esa-sceva/satcom-chunk-collection huggingface dataset:
# Compaction Characteristics and Minimum Void Ratio Prediction Model for Gap-Graded Soil-Rock Mixture
## 2 Vibrating Compaction Test
### Results and Discussion  
#### 2.2.1 Influence of gap intervals  
Figure 3 shows the change of the void ratios of the SRMs with different gap gradations, generated from the basic gradation \(D=2.3\), with gap intervals. The test results demonstrate that the influence of the absence of particles in different gap intervals on void ratios of SRMs is different. If the particles within 5-20 mm are absent, the void ratio of the SRM is smaller than that of the basic gradation, contrary to the conventional belief that gap-graded SRMs are less compatible. This is because the particles within 5-20 mm are relatively coarse and difficult to fill into the voids of the skeleton particles. The absence of the particles within 5-20 mm avoids the probability of enlarging the voids of the skeleton particles. The particles smaller than 5 mm usually act as the filling components in SRMs. The absence of these particles means that voids of the skeleton particles cannot be fully filled during vibrating compaction, so the void ratio of the SRMs increases. As the mass content of the particles within 0.25-0.5 mm is relatively fine, the absence of these particles affects slightly on the void ratio of the SRMs.  
\begin{table}
\begin{tabular}{c c} \hline \hline Fractal dimension \(D\) of basic gradation & 1.9, 2.1, 2.3, 2.5, 2.7 \\ \hline Gap interval (mm) & 5–20, 2–5, 0.5–2, 0.25–0.5 \\ \hline \(f_{c}\) for the gap interval of 5–20 mm & 0, 0.2, 0.4, 0.5, 0.6, 0.8, 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Program of vibrating compaction tests.  
Figure 2: Diagram of the vibrating compaction on soil-rock mixtures.  
Figure 4 presents the changes of non-uniform coefficients \(C_{u}\) (\(C_{u}=d_{60}/d_{10}\)) and coefficients of curvature \(C_{c}\) (\(C_{c}=d_{30}^{2}/(d_{10}d_{60})\)) of the SRMs before and after the tests at different gap intervals. It is seen that after vibrating compaction, \(C_{u}\) increases, while \(C_{c}\) decreases, especially in the absence of particles within 5-20 mm. This means the SRM gradations have changed during the vibrating compaction, resulting from the particle breakage that will be discussed later.
## 3 Remote Terminal Unit (RTU)
### Hardware Description of the RTU
#### 3.1.3 Communications Module  
For wireless communication purposes between the RTUs and BTUs, RF Xtend modules from Digi were used. These modules allow an interface to a host device through TTL levels by using a USART or through an RS-232 interface. With these modules, a mesh network topology was implemented; this allows a message to be addressed to different nodes within the network until it reaches its final destination. In case of connection losses due mainly to weather conditions, critical data might still reach its destination due to programmed capacities in the RF modules which are listed below:  
* _Self-healing:_ Any node can join or exit the network at any time without this causes a failure in the whole network.
* _P2P architecture_: No hierarchy is recognized between RF modules.
* _Acknowledgements delivery_: Remote nodes will answer when a successful communication has been established increasing reliability in transmissions.  
When a transmission starts, the following occurs:  
1. A 10 bits preamble is sent (with the configured number of retransmissions).
2. Data are sent, beginning with the header (destination address type MAC address) and ending with a 16-bit CRC.
3. Wait for an acknowledgement in the MAC layer for 1.8 ms.
4. If the ACK is received, the transmission has been accomplished successfully.
5. Otherwise a timer is initialized and when it overflows a retry will be attempted.  
All the RTUs need to transmit data to the corresponding Base Terminal Units (BTUs). This is, unique parameters related to the serial number of each terminal unit are considered: (a) Destination address constituted by high and low part (DH and DL respectively) and (b) Source address constituted by high and low part (SH and SL respectively). Making an analogy to a telephone system, the destination number (DH-DL) that the RTUs have to \"dial\" is the BTU's unique serial number (SH-SL). On the other hand, to communicate the BTU with each one of the RTUs, it will be necessary to \"dial\" the unique serial number (SH-SL) of each one of the RTUs. This address relationship is shown in Figure 3.
## 3 Issues in Implementing URLLC
### Coexistence with eMBB  
The emerging 5G network must provide services to diversified applications with different requirements. Applications relying on URLLC require low latency with high reliability, whereas eMBB requires high data rates. For the existence of URLLC and eMBB in the same physical resource, as shown in Figure 4, an efficient coexistence method is needed to maintain the required QoS. Such coexistence on the same radio spectrum will open doors to new concerns in the scheduling optimization [17].  
Figure 4: Coexistence of URLLC and eMBB. BS, base station.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Industry** & **Error Rate/Reliability** & **Latency (ms)** \\ \hline Augmented/Virtual Reality & 10\({}^{-3}\)-10\({}^{-5}\) & 5–10 \\ Autonomous/guided vehicle & \(\geq\)10\({}^{-3}\) & 5–10 \\ Automated Industry & 10\({}^{-5}\)-10\({}^{-9}\) & 1 \\ IoT (Internet of things/Tactile Internet) & 10\({}^{-5}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Expected quality of service (QoS) requirements for URLLC [7; 12].  
The proposed agile 5G frame structure [18] shows promising results for URLLC latency requirements by utilizing different transmission time intervals (TTIs) for URLLC and eMMB to meet their desired spectral efficiencies (SE). For example, URLLC traffic can be scheduled on a smaller TTI duration to achieve its low latency goal, and eMBB traffic can be scheduled with a long TTI duration to maintain its extreme SE requirements. However, such a case will bring an additional overhead to the control signaling, which can result in the degradation of the control channel (CCH) capacity [17].
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 4 The Proposed Method  
This paper introduces a critical node identification algorithm named LBIA, which represents a novel method to accurately identifying important nodes in complex networks by integrating local balance and information aggregation. The information primarily consists of two components: the PKs algorithm solves the problem of the rough identification strength in the k-shell algorithm while encapsulating the individual information of nodes. \(LB_{v}\) can solve the problem of bias in the recognition of nodes in the restricted area while encapsulating propagation information between nodes. Subsequently, the LBIA algorithm aggregates the propagation information with the individual information of the nodes to accurately identify the important nodes. In this section, the PKs algorithm, \(LB_{v}\) and LBIA algorithm are introduced.
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 3 Results
### Accuracy Assessment  
The overall accuracy and the Kappa index of the final map were 90.91% and 0.8784, respectively (Table 5). Class-specific omission errors ranged from 0 for native vegetation and cultivated pasture to 20% for sugarcane, while commission errors ranged from 0 for native vegetation to 26% for cultivated pasture. For double cropping, the omission and commission errors were 13% and 5%, respectively, while the F1-score was 0.91. In other words, from a total of 24 field validation sites of double cropping, one site was misclassified as single crop and two sites as cultivated pasture (omission error). On the other hand, one single crop field was misclassified as double crop (commission error). There was no confusion to discriminate natural vegetation, while single crop presented the highest omission error (20%). In the same way, cultivated pastures presented the highest commission error (26.3%).  
Figure 9 shows the classification confidence map of the LULC classification result for the municipality of Goiatuba. The majority of the municipality presented overall high confidence (81.0% \(\pm\) 18.8%) (Figure 10). The classification confidence was high for double cropping (87.6% \(\pm\) 16.1%) and native vegetation (88.0% \(\pm\) 16.7%) and relatively low for sugarcane (73.3% \(\pm\) 18.3%) and cultivated pasture (75.5% \(\pm\) 17.3%).
## 2 Materials and Methods
### Prebiotic Assay  
The bacterial growth was assessed according to the methods described [25], with some modifications. In brief, the following media were prepared with different concentrations of _C. vulgaris_ as the sole carbon source. Basal medium [25] was supplemented with 1.0% and 3.0% (\(w/v\)) _Chlorella_ biomass (pH 6.5). Medium without _Chorella_ biomass inoculated with _Bifidobacteria_ served as the negative control (BS-basal medium) and Wilkins-Chalgren (WCH) anaerobic broth (Oxoid, Hampshire, UK) as the positive control. Following overnight growth, bifidobacteria cultures in the exponential growth phase were separated from the medium by centrifugation (6000\(\times\)\(g\), 7 min), washed with sterile saline solution, and then resuspended at a final concentration of 10\({}^{3}\)-10\({}^{4}\) colony forming units (CFU) mL\({}^{-1}\). All media were inoculated with 1.0% (\(v/v\)) bacterial suspension and cultivated in anaerobic jars (Merck) at 37 \({}^{\circ}\)C for 24 h. The counts of tested strains at 24 h were determined using 10-fold serial dilutions and cultivated on MRS agar (Merck). To determine the concentrations of lactic and acetic acids, as the main fermentation products of _Bifidobacteria_, the isotachophoretic method was used, as previously described [21]. After cultivation, the samples were subjected to isotachophoretic separation using an EA 102 electrophoretic analyzer (Villa-Labeco, Spisska Nova Ves, Slovakia). A mixture of 10 mmol/L HCl, 22 mmol/L 6-aminobenz acid and 0.1% hydroxyethylcellulose (MERCK) was used as a leading electrolyte, and 10 mmol/L caproic acid as a terminating electrolyte. The results are values from three different measurements.
# Load Testing and Analysis of a Large Span Through Simply-Supported Steel Box Arch Bridge
## 5 Dynamic Load Test
### Test Content
#### 5.1.2 Analysis of Test Parameters  
(1) Frequency is typically determined through modal analysis to calculate the natural frequencies or resonant frequencies of the bridge structure. These frequencies represent the vibration characteristics of the bridge under specific conditions. The natural frequency of the bridge structure solely depends on its mass and stiffness, serving as a crucial indicator for changes in stiffness.  
(2) Mode Shape: Mode shapes illustrate the vibration patterns and deformation modes of the bridge at different frequencies. The mode shape diagram obtained from modal analysis can predict how the bridge will vibrate when subjected to various excitation frequencies.  
(3) Damping Ratio: The damping ratio represents the ratio between the damping coefficient and critical damping coefficient. It quantifies the normalized amount of damping in a structure and indicates how vibrations decay after excitation. The damping ratio determines how quickly energy dissipates during vibration, i.e., it governs the vibration decay rate.  
(4) Amplitude: Amplitude generally refers to the maximum displacement experienced by a vibrating bridge from its resting position. Excessive amplitude may lead to fatigue damage in structural components, potentially causing resonance phenomena.  
(5) Impact Coefficient: The impact coefficient describes the ratio between vibration amplitude and static force under an impact load condition. It characterizes the vibrational properties of bridges subjected to impact conditions, with higher values indicating poorer safety, seismic resistance, and stability.
## 1 Introduction  
In recent years, as part of the digitalization of Social Overhead Capital (SOC), the port sector is pursuing digital innovation. Unlike other facilities, port facilities need to be managed and operated for a long time once they are built, and since they are in contact with the sea, maintenance is very essential and depends on external factors. However, the port-related maintenance system still focuses on data collection, storage, and management, and it is insufficient to establish an environment that can be used or analyzed for maintenance work. In addition, port-related maintenance data exists in the form of a silo that is built once, and there is a problem that data cannot be shared due to lack of connection with other systems. In particular, when maintenance information is updated, the provision of a related system that can be replaced quickly is insufficient. In addition, port facilities prone to data loss need to be converted into BIM-based digital data for facility maintenance [1]. Therefore, this study intends to establish a framework that allows port maintenance information to be exchanged, shared, and utilized among systems, subjects and functions within the BIM environment. To this end, we developed port COBie, a standard format for port BIM-based maintenance data exchange and describe how it is processed in the process of generating, converting, transmitting and utilizing COBie data through overseas commercial BIM S/W. Through this, we develop a processing mechanism that allows maintenance information to be exchanged between the port maintenance system and the platform on the port BIM platform and to check the maintenance information for each facility and member through the port BIM viewer.  
In the future, this study will be able to be utilized as a common method of exchanging and processing maintenance data if the BIM platform is developed not only for other port facilities, but also for objects requiring maintenance such as roads and bridges.
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation  
In this section, the simulation parameters and results of the analysis are discussed. The IHD-V2X performance of the proposed algorithm is compared with other state-of-the-art handover mechanisms, including the Advanced Conditional Handover in 5G and Beyond Q-Learning (ACHQ) presented in [28], and the non-Q-Learning algorithm named Context-aware Enhanced Application-Specific Handover in 5G V2X Networks (CAP) [30].
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
###### Abstract  
Yttria-alumina-silica (YAS) glass microspheres doped with Nd\({}^{3+}\) ions were excited with a 532 nm continuous laser in order to study the dependency of the wavelength of the whispering gallery mode (WGM) peaks on the temperature of the sample. This was possible due to a previous calibration of the 808 nm and 890 nm emission bands of the Nd:YAS glass sample for different temperatures using the fluorescence intensity ratio (FIR) technique. A maximum sensitivity of 15 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) and a temperature resolution limit of 0.2 K were obtained for the microsphere sensor. Moreover, laser emission at 1064 nm was observed by continuous pumping at 532 nm, and a power threshold of 100 mW was determined. Upconversion emissions of Nd\({}^{3+}\) were also studied by exciting the sample at 808 nm.  
transparent microspheres; neodymium; emission; whispering gallery modes; fluorescence intensity ratio; temperature sensor; laser; upconversion +
Footnote †: journal: Applied Sciences
## 4 Empirical Evaluation
### Research Questions  
Our experiments focused on the dataset described above, and aimed at answering the following research questions:  
**(RQ1)**: To what extent are the abstracts of reviews (that self-identify as systematic reviews) systematic? Here we rely on the answers to the eleven questions specified above to assess the systematicity of the reviews that are included in our dataset.
**(RQ2)**: To what extent does the publication year influence the systematicity of the analyzed review abstracts?
**(RQ3)**: Does the use of PRISMA increase the systematicity of the review i.e., their reporting quality?
**(RQ4)**: What are the items that were appropriately reported in the review abstracts i.e., what are the questions to which most of the abstracts of the analyzed reviews were able to provide answers? Investigating this question will help us determine the minimum set of core abstract items to which systematic literature reviews commonly comply with in the software engineering field.
**(RQ5)**: Does the systematicity of the review abstracts influence the number of citations of these reviews? The rationale is that the more a review abstract is well-reported, the more the user may feel like the review findings are credible, may be motivated to read the full content of the review to eventually exploit its results, and may decide to cite the review as a relevant related work for instance.
**(RQ6)**: How do systematic reviewers usually structure reviews?  
When discussing the adherence of reviews to reporting guidelines, [PERSON] et al. [8] stressed that some researchers estimate that a 100% adherence to the checklist items of a reporting guideline would mean that a review constitutes \"a quality paper\". However, in the currently published research, it is virtually impossible to find a review that yields a 100% adherence with the target reporting guideline. Hence, that \"top quality\" may not be reachable and reviews adhering to, say, 80% of the items should be considered as \"well reported\". A threshold equal to 80% seems to be an optimal value and involves the coverage of most items in the reporting guideline checklist. Based on that reasoning, we therefore considered that if the overall confidence in the systematicity of an analyzed review abstract is at least 80% (i.e., High), then that review abstract exhibits an optimal systematicity. Otherwise, that abstract exhibits a suboptimal systematicity.
## 3 Results and Discussion
### Results
#### 3.1.5 Root Zone N Dynamics  
The dynamics of nitrogen in the root zone are defined primarily by a series of seven rate terms, including the mobilization and flushing of DON and DIN, the immobilization and uptake by vegetation of DIN, and the breakdown of DON to produce DIN, which occurs through respiration processes. There is also potential for denitrification as a pathway of loss, however given the well aerated nature of soils in WS10, we assume that denitrification does not occur in the root zone. These terms are outlined in Figure 8, as both time series and box plots. These results indicate a net mobilization (mineralization) of inorganic nitrogen of 2.51 g/m\({}^{2}\)-year, and that mobilization of DON is 2.51 g/m\({}^{2}\)-year. The values are equivalent because, without additional data, we simply assume that total nitrogen mobilization was proportional to respiration and that the product was half organic and half inorganic nitrogen. Mobilized DON is continuously decomposed to further add to the DIN pool, most of which is utilized through plant uptake. The average yearly uptake rate is 3.62 g/m\({}^{2}\)-year, similar to the 2.29 g/m\({}^{2}\)-year estimated by [13].  
The rate of flushing for DON (0.337 g/m\({}^{2}\)-year) is considerably larger than for DIN, which was effectively 0 for our simulations. This result is consistent with the well-established high N retention capacity of these watersheds. The rates of flushing periodically fall to zero, indicated as breaks in the time series in Figure 8. This occurs when amounts of DIN or DON are not sufficient to support all of the simulated loss pathways.
## 3 Synthesis of Amine-Functionalized Silica/Organosilica Membranes
### Post-Treatment of Mesoporous Silica Membranes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Membrane** & **Functionalized** & **Temperature** [\({}^{\circ}\)**C] \({}^{\circ}\) & **CO\({}_{2}\) Permeance [10\({}^{-10}\)** & **CO\({}_{2}\)/N\({}_{2}\)** & **Ref.** \\
**Support** & **Agent** & **moll/(m\({}^{2}\) s Pa)]** & **Selectivity [-]** & **Ref.** \\ \hline Vycor tubes & APTES & 120 & 1.8 & 10 \({}^{\mathrm{b}}\) & [11] \\ \hline Silica & APTES & 100 & 10 & 800 \({}^{\mathrm{b}}\) & [11] \\ \hline Silica & Azirdine & 35 & \(\sim\)6 & 0.15 \({}^{\mathrm{c}}\) & [13] \\ \hline Silica & Azirdine & 35 & \(\sim\)10 (wet) & \(\sim\)3 \({}^{\mathrm{c}}\) & [13] \\ \hline Silica & TRIES, NH\({}_{3}\) & 200 & 2000 & 2 \({}^{\mathrm{c}}\) & [32] \\ \hline Silica & APTES & 40 & 0.15 & 50 \({}^{\mathrm{b}}\) & \\ \cline{3-5}  & 40 & 0.023 (wet) & 9 \({}^{\mathrm{b}}\) & [33] \\ \cline{3-5}  & 60 & 6.1 & 72 \({}^{\mathrm{b}}\) & \\ \cline{3-5}  & 80 & 5.2 & 289 \({}^{\mathrm{b}}\) & \\ \cline{3-5}  & 100 & 6.1 & 339 \({}^{\mathrm{b}}\) & \\ \hline Silica & TA & 60 & 6.9 & 300 \({}^{\mathrm{b}}\) & [33] \\ \cline{3-5}  & 60 & 0.11 (wet) & 256 \({}^{\mathrm{b}}\) & \\ \hline \hline \end{tabular}  
* The temperature used for gas permeation testing; \({}^{\mathrm{b}}\) Separation factor of a mixed system; \({}^{\mathrm{c}}\) Ideal selectivity. Abbreviations: **APTES**, 3-aminopropylritehoxysilane; **TRIES**, triethoxysilane; **TA**, 3-trimethoxysilopropyl-diethylenetriamine.  
\end{table} 
Table 2: Summary of synthesis conditions and membrane performance for reported amine-functionalized silica-based membranes fabricated via post-treatment of mesoporous silica-based membranes.
# Position Accuracy and Distributed Beamforming Performance in WSNs: A Simulation Study
###### Abstract  
This work investigates the performance of distributed beamforming in Wireless Sensor Networks (WSNs), focusing on the impact of node position errors. A comprehensive simulation testbed was developed to assess how varying network topologies and position uncertainties impact system performance. Our results reveal that distributed beamforming in the near-field is highly sensitive to position errors, resulting in a noticeable degradation in performance, particularly in terms of Bit Error Rate (BER). Cramer-Rao Lower Bound (CRB) was used to analyse the theoretical limitations of position estimation accuracy and how these limitations affect beamforming performance. These findings underscore the critical importance of accurate localisation techniques and robust beamforming algorithms to fully realise the potential of distributed beamforming in practical WSN applications.  
Keywords:wireless sensor network; distributed beamforming; Cramer-Rao Bound; near-field communication; communications and sensing; communication-assisted localization +
Footnote †: journal:  
_Article_
## 4 RFR-Based LP-IoT Channel Estimation Models  
This section demonstrates the LP-IoT channel estimation using the RFR algorithm, which is operated using a collection of decision trees. The output of all the trees is averaged for regression tasks to reduce variance and avoid over-fitting, which is a common issue in individual decision trees. Each tree is trained on a random subset of data with replacement (bootstrap samples). At each node, a subset of environment features (see Section 4.1) is randomly selected to determine the split, which enhances diversity among trees. In this section, the RFR algorithm has been applied in two variations, RFR(F) and RFR(S), which are detailed in the subsequent subsections.