esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods
### Electromagnetic Survey--EM Profiler  
EM profiles in the field of Agoulinitsa were acquired using a multi-frequency EM conductivity meter, the EMP-400 Profiler of the Geophysical Survey Systems Incorporation (GSSI). The instrument is portable, carries both the transmitting and receiving coils required to measure the EM field, and does not require direct contact with the ground while measuring. This facilitated data collection, allowing for fast implementation of several measuring profiles above the study field. Subsequently, profile conductivity measurements were combined using appropriate software to create subsurface conductivity maps, which in turn, provide information on subsurface deposits, porosity, and soil properties and structures.  
The study area (4.5 hectares) was covered by a set of line measurements (profiles) of 400 m in length and with a spacing of 10-12 m (Figure 2). This griding resulted in 9 profiles, named emTST01-09, which are plotted in Figure 2, along with the location of the sample core (marked in Figure 2 as \"Core AG\"). For each profile, EMP-400 was set to automatically take measurements every 0.5 s, while the operator was walking at a steady pace. Since EMP-400 is a multi-frequency measuring unit, we parameterized it to measure three distinct frequencies (1, 9, and 16 kHz) simultaneously to obtain information corresponding to shallow and deeper subsurface layers.  
The subsequent step in our analysis comprised the 2D inversion of the collected data for the \"quadrature\" component of the EM signal, linked to conductivity. The inversion was performed using the EM4 Soil-MF software package (EMTOMO, 2018), which was developed for data of multi-frequency instruments, such as the EMP-400 used in this study. The underlying inversion algorithm is the nonlinear, smoothness-constrained sort, proposed by [46]. A detailed description of the inversion algorithm is provided in the adopted software's manual (EMTOMO, 2018).  
Figure 2: Studied area with the location of the nine profiles scanned with the EMP-400 profiler and the location of core AG (4 m total length).
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 3 Empirical Formulae for Damping Ratio of RC Buildings for Wind Design
### Frequency- and Amplitude-Dependent Damping Ratio
#### 3.2.4 [PERSON] [6] proposed empirical formulae similar to those in AIJ 2000 and ESDU 83009 based on the measured damping ratio data of 17 rectangular residential buildings in Korea. The heights of the measured buildings were 24.4-67 m, and an ambient vibration test and a human excitation test were performed to estimate the damping ratio. The empirical formulae for damping ratios along the longer and shorter directions are as follows:  
\[\zeta=0.0057f_{1}+310\bigg{(}\frac{X}{H}\bigg{)}+0.0057\text{ for shorter direction} \tag{16}\]  
\[\zeta=0.0059f_{1}+310\bigg{(}\frac{X}{H}\bigg{)}+0.0039\text{ for longer direction} \tag{17}\]  
where the first mode natural frequency is calculated using Equation (7) obtained from a regression analysis.
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 3 Opportunistic Void-Avoidance Routing Protocol
### Beaconing Model
Moreover, nodes employ \(T_{invalid}\), which shows how long a path is valid at each node. If a node cannot sense any neighbouring node with a lower hop count in its vicinity in this time interval, it should determine a new hop count value based on the recently received, and still valid, beacons. The routing performance depends on the assigned value for \(T_{update}\) in a way that a higher value leads to the invalidity of the vicinity information and a lower value imposes high communication overhead. According to the mobility pattern and speed of underwater nodes, \(T_{update}\) should be carefully determined.
## 1 Introduction
### Solution Combustion Synthesis  
A very efficient method for the synthesis of nanomaterials is solution combustion synthesis (SCS) (e.g., [16]). It is an exothermic process and is characterized by redox reactions in a water solution containing metal nitrates as oxidizers and various organic compounds as reducers [17; 18] At a pre-heating temperature range of 300-600 \({}^{\circ}\)C, the water is evaporated and the remaining reactants dry and heat up rapidly. Within, at most, a few minutes, they ignite and sustain a rapid exothermic reaction which results in foam-like, nano-structured, powder-form [19] SCS products with a pronounced three-dimensional dendritic structure which occurs due to the formation of hydrates when the nitrates are heated in water and the extensive gas generation during SCS [20; 21].  
Oxides are the most common product of the SCS process as a result of the nitrate decomposition. [PERSON] et al. [22] produced nickel oxide nano-powders and evaluated the influence of glycine andurea as reducers. Glycine was found to have a more intense combustion, less residual carbon, and interacted better with metal cations.  
[PERSON] et al. [23] investigated the possibility of producing compounds other than oxides, such as metals or alloys, using SCS. They directly synthesized pure nickel nano-powder and suggested a specific reaction pathway to describe the metallic phase formation during SCS. Their research revealed that ammonia (derived from fuel decomposition) and nitric acid (derived from nitrate decomposition) are mixed in one volume during combustion and, thus, they can react. This reaction is highly exothermic and results in water, nitrogen, and hydrogen as the main equilibrium products. Depending upon the fuel-to-oxidizer ratio, they indicated that in fuel-rich conditions, the system provides a reducing atmosphere (hydrogen and carbon monoxide) that could lead to the formation of pure nickel.  
The fuels added in the SCS precursor solution act as reducing agents; therefore, the production of metals is feasible through the reduction of metal oxides by the residual fuel as well [24]. [PERSON] et al. [25] produced nickel particles using microwave-assisted SCS in fuel-rich conditions and air atmosphere. [PERSON] et al. [26] investigated the suitable fuel-to-oxidizer ratio in order to control the velocity of combustion and obtain nickel foam. They concluded that fuel-rich conditions are necessary to provide a reducing atmosphere for the formation of metallic nickel.  
Solution combustion synthesis is also a very efficient method for preparation of catalysts. The SCS-derived catalysts can be distinguished in two main groups: bulk and supported catalysts [16]. In the first group, [PERSON] et al. [27] reported the preparation of bulk metallic catalysts (Cu, Ni) and tested them towards ethanol reforming reactions. Concerning the synthesis of supported catalysts, [PERSON] et al. [28] reported on the synthesis of nickel catalysts on highly porous alumina supports, achieving a surface area of \(\sim\)160 m\({}^{2}\)/g.
# Efficient Dissemination of Safety Messages in Vehicle Ad Hoc Network Environments
## 3 The Proposed Safety Message Dissemination Scheme
### V2I Communication
#### 3.4.1 Cluster-Based V2I Communication  
Figure 5 shows the operation process of V2I communication. Cluster-based V2I communication has similar required time and operation processes to intercluster communication in V2V communication. Cluster-based V2I communication is performed when disseminating emergency messages and when general safety messages that do not exist within the cluster are requested. To prevent load on the RSU, CMs communicate with the RSU through the CH. INs communicate directly with the RSU.  
Algorithm 4 shows the cluster-based V2I communication algorithm. If an emergency safety message exists, the CM delivers the emergency safety message to the CH in the same way it operates in V2V intracluster communication. CMs do not communicate directly with the RSU. On the other hand, CHs and INs directly deliver emergency safety messages to the RSU when such messages exist. The RSU receiving the emergency safety message checks for duplicates and then immediately disseminates the message. If a request for a general safety message exists, the CH delivers the CRD to the RSU in the same way it operates in V2V intercluster communication. The RSU receiving the vehicle's general safety message request broadcasts the message needed by the vehicle. Here, the RSU performs scheduling considering factors such as the holding rate and request rate within the communication range to disseminate higher-priority messages. The RSU disseminates the safety message to vehicles within the communication range according to the scheduling results.
# Two-Dimensional Topology Optimization of Headtube in Electric Scooter Considering Multiple Loads
## 4 Results  
### Topology Optimization for Single-Load Conditions  
Figure 5 shows the topology optimization results of the headtube according to the loads applied to the handle-supported part of the electric scooter. The results indicate that the 16 load conditions can be divided into four groups according to the axis (X, Y) along which the load is applied. Figure 6 shows the strain energy distribution of the topology optimization results. Within each group, the compliance values of the design domain and the strain energy of each element remain the same.  
Topology optimization under 16 single-load conditions showed that the loads were divided into four groups. This classification indicates that the various loading conditions experienced while driving an electric scooter can be summarized into four types. The final optimization used multiple loads from Groups A-D (Table 1), ensuring that all potential collision scenarios were considered. As a result, Group C showed the lowest mean and standard deviation of strain energy (Figure 7).  
Figure 4: Two commercial models for comparison of topology optimization considering the multiple-load conditions.  
As shown in Figure 6, Group C exhibits the lowest compliance. This result is attributed to the distance between the fixed nodes and the axis on which the load is applied (the displacement boundary condition given to the nodes of the backboard is a fixed support that restrains both rotation and translation, not a frictionless support). The distance from the fixed support to the axis on which the load is applied increases the stress. This phenomenon can be explained by the positioning of the fixed nodes at the rear of the scooter, with the load application axis located close by. Consequently, Group C achieves the lowest compliance owing to the minimal distance between the applied load axis and the fixed support.  
Figure 5: Topology optimization results of the electric scooter headtube according to the load conditions. The arrows indicate the direction of the applied loads; the horizontal arrows represent the forces in the x-direction, and the vertical arrows denote the forces in the y-direction.  
Figure 6: Strain energy distribution of electric scooter headtube according to load condition. The arrows indicate the direction of applied loads: horizontal arrows represent forces in the x-direction, while vertical arrows denote forces in the y-direction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Group A** & **Group B** & **Group C** & **Group D** \\ \hline Compliance & \multirow{2}{*}{\(\left[\text{N}\cdot\text{mm}\right]\)} & \multirow{2}{*}{164.39108} & \multirow{2}{*}{231.94289} & \multirow{2}{*}{36.76632} & \multirow{2}{*}{96.38500} \\ \(\left[\text{N}\cdot\text{mm}\right]\) & & & & \\ \cline{2-5} \cline{5-6}
# Cross-Laminated Timber Floor: Analysis of the Acoustic Properties and Radiation Efficiency
## 2 Materials and Methods  
In this study the impact and airborne sound insulations as well as the radiation efficiency are investigated using sound pressure and velocity sensors on a CLT floor in the laboratory of the Free University of Bolzano, accordingly to ISO 10140-5 [48], to minimize flanking transmission. The laboratory consists of massive structure lined with double layer of gypsum board with a cavity filled with rock wool panels. The test frame and the floor of the receiving room are made of concrete. The two doors of the laboratory have a weighted sound reduction index of 42 dB (Figure 1).  
In order to minimize the variability of the measurements with time a seasoned wood floor was adopted. Thus, for this reason the measurements were all carried out in a short time range (2 consecutive days).  
The 5-layer CLT floor has dimensions of 4155 mm (longer length) \(\times\) 3000 mm (shorter length) with a mass per unit area, \(m^{\prime}\), of 84 kg/m\({}^{2}\) and a thickness of 200 mm. The shorter length consists of three resistant timber layers, while the longer length direction consists of two resistant wood layers (Figure 2).  
Figure 1: Laboratory receiving room and CLT floor.  
Figure 2: Wood fiber direction. Longer dimension (4155 mm) with 2 resistant layers (**a**). Shorter dimension (3000 m) with 3 resistant layers (**b**).  
The CLT floor is made of spruce and it is classified as a C24 according to the EN 338 [59], with the properties described in Table 1.  
The CLT floor was laid on an anti-vibration material, 30 mm thick, with a density of 500 kg/m\({}^{3}\). The perimeter of the specimen is therefore not bound to the structure.  
To characterize the acoustic behavior of the CLT floor's airborne and impact noise, some acoustic indices were determined. With sound pressure levels measurements, the normalized impact sound pressure level with tapping machine, the sound reduction index and the impact sound pressure with rubber ball have been determined. Velocity levels and radiation efficiency levels are then determined combining the vibration and acoustic measurements.  
For the measurements, an airborne source and two impact noise sources according to ISO 10140-5 were used (Figure 3). A Svantek 958 four-channel analyzer to acquire sound pressure and vibration data was used.  
An omnidirectional source with twelve loudspeakers (airborne source) driven with a pink noise signal, a standard tapping machine and a rubber ball have been used, respectively. In particular, the rubber ball is useful for a low frequency analysis.  
The tapping machine is the one most used both in the study of bare structures and in the case of the application of additional layers [60]. The three sources above described are illustrated in Figure 3.
## 4 Spatial Positioning and Postural Estimation Method
### Rough-Matching Process  
Before applying the surface point cloud of a component to the processed scene point cloud for rough matching, the candidate regions with point counts close to the surface point cloud are selected, while the regions with significant point counts are skipped to further improve the efficiency. The point cloud rough matching employs ISSs [23] to extract point cloud features, and the FPFH operator [24] describes the features. SAC-IA [25] is employed to roughly match the surface point cloud to the segmented point cloud. The reason why the methods above are applied is as follows: ISS is relatively efficient and robust for different shapes of objects, such as the MCB components. FPFH is fast and robust to noise, so it is suitable for our acquired point cloud. SAC-IA is also fast and widely used for rough matching. The procedure of rough matching was performed according to the following steps:  
1. Select sampling points according to conditions: select \(n\) sampling points from the candidate point cloud, and meanwhile, to ensure the discrimination of the FPFH features of each sampling point, the distance between each sampling point should be greater than the set threshold.
2. Search the corresponding point pair set: according to the FPFH features of the sampled points, nearest-neighbor searching is used in the model surface point cloud to find the point pairs with the smallest difference between the FPFH features of the MSPC and the CPC, and RANSAC [28] is used to remove incorrectly matched point pairs to obtain optimized point pairs with a similar feature set.
3. Obtain the optimal transformation parameters: first, the MSPC is transformed according to the matched point pair set obtained in Step 2, and then the matching effect is evaluated by the sum function of the distances between the transformed MSPC and the CPC. The Huber function is used to compute the sum function of the distance error. \[Huber=\sum_{i=1}^{n}H(l_{i})\] (1) \[H(l_{i})\begin{cases}\frac{1}{2}l_{i}^{2},&\|l_{i}\|>m_{l}\\ \frac{1}{2}m_{l}(2\|l_{i}\|-m_{l}),&\|l_{i}\|<m_{l}\end{cases}\] (2)  
where \(m_{l}\) is the preset threshold, and \(l_{i}\) is the distance error of the \(i\)-th corresponding point pair after the transformation. The optimal transformation is thus achieved for rough matching by finding the minimum error computed by Equation (2).  
Figure 11: Color rendering of cluster segmentation results.  
As shown in Figure 12, the original scene point cloud obtained by the 3D point cloud camera is color-rendered according to the field depth, which is used as a comparison before matching. Figure 13 shows the match result obtained by the rough matching of the magnetic system component. The results illustrate that the rough matching process achieves the approximate position and direction, but the error can still be observed. Therefore, fine registration is applied to improve the precision.
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 3 Clinical Brain Monitoring with TD-NIRS  
The focus if the present review was to evaluate the use of TR-NIRS in a clinical context regarding brain monitoring. Therefore, papers were identified using PubMed and Scopus, searching for a combination of keywords including (near-infrared spectroscopy \(|\) near infrared \(|\) optical \(|\) tomography) and (time domain \(|\) time resolved) and (brain \(|\) cerebral). Moreover, a manual search from articles' references was performed and authors related to the subject were contacted. Papers were rejected if only CW-NIRS or FD-NIRS were used, if the application was not focused on the brain, and if no clinical application could be identified (i.e., applications on healthy volunteers or animals only). A total of 52 publications covering a variety of applications were identified. We have grouped the applications into seven subgroups that we will discuss below: neonatal applications (19 studies), stroke assessment (8 studies), traumatic brain injury (TBI) assessment (four studies), psychiatry (nine studies), brain death assessment (three studies), perioperative care assessment (seven studies), and other applications (two studies). Table 2 provides a summary of the main characteristics of the instrument used in the studies reported in this review, and Figure 3 shows pictures of the instrumentation typically used. A more detailed list of TD-NIRS instruments is available in the review by [PERSON] and colleagues [57]. Moreover, a summary of the studies included in this review is presented in Table 3 and includes the system used, the number of patients scanned, the methodology used to process the data, and finally states the measurand reported in these studies. Finally, Figure 4 summarizes the main characteristic of the TD-NIRS systems used in the reviewed publications and shows the number of publications over the last 20 years that report the use of TD-NIRS in a clinical context. Figure 3: Example of homemade and commercial TD-NIRS instrumentation. (**a**) Homemade TD-NIRS system used in [58], used with ICG. (**b**) The tNIRS-1 commercial system, from Hamamatsu (Japan), courtesy of Hamamatsu. (**c**) Example of probe positioning over both cerebral hemispheres of a patient (the system is the one illustrated in (**a**)). Extracted from [58].
# Study of Annealing Temperature Effect on the Photovoltaic Performance of BiOI-Based Materials
###### Abstract  
Bismuth oxyiodide (BiOI) is expected to be promising material for photovoltaic devices since it has good activity under the visible range. Here, we studied the annealing treatment on BiOI and its effect on the photovoltaic application. Firstly, the synthesized BiOI from Bi(NO\({}_{3}\))\({}_{3}\) and KI was annealed at varied temperatures (100-550 \({}^{\circ}\)C). The structural investigation by X-ray diffraction and Raman spectroscopy analysis was supported with morphology and optical analysis by scanning electron microscope (SEM) and UV-Visible spectroscopy. Due to the heating treatment, it could result in iodine-deficient bismuth-based materials, namely Bi\({}_{7}\)O\({}_{3}\)I\({}_{3}\), Bi\({}_{5}\)O\(\intercal\), and \(\beta\)-Bi\({}_{2}\)O\({}_{3}\). Secondly, the photovoltaic test measurement was performed by solar simulator air mass (AM) 1.5 illumination which presented the current-voltage curve from each material. The enhancement of photovoltaic performance was given by the increase of temperature up to 300 \({}^{\circ}\)C. At that temperature, the performance of the device which consisted of Bi\({}_{7}\)O\({}_{3}\)I\({}_{3}\) achieved three times higher efficiency than the annealed parent BiOI at 100 \({}^{\circ}\)C. Hence, the structural changing owing to the oxygen addition to BiOI structure had an impact on the photoelectrochemical cell. Based on this work, it is possible to attempt BiOI derivation with suitable holes and electron transport layers for better photovoltaic performance.  
BiOI; annealing; iodine-deficient; photovoltaic
## 2 Materials and Methods
### Dar-Fm Staining  
Nitric oxide detection and quantification were performed using DAF-FM (4-amino-5-methylamino-2\({}^{\prime}\), 7\({}^{\prime}\)-difluororescein) diacetate (Cayman Chemicals, Ann Arbor, MI, USA). DAF-FM diacetate is a non-fluorescent reagent that reacts with NO to form a detectable fluorescent benzotriazole at 495/515 nm under a fluorescence microscope. The myoblasts were cultured at a density of 1 \(\times\) 10\({}^{4}\) cells/well in 24-well culture plates under both hypoxia and normoxia for 3 h, 24 h, 3 d, and 7 d using the respective medium. To avoid any potential background interference, a phenol red-free medium was used. The cells were washed twice with warm PBS, and then 500 uL per well of prewarmed Hank's Balanced Salt Solution (HBSS, Capricorn Scientific, Ebsdorferground, Germany) was added. After 30 min of incubation with HBSS, the cells were incubated in the dark with 5 uM DAF-FM in HBSS for 1 h at 37 \({}^{\circ}\)C. As a negative control, cells were incubated in parallel without DAF-FM. Finally, the cells were washed in HBSS and then were incubated for an additional 30 min to ensure complete ester cleavage of the intracellular diacetate. The cells were observed under a fluorescence microscope equipped with the 'AxioCam MRm Rev.3\({}^{\prime}\) camera and the 'AxioVision Image Analysis 4.8.2' software. Approximately ten random microscopic fields were photographed per experimental group, and the intensity of nitric oxide (NO) was quantified using ImageJ. The intensity of NO was estimated using the following formula: final mean field intensity for cells minus the mean field intensity of the background.