolithography process. Various methods and research to reduce the misalignment problem that is directly related to the yield of production are constantly being conducted. In this paper, we use machine vision for exposure equipment to improve the image resolution quality of marks for accurate alignment. To improve image resolution quality, we propose an improved Multi-Scale Residual Network (MSRN) that combines Attention Mechanism using a Multi-Scale Residual Attention Block to improve image resolution quality. Our proposed method can extract enhanced features using two different bypass networks and attention blocks with different scale convolution filters. Experiments were used to verify this method, and the performance was improved compared with previous research.  
MSRN; attention mechanism; computer vision; super-resolution; photolithography Article