esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Communication in IoT for Enabling Next-Generation Wireless Systems
## 3 Proposed Routing Protocol
### Routing Protocol
#### 3.1.2 Energy Model  
To send the message from a sensor to the BS, the objective function should be calculated based on minimizing the total amount of the energy consumption of all sensors participate to deliver the message to the BS. To select the next hop of the message, two parameters should be considered: the distance to the next sensor, and the straight-line distance (SLD) between the next sensor and the BS as shown below.  
\[Dist_{t}=dis_{(s)}+dis_{(bs)} \tag{1}\]  
where \(Dis_{t}\) is the total distance from the sensor wants to communicate with BS to the BS, \(dis_{(s)}\) represents the distance to the next sensor (next hop), and the \(dis_{(bs)}\) is the SLD from next node.  
In the literature, all the proposed approaches used the Euclidian distance, as shown in the following equation, in order to find the distance (shortest distance) between sensor-to-sensor, or sensor-to-BS.  
\[D=\sqrt{(x_{1}-x_{2})^{2}\ +\ (y_{1}-y_{2})^{2}} \tag{2}\]  
In this work, the distances are calculated using a haversine equation which is utilized to find the shortest distance between two points on a sphere, using their coordinates (longitudes and latitudes) measured on the surface as shown in Figure 4.  
\[haversine\ (\alpha)=sin^{2}\ (\alpha/2) \tag{3}\]The haversine of central angle \((D_{t}/R)\) is calculated as follows:  
\[Dis_{t}/R=haversine(\psi_{bs}-\psi_{i})+cos(\psi_{i})\ cos(\psi_{bs})\ haversine( \zeta_{bs}-\zeta i) \tag{4}\]  
where \(R\) is the earth's radius (\(\sim\)6371 km), \(Dis_{t}\) is calculated using the inverse of sin function or haversine as follows:  
\[Dis_{t}=Rhav^{-1}(h)=2 Rsin^{-1}(h) \tag{5}\]  
\[Dis_{t}=2 Rsin^{-1}(h)*\sqrt{sin^{2}(\frac{\psi_{bs}-\psi_{i}}{2})+cos(\psi i) \ cos(\psi_{bs})\ sin^{2}(\zeta_{bs}-\zeta_{i})} \tag{6}\]  
When a sensor transmits or receives data, it consumes a certain amount of energy (\(E_{\pi}\)), which depends on the size of data, and the distance (in transmission mode) that the sensor needs. Energy consumption model can be mathematically represented as follows:  
\[E_{\pi}(L,\ D)=\begin{cases}LE_{n(elc)}+\ L\eta_{fr}D^{2},\ D<D_{0}\\ E_{n(elc)}+\ L\eta_{pt}D^{4},\ D\geq D_{0}\end{cases} \tag{7}\]  
where \(L\) represents the size of data packet, \(D\) is the distance between the transmitter and receiver, \(\eta_{fr}\) represents free space energy loss, \(\eta_{pt}\) denotes the multipath energy loss, \(D_{0}\) is the threshold distance which controls situation to select \(\eta_{fr}\) or \(\eta_{pt}\). \(D_{0}\) can be calculated the following equation:  
\[D_{0}=\sqrt{\frac{\eta_{fr}}{\eta_{pt}}} \tag{8}\]
# Genetic Multi-Objective Optimization of Sensor Placement for SHM of Composite Structures
## 2 Tested Structure and Experiments  
### Parameters of the Tested Structure  
The structure considered in this study to test the developed OSP approach is a carbon-fiber-reinforced polymeric (CFRP) composite plate with the dimensions of 490 \(\times\) 240 mm, thickness of 1.84 mm, and the following stacking sequence: \((0/90)_{58}\) (see Figure 1a). In the study, the healthy plate was considered as a reference for building the numerical models described in the next subsection. The detailed description of the manufacturing process and material properties of this plate are presented in [7].  
The plate was fixed on the shorter side to the aluminum frame at a distance of 20 mm from the edge using screws located with a distance of 50 mm on the clamped sides and a tightening torque of 20 Nm. The plate was then subjected to mechanical excitation using the Penton CAD10T/EN loudspeaker (Worthing, UK) using the periodic chirp signal in the bandwidth of 0 \(\div\) 625 Hz with a resolution of 0.195 Hz (see Figure 1b).  
The measurements of a structural response were performed on a defined grid of 25 \(\times\) 13 equidistantly spaced measurement points using a Polytec(r) PSV-500-3D scanning laser vibrometer (Waldbronn, Germany) with three averaging cycles to improve the accuracy of the measurements. During measurements, the vibration velocity in the defined measurement points was acquired. Details on the experimental setup are provided in [7]. As a result of the performed measurements, the structural response of a plate was acquired in the form of a frequency-response function (FRF). The natural frequencies determined from the modal analysis are presented in Table 1.  
To determine the strain values in both directions for the tested plate, _Sx_ and _Sy_, respectively, the transverse displacements \(w\) were considered. Further, from transverse displacements smoothed using the least squares linear regression, the strain fields were  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode No.** & **Frequency, Hz** & **Vibration Velocity, \(10^{-4}\) m/s** \\ \hline
1 & 58.20 & 0.2 \\
2 & 67.58 & 0.17 \\
3 & 159.76 & 0.31 \\
4 & 174.22 & 0.48 \\
5 & 232.81 & 1.1 \\
6 & 294.92 & 0.66 \\
7 & 299.61 & 1.37 \\
8 & 339.45 & 0.41 \\
9 & 434.76 & 0.53 \\
10 & 543.36 & 0.47 \\
11 & 562.11 & 0.18 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Natural frequencies of the tested plate.  
Figure 1: The scheme of the tested CFRP plate (**a**), and the experimental setup (**b**).  
determined. Each smoothed value is determined using the adjacent data points. The strain value maps were normalized in such a way that the largest value was assigned to a unit and the remaining values were scaled relative to the maximum value. The deformation values on the plate surface in appropriate directions are calculated using the following formula:  
\[Sx_{(i,j)}=\left(\frac{\partial^{2}w}{\partial x^{2}}\right)_{(i,j)}\cdot\frac {1}{2}h;\;Sy_{(i,j)}=\left(\frac{\partial^{2}w}{\partial y^{2}}\right)_{(i,j)} \cdot\frac{1}{2}h, \tag{1}\]  
where \(h\) is the thickness of the plate.
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 1 Introduction
This study aims to address this gap by investigating the combined effects of RFA and stabilizers on soil strength and structural stability to provide a sustainable solution for stabilizing red clay soils with high plasticity, low strength, and high water content. The effects of recycled aggregate content (R), soil stabilizer content (C), and water curing age (M) on the mechanical properties of stabilized soft soil were analyzed through unconfined compression tests and triaxial tests. Additionally, microscopic techniques such as scanning electron microscopy (SEM) and laser diffraction particle size distribution (PSD) analysis were employed to investigate the microstructure, pore characteristics, and morphological changes in stabilizedsoft soil. The findings reveal the reinforcement mechanisms by which recycled aggregates and soil stabilizers synergistically enhance the performance of stabilized soft soil.
## 3 Results  
### Characterization of MSCs  
#### 3.1.1 Surface Markers of MSCs  
Three types of oral tissular remnants were used to obtain single-cell suspensions for identifying the putative MSCs. Putative MSCs were cultured under normal culture conditions or in an HA-containing medium and characterized in several ways. MSCs firmly adhered to the plastic surface of the culture plates and presented a fibroblast-like morphology that remained unchanged during the passage process.  
Flow cytometry was used to show the percentage of positive cells for the cells-surface markers. The immunophenotypic characterization of MSCs revealed that the cells were positively stained for the following surface markers: CD29, CD73, CD90, CD105, CD44, and CD79a (Table 1, Figures S1 and S2). In contrast, a negative expression of CD34, CD45, HL-DR, andCD14 was associated with these cells.
# Groundwater and Urban Planning Perspective
## 3 Discussion of Urban Planning and Subsidence  
### Analysis of the Current Urban Planning and Development Plans in Romania  
The analysis performed by the authors on the urban planning and development documentation emphasizes that groundwater information is not regularly mentioned in the urban planning documentation of the 17 cities [44; 45; 46; 47; 48; 49; 50]. According to the Romanian legislation, the plans are based on support studies, but only the main planner decides which studies are required. The information from the support studies is incorporated into the general report. Only in some cases such as Iasi, Braila, Bacau, Ploiesti, and Pitesti [44; 45; 48; 50] do the plans include geotechnical or hydrogeological analysis. In some cases, even if these studies include the hydrological component, they only refer to surface water (for example, the city of Bacau [45]), thus outlining the areas that are unsuitable for construction due to the risk of floods or erosion.  
In other cases, for example the city of Pitesti [50], the local planning regulation accompanying the plan, which takes the information from the support study, provides measures to encourage building on difficult foundation sites. For example, underground parking areas are allowed to be built if the beneficiary can prove that the terrain has difficult foundation conditions, including high groundwater levels. The proof is presented by the significant comparative costs estimated for both underground and surface options, supported by geotechnical or hydrogeological studies.  
Figure 5: Detection map of land surface changes for Bucharest (2014-2018), obtained from Sentinel-1 GRD images (modified after [28]). Gray tones symbolize areas where no changes were detected between 2014 and 2018. Areas where ground changes occurred between 2014 and 2018 are symbolized in red. Water bodies are shown on the map in dark tones. Map generated using ESA SNAP (Sentinel Application Platform) and QGIS open-source software. Source: elaborated by the authors.  
In some cases, for example the Manta Rosie district of Iasi city (where new high buildings have been constructed) [44], although the problem of a high groundwater level flooding the cellars of old houses is acknowledged in the general report of the urban planning documentation, the associated regulation does not prescribe specific construction restrictions nor recommend specific measures to improve the urban hydrogeological conditions. Although recently elaborated, other general urban plans for the cities of Braila, Galati, Timisoara, and Sibiu [47; 48; 52; 58] incorporate only generic statements of the current legislation (General Urban Planning Regulation adopted by Government Decision 525/1996, updated in 2002), namely that, in the \"constructions are allowed in the buffer protection zone of the rail transport infrastructure if all measures triggering landslides, subsidence, or altering the groundwater table, are provided\".  
For Bucharest, the analysis of the studied strategies and General Urban Plan [60; 61; 62] concluded that information related to groundwater is not considered.
# Similarity Calculation via Passage-Level Event Connection Graph
## 5 Experiments and Analyses
### Experimental Results
#### 5.2.2 Comparison of Different Event Extraction Methods
This way can also alleviate the adverse effect brought from the incorrect extraction. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**English**} & \multicolumn{2}{c}{**Chinese**} & \multicolumn{2}{c}{**Spanish**} \\ \cline{2-7}  & **Para** & **Q\&Q** & **MA** & **Q\&Q** & **MA** & **MA** \\ \hline BeemSearch & 0.81 & 0.84 & 0.66 & 0.85 & 0.62 & 0.67 \\ JointTransition & 0.79 & 0.83 & 0.64 & 0.84 & 0.63 & 0.68 \\ ODEE-FER & **0.82** & 0.85 & **0.68** & 0.86 & 0.65 & **0.71** \\ OneIE & **0.82** & **0.86** & **0.68** & **0.89** & **0.67** & **0.71** \\ Extreme & 0.77 & 0.82 & 0.63 & 0.82 & 0.61 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of our calculation obtained on the condition that the event connection graph is constructed via several popular event extraction methods (highest values in bold). To clearly see the effects brought from different event extraction methods, we also draw a histogram figure to illustrate the calculating results obtained by different sentence-level event extraction methods. In addition, to compare their results in two tasks of text similarity calculation and event extraction, we show the results obtained by different event extraction methods in both tasks in two colors. In this figure, each algorithm corresponds to two columns. The column with blue color indicates the text similarity calculating results, which is averaged across all the corpora including open tasks and manual annotation. The column with orange color indicates the event extracting results, which is averaged across the corpora from ACE and FrameNet, two popular event extraction tasks. As shown in Figure 6, it is hard to see the performance gap among all the event extraction methods in text similarity calculation task. However, the event extraction methods adopted in the experiment perform differently in the event extraction task. As shown in this figure, ODEE-FER performs much better than the other event extraction methods. Anyway, in this experiment, we also design a simple method called Extreme. This method just simply treats a verb in the sentence as trigger and a noun as argument. It is easy to assume that Extreme should have a poor performance in the event extraction task. The result also proves this assumption. Surprisingly, Extreme obtains almost the same result with the other event extraction methods in the text similarity calculation task. That indicates we do not need the precise event extraction results, as long as the extracted results contain enough triggers and arguments.
# On the Performance of Underlay Device-to-Device Communications
## 3 System Model
### Power Allocation in D2D Networks  
In this paper, we adopt three distinct power allocation schemes in the D2D networks comprising the equal, random, and path-loss-based schemes [49]. The detailed definition of these schemes are given below.
# Local Balance and Information Aggregation: A Method for Identifying Central Influencers in Networks
## 5 Experiment  
### Experimental Setup  
To evaluate the performance of LBIA, we compared it with eight representative critical node identification algorithms: betweenness centrality (BC) [19], cluster centrality (CC), degree centrality (DC) [18], PageRank [15], KSGC [25], LCH [20], NTPR [23], and IDME [27]. These algorithms cover segmentation by centrality, improvements to classical algorithms, and algorithms that analyze the network structure in terms of its structure. It is more convincing to compare LBIA with algorithms identified from these different perspectives. For the scientific validity of the results, experiments were conducted on the accuracy of different algorithms in terms of recognition ability.  
In various diffusion networks, infectious disease models such as SIR are utilized to detect critical nodes [34]. A low propagation probability will result in the under-propagated of information, while a high propagation probability will lead to rapid propagation in a short period, which is of no research significance. In order not to lose generality, the propagation probability was set to 0.1. When the recovery probability was set to 1, the infected nodes were immune in the next round, which effectively reduced the time required by the algorithm. Since there may be situations where no node is infected during the first propagation, we ran 1000 iterations of the SIR model to obtain the average propagation value of the nodes.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 1 Introduction
### Related Work  
In the context of achieving uplink synchronization for terminals without GNSS assistance in LEO satellite scenarios, several studies have been conducted. Paper [14] compares GNSS and LEO-based positioning schemes and proposes evaluation metrics for IoT terminals to perform PNT services, including positioning accuracy, power consumption, hardware availability, and network accessibility. These metrics will also serve as key indicators for evaluating the proposed scheme in this paper. Paper [15] suggests that terminals can obtain TDOA and FDOA measurements over a time window of 2-12 s to estimate their positions and subsequently calculate TA values using satellite ephemeris broadcast data. However, this method requires the terminal to remain in receive mode for extended periods, resulting in significant power consumption, making it unsuitable for low-power NB-IoT devices. Moreover, the paper does not provide a detailed analysis of power consumption. In [16], a method called SPIN is proposed, in which the terminal measures the downlink primary synchronization signal to acquire observation data and then solves an observation equation. The paper also includes a comparison of power consumption with GNSS-based approaches. However, it does not analyze the specific signal format nor describe how the observations are obtained. In [17], a receiver model is designed that incorporates pilot signal insertion and Doppler shift estimation. While it provides a complete receiver framework, it requires modification of the existing signal structure and leads to increased power consumption on the transmitter side. Paper [18] proposes that, for NB-IoT terminals, uplink frequency synchronization can be achieved by compensating the downlink frequency offset estimation into the uplink and performing frequency tracking. However, this study does not detail a specific downlink synchronization mechanism. Additionally, some approaches estimate uplink TA values without relying on terminal position by modifying the signal structure. Papers [19; 20] propose 5G synchronization signal detection algorithms under LEO satellite channels that utilize the multipath energy window of the signal and cyclic prefix-based joint time-frequency estimation method. These methods aim to address the severe Doppler shift inherent in LEO channels, which renders traditional synchronization techniques ineffective. Therefore, it is necessary to redesign detection algorithms specifically for synchronization signals to improve the overall synchronization performance of the system.
## 4 The Concept of GORBA for Fast Rescheduling
### Heuristics for Scheduling and Rescheduling  
Both sets of heuristics work in two steps. In its first step the first set produces three sequences of grid jobs each based on one of the following three precedence rules:  
1. _earliest due time_: grid jobs of the application job with the earliest due time first,
2. _shortest working time of grid job_: grid jobs with the shortest working time first,
3. _shortest working time of application job_: grid jobs of the application job with the shortest working time first.  
In the next step resources are allocated to the grid jobs using one of the following three resource allocation strategies (**RAS**):  
1. Use the fastest of the earliest available resources for all grid jobs.
2. Use the cheapest of the earliest available resources for all grid jobs.
3. Use RAS-1 or RAS-2 for all grid jobs of an application job according to its time/cost preference given by the user.  
As every RAS is applied to each grid job sequence, nine schedules are generated by the first set.  
The second set is aimed at handling the replanning situation. For this the grid jobs of the old plan are divided into two groups: all grid jobs which have been already started or will be started within the planning time, _i.e._, the next three minutes, belong to the first group. From this group, all grid jobs are extracted which cannot be processed using the previously assigned resources. Together with the rest of the old grid jobs, they form the second group, which is subject to rescheduling. The first group can be regarded as fixed jobs, which are not affected by the rescheduling event.  
The grid jobs of the second group are placed in the same sequence as in the old plan. If rescheduling was caused by the arrival of one or more new application jobs, their new grid jobs are sorted according to one of the three heuristic rules already described and added to the sequence of old jobs, yielding three different sequences. Resources are allocated using the three RAS and again, nine more schedules are generated, but this time based on information of the old plan. The best of these eighteen schedules is the final result of the first planning stage, while all are used to seed the subsequent EA run of the second stage. As for other reasons for replanning, there is only the sequence of the old plan to which the three RAS are applied so that there are only twelve heuristic solutions. To sum up, the scheduling sequence of the old plan is still used as far as possible, while the resource allocation may differ especially when the set of resources is changed or new grid jobs are processed.
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 5 Implementing the Proposed Model
### Technical Considerations
#### 5.1.1 Data Entry Design  
The data entry design integrates cycle identifiers into form templates, automatically tagging each form with its respective treatment cycle. This ensures accurate and consistent data tagging while providing a user-friendly interface for healthcare providers to minimize errors and streamline the data entry process. These cycle-based form templates are implemented in the following way.  
* Each data form (f1, f2,..., fn) within a treatment cycle is designed to include a field for a cycle identifier (flag).
* The form templates are pre-configured to automatically assign the appropriate treatment cycle flag according to the patient's current treatment stage.
* For instance, during the first treatment cycle, forms will be tagged with \"Cycle 1\". If the patient begins a new cycle, subsequent forms will be tagged with \"Cycle 2\".  
The system employs automated flagging to ensure that the cycle identifier is consistently applied to all relevant forms without manual input, reducing the risk of errors.
# Management of Acute Life-Threatening Asthma Exacerbations in the Intensive Care Unit
## 4 Acute Life-Threatening Asthma Advanced Management in the ICU
### High-Flow Nasal Oxygen (HFNO)  
Asthma patients admitted to the ICU are often hypoxic, and supplemental oxygen therapy is one of the most common first-line treatments provided either via low-flow nasal cannula (LFNC) or via facemask [13]. LFNC can deliver supplemental oxygen at a maximum rate of 4 to 6 litres (L) per minute, while a standard reservoir mask can deliver a maximum rate of 15 L per minute [13]. Recently, several small studies have been performed evaluating the use of high-flow nasal oxygen (HFNO) for asthma exacerbation. A 2020 pilot randomised controlled trial of 37 patients compared HFNC therapy to conventional oxygen therapy in acute severe asthma patients and found that HFNC therapy reduced the severity of dyspnoea and respiratory rate in hypoxemic patients [52]. A 2021 study that randomised 62 acute severe asthma patients to either non-invasive ventilation (NIV) or HFNC found that the HFNC group had significantly lower respiratory rates at 48 h when compared with NIV [53]. Furthermore, the HFNC group had shorter mean ICU and hospital lengths of stay and a greater average comfort score [53]. A meta-analysis of four randomised controlled trials totalling 175 patients showed that although HFNC appeared to significantly lower dyspnoea score, there was no significant influence on gas exchange, intubation rate, or hospital length of stay [54]. Although a trial of HFNC could be considered for improvement of dyspnoea in certain patients, it should not be regarded as an alternative to mechanical ventilation.