esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experiment Site and Data Collection
#### 2.1.3 Canopy Parameters and Canopy Height Measurement  
The plant area index (_PAI_) and clumping index (\(\Omega\)) were measured using an LI-COR 2200 canopy analyzer. The leaf area index (_LAI_) was extracted from the _PAI_ using an empirical method proposed by [47] (see Supplementary Material Section S4). Canopy height (\(h_{c}\)) was measured using a tape measure. _PAI_, \(\Omega\) and \(h_{c}\) were all measured at fixed positions in the paddy field. There were 39 sampling positions in 2022 and 30 in 2023, respectively.
## 3 Results
### Plant Extracts and Plant-derived Compounds  
In a set of eleven studies [45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55] determining an activity and morphological effect of plant extracts and plant-derived compounds on _H. pylori_, four of them reported a bacteriostatic activity [45; 46; 47; 48] and six indicated a bactericidal effect [49; 50; 51; 52; 53; 54] of the tested compounds. In one case, the use of this classification was avoided [55]. In almost all (10 out of 11) basic techniques, microscopy and culture methods, were used. Only the one article has been expanded to include a number of fluorescence measurement techniques [55] (Table 2 and Figure 2).  
Among the four studies [45; 46; 47; 48] suggesting the bacteriostatic effect on _H. pylori_, in two a dominance of spherical forms was noted [46; 48], in one the stimulation was low (33% after 48 h) [45], while in the last the production of spherical forms was not observed [47]. From these, an ability of the substance to completely reduce the viability was only demonstrated in the [PERSON] et al. study [46], which took place after 6 h and 9 h for 2\(\times\) MBC and MBC, respectively.  
The six studies indicated the bactericidal effect of the tested compounds on _H. pylori_[49; 50; 51; 52; 53; 54]. A capacity to completely inhibit growth was only proven in half of them [49; 51; 52], which was achieved in a range of 18 h [49] to 72 h [51] with MICs. A relationship between the decrease in viability and number of spherical forms was particularly well illustrated in the [PERSON] et al. article [51]. Here, a 48-h incubation with MIC (a 2 log viability decrease) and 2\(\times\) MIC (a total viability reduction) of dehydrodocostus lactone from _Magnolia sieboldii_ leaves induced the formation of 49% and 94% of these morphological forms, respectively. In the remaining three articles [50; 53; 54], the decrease in viability of treated cells was different and ranged from 1 log after 6-h exposure to 2\(\times\) MIC [53] to 3-4 logs after 12 h with a fractional inhibitory concentration (FIC) of the tested compounds [54]. In the case of two articles [53; 54], an ability of the tested substances to induce cell destruction and create forms with degradative properties was indicated.  
Authors of the last analyzed article [55] interpreted the obtained results in the most careful manner, indicating that \"daphnetin has a potential to be an effective anti-_H. pylori_ agent\", despite the use of several techniques to determine the bacterial viability. Microorganisms treated with \(\frac{1}{2}\times\) MIC of daphnetin after 72 h had many morphobstructural changes when observed by a scanning electron microscopy (SEM), but these observations were not confirmed using fluorescent techniques. However, it was recognized that DNA damages were induced.  
Figure 1: Diagram presenting the activity of classically used antibiotics and proton pump inhibitors (PPIs) against _Helicobacter pylori_.
# Inter-Data Center RDMA: Challenges, Status, and Future Directions
## 4 Existing Representative Works
### Swing  
SWING [19] introduces a PFC-relay mechanism to enable efficient lossless RDMA across long-distance DCI, addressing the critical challenge of PFC-induced throughput loss and excessive buffer requirements in traditional solutions. The core innovation lies in deploying relay devices at both ends of the DCI link, which act as intermediaries to dynamically relay PFC messages between DCs. Unlike native PFC, where long propagation delays force switches to reserve buffers equivalent to 2 \(\times\) BDP to avoid throughput loss, SWING's relays establish a semi-duplex feedback loop: they mimic the local switch's draining rate and propagate PFC signals in a time-synchronized manner, reducing the required buffer to just one BDP per direction. This mechanism ensures that upstream and downstream switches receive timely congestion feedback, eliminating the idle periods caused by delayed RESUME messages in traditional PFC.  
As demonstrated in Figure 3, the relay device intercepts PFC signals from the local DC switch, forwards them across the long-distance link with minimal processing delay, and enforces a cyclic sending rate matching the remote switch's capacity. This creates a \"swing\" effect where traffic is paced to the network's draining rate, preventing buffer overflow  
Figure 2: The impact of latency and packet loss on RDMA performance.  
while maintaining full link utilization. Critically, SWING achieves this without modifying intra-DC networks or RDMA protocols, retaining full compatibility with existing RoCEv2 infrastructure. The authors evaluate SWING by conducting experiments in various traffic scenarios to measure its performance and comparing it with the original PFC solution. The results show that SWING can reduce the average FCT by 14% to 66%, leveraging half the buffer of traditional deep-buffer solutions while eliminating HoL blocking and PFC storm issues. By focusing on protocol-layer relay rather than hardware upgrades, SWING offers a pragmatic solution to extend lossless RDM's benefits to inter-DC links with minimal infrastructure changes.
# Server Operation and Virtualization to Save Energy and Cost in Future Sustainable Computing
## 4 Discussion  
The price of the existing EPC (Evolved Packet Core) equipment is 1.8 billion Korean won per unit (KRW). Since there are expectations of reducing the equipment purchase price and installation price using NFV virtualization technology, the three major telecommunication service providers in Korea prepared the vEPC (Virtual Evolved Packet Core). In particular, SK Telecom commercialized the vEPC and network resource management system. KT developed vEPC for 5G in cooperation with Alcatel-Lucent. LGU+ started core equipment virtualization with global network companies such as Affirmed Networks.  
If any, how much savings are expected for the telecommunication service providers who introduce NFV virtualization? It was compared with the previous price structure as below. As for the EPC produced by a network manufacturer, hardware, OS, middleware, and application software are all-in-one as shown on the left. For vEPC on the right; however, the server will be bought from HP, and vEPC software will be purchased from the software manufacturer separately.  
Figure 11 shows a feature of vEPC that is changed by applying NFV virtualization. In the case of the existing EPC, the telecommunication service provider purchased hardware and software manufactured by the dedicated network equipment manufacturer. In vEPC, however, hardware and software were separated. The service provider bought hardware from the manufacturer of compatible hardware but purchased software from the manufacturer of the existing network equipment. Figure 12 shows the vEPC construction designed in the paper.  
Compared with the existing technology, my new content proposes a vEPC suitable for Korean data centers to reduce costs. Also, by developing an Android application which can effectively manage servers, the author allowed the server administrators to reduce energy consumption conveniently.
# Optimization of Microalgae-Bacteria Consortium in the Treatment of Paper Pulp Wastewater
## 2 Materials and Methods
### Configuration of Photobioreactors  
The operational setup consisted of six photobioreactors (PBR), with different microalgae and aerobic sludge inoculum ratios. To serve as a control, two photobioreactors were inoculated with only aerobic sludge and only microalgae. The experiment lasted for 14 days. [PERSON] et al. [26] suggest an initial concentration of VSS of 100 mg/L. The six photobioreactors were inoculated with the ratios of microalgae and activated sludge (MA:AS) presented in Table 1 and afterwards were filled with wastewater from the paper pulp industry with a total working volume of 1 L.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**PBRs** & **Ratio of MA:AS** & \
\begin{tabular}{c} **Initial Concentration** \\ **of Microalgae (mg/L)** \\ \end{tabular} &
\begin{tabular}{c} **Initial Concentration of** \\ **Aerobic Sludge (mg/L)** \\ \end{tabular} \\ \hline PBR1 & 1:0 & 100 & - \\ PBR2 & 0:1 & - & 100 \\ PBR3 & 1:1 & 100 & 100 \\ PBR4 & 1:3 & 100 & 300 \\ PBR5 & 1:5 & 100 & 500 \\ PBR6 & 3:1 & 300 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the initial inoculation concentration of photobioreactors.  
Figure 1: Schematic representation of the experimental setup.  
A constant air injection of 2 L/min was used to aerate all photobioreactors. The PBRs were kept in continuous agitation with a magnetic stirrer at 100 rpm during the experiment to avoid biomass sedimentation. The average temperature was kept at 25 \(\pm\) 2 \({}^{\circ}\)C, with a light and dark cycle of 12 h:12 h and a radiation of 200 \(\upmu\)mol/m\({}^{2}\).s, all while using an LED intensity-adjustable system based on the LED MAXLED Pro (Fajozes, Portugal). A similar strategy was used by [PERSON] et al. [27] to evaluate the synergistic effects between microalgae and bacteria. It is important to mention that these operational factors have a direct impact on the system's balance and overall efficiency. During the operation of the PBRs, the pH was monitored daily and continuously adjusted to around 7.5 \(\pm\) 0.5, using a 1M HCL solution to maintain the system equilibrium.
###### Abstract  
Because both the inner and outer rings rotate, the intershaft bearings used in gas turbines do not have fixed bearing housings. As a result, the vibration of intershaft bearings cannot be measured directly. Therefore, a vibration signal can only be collected through indirect measurement. First, it must be transferred to adjacent bearings through the shafting. Then, it should be transferred by the elastic supports and complex structure of the thin-walled strut. The vibration signal is severely weakened during transmission under the influences of the transfer path. In the meantime, in the vibration of other components, a huge amount of noise is produced by the air flow, and the variable speeds of the inner and outer rings of the intershaft bearings make it harder to analyze the signal. Hence, it is very difficult to extract the vibration fault features of intershaft bearings. To deal with the variable speed of dual rotors, as well as the weak signal, a fault feature extraction scheme for the weak fault signals of intershaft bearings is proposed in this paper. This scheme is based on linear prediction, spectral kurtosis, and order tracking in the rotation speed difference domain. First, a prewhitening process, based on linear prediction, is applied to the fault signal of the intershaft bearings to eliminate the stationary component. Thus, the remaining components, including the impulse signal of faulty bearings and nonstationary noise, can retain the features of the vibrational bearings, in addition to reducing the noise. Second, the optimal center frequency and bandwidth of the band-pass filter, applied to resonant demodulation, are selected by spectral kurtosis. Subsequently, the enveloped signal containing the features of the faults found in the intershaft bearings is obtained by resonance demodulation. The quasi-stationary signal in the angle domain is acquired by the even angle resampling of the nonstationary envelope signal, as a result of the variable speed. The final order spectrum is obtained through a Fourier transform. Fault diagnosis can be conducted for the intershaft bearings by comparing this spectrum with the feature order of the bearing fault. Experiments were conducted to verify the validity of the proposed scheme.  
fault diagnosis; intershaft bearing; weak fault feature extraction; rotation speed difference domain; linear prediction; order tracking
## 2 Background and Related Works  
### EPCglobal Network  
We developed the RFID system based on a de-facto standard--EPCglobal standards, so that the system can be easily applied to anonymous supply chain partners. The EPCglobal network is a network of service providers (as subscribers) that follow the EPCglobal architecture framework (Figure 1) on the Internet and the subscribers that use the services specified in the framework. It is a network system that can query the product information, history information, and movement status by automatically recognizing objects using RFID technology and sharing the recognized object information through the Internet. EPC stands for Electronic Produce Code and refers to the standard RFID code system designed by GS1 which is a not-for-profit organisation that develops and maintains global standards for business communication [6].  
The EPCglobal architecture framework comprises three core information services: EPC Information Services (EPCIS), Object Naming Services (ONS), and Discovery Services (DS). These services can be used to secure item traceability and supply chain visibility. A brief description of each service is given below [7].  
* EPCIS is a repository of RFID events. It stores the RFID event information in the site and shares the RFID event information between partners. It includes the capture interface for RFID event repository and the query interface for queries; the implementation of repository is vendor-independent.
* ONS provides one or more uniform resource identifiers (URIs) related to EPC, and thus additional information can be acquired through the provided URIs. The provided URIs include the information systems that can provide additional information (product name and manufacture date) of certain EPC or EPCIS and DS for object tracking.
* DS is a service that stores information for the movement of objects among sites. DS stores and shares receiving/shipping events in each site, whereas EPCIS stores the information of RFID events at a site. At present, the development of standards for DS in the concept stage is ongoing.  
Figure 1: EPCglobal architecture framework (edited from [6]).
# Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
## 1 Introduction
The main contribution of the present research paper is the definition of a centralized fuzzy controlled clustering algorithm ruled by three stages, as a result of a PSO optimization process or CFC3 PSO. In each of the aforementioned stages, a different knowledge base is used, which, as mentioned above, is the product of an optimization process. Accordingto our previous research, the application of three different KBs, which are the result of an incremental optimization performed by a PSO system, represents a certain innovation in the field of clustering. Therefore, the reason for using the presented optimization process to improve the performance of a WSN in its three phases of operation (up to FND, from FND to HND, and from HND to LND), taking into account three possible deployments of the base station, was because we wanted to achieve a clustering algorithm that can adapt to different deployments of the WSN, without having to make adjustments. Thus, the knowledge bases used in the fuzzy system for the selection of the CHs are prepared for situations in which the BS is inside the deployment area, as well as if the BS is in its vicinity, or even far from it. Consequently, this clustering method was developed using a optimization process based on a PSO that detects which rules of the entire knowledge base are meaningful for the clustering method in order to maximize the value of one of the three parameters (FND, HND, and LND). Moreover, as a consequence of the optimization process, the processing in the BS can be faster, because less relevant rules can be omitted from the fuzzy control system. Thus, after the optimization process, each rule is assigned a weight for one of the performance metrics (FND, HND, and LND). If the WSN application needs, for example, a highest LND, only the set of fuzzy rules with high weights for that parameter are chosen, while the rules with low weight values will barely affect that application and, consequently, they will be removed. In addition, the optimization process can detect those rules that are not meaningful for maximizing any of the three parameters, which helps with the analysis of the design process and can result in modifications that improve the system, such as removing, adding, or tuning fuzzy sets; suppressing or adding fuzzy variables; etc. This paper is organized as follows: Section 2 presents the optimization algorithm for the rule base in a clustering application for WSNs. Then, in Section 3, the results of the proposed algorithm are shown within a specific scenario, while, in Section 4, we discuss how to interpret the final rule base and the weights obtained for the rules. Finally, the paper ends with Section 5, where the conclusions of the present work are presented.
# GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation
## 3 Experimental Analysis and Method
### Model Training
When DT reaches a depth limit or a limit on the number of leaf nodes, the residuals are fitted by assigning leaf node weights to fit this time. After training, the LightGBM model for vehicle position prediction in the event of a GNSS outage can be updated, and the process can be repeated until \(n\) regression decision trees are constructed. Following this process, the final LightGBM model for vehicle location prediction in the event of a GNSS outage was generated. In Figure 3, \(f_{i}(x)\) is the output of DT\({}_{i}\). The final output of the LightGBM model for vehicle position prediction in the event of a GNSS outage is \(\sum_{i=1}^{n}f_{i}(x)\), which is the prediction of the differences in longitude (\(\delta E\)) or latitude (\(\delta N\)).
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 4 Experiment
### Evaluation Index and Parameter Setting  
#### 4.2.1 Evaluation Index  
The evaluation of sentence representation in this paper is mainly divided into the objective evaluation and subjective evaluation. The higher the correlation, the closer the representation content is to the original meaning of the sentence.  
The objective evaluation uses the accuracy index to evaluate the performance of the model. The formula of accuracy is as follows:  
\[Accuracy=\frac{N_{t}}{N_{all}} \tag{14}\]  
where \(N_{t}\) is the number of samples with a correct prediction, \(N_{p}\) is the number of samples consistent with the target labels in the prediction results, and the total number of samples is \(N_{all}\).
## 3 Materials and Methods
### Position Algorithm Calculation
#### 3.1.1 Step 1: Calibrated Logarithmic Propagation Model  
The RSSI location is a common method in the process of wireless location [45]. The RSSI varies with distance, so the distance can be estimated according to signal attenuation, which is a direct and simple ranging method. The main concern in RSSI positioning is the distance propagation model. The path loss in the process of signal propagation directly affects the ranging results, which then affects the positioning accuracy. Therefore, a reasonable communication model could be crucial. In this article, the logarithmic distance propagation model is selected as the distance propagation model; it has been widely used in vehicle wireless positioning. The distance between nodes, calculated by this model, is more consistent with the distance between real nodes, which can effectively improve the accuracy of vehicle positioning.  
The logarithmic propagation model refers to the logarithmic attenuation trend of the average received signal power with the increase in distance when the wireless signal propagates indoors or outdoors. In the model, the distance \(d\) between the RSSI and the nodes varies logarithmically, so the path loss \(PL\) meets the following requirements:  
\[PL=PL(d_{0})+10n\lg(\frac{d}{d_{0}})-\delta \tag{1}\]  
where \(PL(d_{0})\) indicates the signal strength value received by the vehicle at the reference distance \(d_{0}\), unit: dBm, \(d_{0}\) = 1 m; \(PL\) indicates the signal strength received by the vehicle when the distance between points is \(d\), unit: dBm; \(n\) indicates the path loss coefficient, generally 2-9; and \(\delta\) indicates the Gaussian random distribution with the mean value of 0 and the variance of \(\mu\) (generally 4-10).  
In this article, the experimental data in the field vehicle road coordination environment are selected to calibrate the model. When the experimental vehicle travels around the fixed base station, the signals transmitted by the base station are collected and received by the vehicle when driving. The experiment received information that included RSSI and GPS longitude and latitude. The GPS positioning data in the experimental data have high accuracy and are complete data. Therefore, the GPS positioning track is used as the real driving track of the vehicle to calibrate the model. The GPS data and corresponding RSSI are selected at a certain time, the real distance between nodes is calculated, and the modelis calibrated. The logarithmic curve of the formula above is fitted by MATLAB software, and the fitting result is obtained:  
\[PR(\mathrm{dBm})=180.6135-10\times 8.192\mathrm{lg}d \tag{2}\]  
The model fitting formula-related parameters are shown in Table 1:  
According to the calibrated distance propagation model, when the RSSI is known, the absolute distance between the vehicle and the base station sending the signal at the current time can be calculated. Select the appropriate positioning method for position estimation and, finally, get the vehicle position.
## Materials and Methods
### Total Polyphenol Content Determination  
The total content of phenolic compounds (TPC) of the corresponding juniper extracts was determined according to the Folin-Ciocalteu method [21] with minor modifications. In brief, 20 \(\upmu\)L of the extract [5 mg/mL in 80% (_v/v_) methanol] were mixed with distilled water (1.58 mL) and FC-reagent (100 \(\upmu\)L) was added. The control sample contained the same reagents but without plant extract. After 3-5 min, 300 \(\upmu\)L of sodium carbonate (20% _w/v_) were added, and the samples were kept at room temperature for 2 h. The absorbance at 765 nm was registered on a spectrophotometer.  
The calibration curve was prepared using gallic acid standard. The TPC of the extracts was expressed in GAE (Gallic Acid Equivalents) according to the formula \(C=c.V/m\), where \(C\) is the concentration of phenolic compounds in mg GAE per gram dry extract (DE); \(c\) is the gallic acid concentration [mg/mL] from the calibration curve; \(m\) is the weight of plant extract [g]; \(V\) is the volume of plant extract [mL]. The TPC of each extract was determined by 2 independent analyses and was given as an average value \(\pm\)SD.