esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Semi-Supervised Learning Model to Coal Sample Classification
## 2 Experimental Setup  
### LIBS Experimental Setup  
As shown in Figure 1, the LIBS experimental system consists of a Q-switched Nd:YAG laser, a spectrometer, a laser attenuator, optics, and a computer. The laser is a Q-switched Nd:YAG laser manufactured by Beantech Optronics with an output wavelength of 1064 nm, a pulse width of 7 ns, a spot diameter of 7 mm, an energy of 300 mJ, and a frequency of 5 Hz. The laser passes through a laser attenuator, which reduces the energy from 300 mJ to 100 mJ, and then passes through a UV-fused silica plano-convex lens with a focal length of 75.3 mm UV-fused silica plano-convex lens with a focal length of 75.3 mm. In this way, the laser is focused on the surface of the sample, forming a coal plasma. The plasma signal was then transmitted to the spectrometer through two plano-convex lenses with a focal length of 40.1 mm each. Optimal experimental parameters were determined through parallel experimental tests, including a laser energy of 100 mJ, a Focus Depth of 2 mm (depth of focus buried in the sample), a delay time of 1.2 \(\upmu\)s, and a sample pressure of 40 MPa. Under these parameters, the average relative standard deviation (RSD) of the main peak of the sample spectrum was kept within 15%. The LIBS system diagram is shown in Figure 1.
# Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic
## 1 Introduction
The common feature of [21; 22; 23; 24; 25; 26; 27; 28] is that all RRHs serve calls which require nothing but a single RRU and CRU (single-service calls). However, in modern networks it is significant to be able to study a multiservice environment where calls need more resources. The analysis at call-level of such contemporary networks is essential in network planning procedures [31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. In this paper, a generalization of both the f-SC-SC and f-SC-MC models is proposed by considering that RRHs accommodate different service classes with different (per call) resource requirements. A candidate application is the enhanced mobile broadband case which considers service classes with high resource requirements such as virtual reality and online 4K video [44; 45]. The proposed work is the first that studies a C-RAN that accommodates multiservice quasi-random traffic and, at the same time, provides convolution algorithms for the efficient determination of congestion probabilities (recently, the case of C-RAN multi-service random traffic has been proposed in [46]). Such algorithms are used in the literature in order to express complicated resource sharing policies such as the bandwidth reservation policy and threshold-based policies [47; 48; 49; 50; 51; 52; 53; 54]. The proposed models of this paper are named finite multi-class-single-cluster (f-MC-SC) and finite multi-class-multi-cluster (f-MC-MC), respectively, while the summary of our contribution is as follows: (1) we propose the f-MC-SC model and show that the model has a PFS, (2) we present a brute force (BF) analytical method together with a convolution algorithm for the calculation of congestion probabilities in the proposed model, (3) we compare the congestion probabilities results of the f-MC-SC model with simulation results and those obtained via [27], (4) we propose the f-MC-MC model and show that the model has a PFS, and (5) we present a BF method as well as a convolution algorithm for the determination of congestion probabilities in the f-MC-MC model. The remainder of this paper is as follows. In Section 2, we propose the f-MC-SC model. In Section 2.1, we determine the steady-state probabilities of the proposed model via a PFS, while in Sections 2.2 and 2.3, we propose a BF method as well as a convolution algorithm for the determination of congestion probabilities, respectively. In Section 3, we provide simulation and analytical congestion probabilities results for the proposed f-MC-SC model and analytical congestion probabilities results of the f-SC-SC model of [27]. In Section 4, we propose the f-MC-MC model. In Section 4.1, we determine show that the model has PFS, while in Sections 4.2 and 4.3, we propose a BF method and a convolution algorithm for the computation of congestion probabilities, respectively. We conclude in Section 5. A list of abbreviations is presented in Table 1.
## 2 Study Area  
The study region is located in the transboundary zone of southern Burkina Faso and northern Ghana (3\({}^{\circ}\)10\({}^{\prime}\)33\({}^{\prime\prime}\)W-0\({}^{\circ}\)11\({}^{\prime}\)31\({}^{\prime\prime}\)W/9\({}^{\circ}\)29\({}^{\prime}\)4\({}^{\prime\prime}\)N-12\({}^{\circ}\)24\({}^{\prime}\)9\({}^{\prime\prime}\)N) with an approximate size of 98,000 km\({}^{2}\) (Figure 1). The climate is tropical and strongly seasonal with distinct rainy and dry seasons. The rainy season lasts for about four to five months between May and October with mean annual rainfall ranging from 700 to 1200 mm. The temperatures are relatively stable over the year with monthly averages between 25 \({}^{\circ}\)C and 32 \({}^{\circ}\)C. The study area is located in the West Sudanian Savanna ecoregion with vegetation types ranging from open grassland to closed woodland and forests [15]. Dense woody vegetation or forests mainly occur along rivers or in protected areas such as the Nazinga game ranch, Sissili classified forest, the Kabore-Tambi National Park, or the Mole National Park. Outside these protected areas, agricultural use is predominant, e.g., around Kabore-Tambi National Park, a strong increase in agricultural area (from about 30% to 60%) between 2001 and 2013 has been documented [16]. The agriculture in the study area mostly consists of small-scale rain-fed crops with average field sizes around 1 ha and a low productivity (1.15 t\(\cdot\) ha\({}^{-1}\cdot\) year\({}^{-1}\) cereals for Burkina Faso) as well as livestock grazing [17; 18]. The small-scale mosaic of different land cover and land use types (mainly semi-natural vegetation and croplands) make this study region spatially very heterogeneous [18]. An important landmark in the northeast is Lake Bagre which retains the White Volta for hydro-power generation but also supplies water to irrigated agricultural areas downstream of the dam.  
In some parts of the study area, the rainy season triggers flood events, whereas fires frequently occur during the dry season. In summary, the study area is highly dynamic and heterogeneous in space and time with expanding small-scale agriculture and a rainy and dry season cycle that strongly influences the seasonal development of vegetation.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 4 Challenges and Future Research Directions
### Future Research Directions
#### 4.2.4 Standardization Research and Development  
Despite the challenges, the reviewed efforts presented in Section 3 can be regarded as preliminary results supporting the feasibility of integrating the O-RAN concept into IoT architecture to support the next-generation IoT systems. However, there is a need for collaborative efforts in this direction from academia, industries, and all the relevant bodies involved in transitioning these visionary ideas into reality. This can be achieved by establishing a collaborative IoT ecosystem housing device manufacturers, solution developers, and focus groups dedicated to IoT within the O-RAN Alliance community to focus on IoT-related standards, use cases, and best practices.  
Similarly, research and development should be geared toward simulation tools and testbeds to motivate proof of concept developments and validation before deployments. Some efforts have been recorded in this direction, which led to the development of an _\"openman gym\"_[37] for end-to-end or comprehensive design and testing of data-driven xApps, _\"ns-O-RAN\"_[105] for simulating O-RAN solutions in ns-3 [106], and _\"NS3-O-RAN\"_[107] for simulating O-RAN-based AI/ML solutions in ns-3, etc. These efforts primarily focused on the O-RAN concept. There needs to be more effort to develop testing/validation tools for O-RAN-based IoT systems.  
Finally, investments in training programs to develop a skilled workforce capable of designing, deploying, and managing O-RAN networks should be encouraged in all facets of teaching and learning. This should include formal and informal training on technical aspects and best practices.
## 3 Experimental Setups for Indoor Positioning
### Setup for Experiment 2  
Figure 11 shows the second experimental setup and indoor distances between each receiver and repeater. In this experiment, a non-line-of-sight condition is formed by locating the receiver closer to Repeater 1. Repeater 2 does not directly see the receiver due to the corner at the intersection of two perpendicular corridors. The repeater configurations are kept as provided in Table 3. Repeater 2, which has an additional amplifier, is used with 5 dB attenuation to compensate for the non-line-of-sight condition and the scattering from the corner. Therefore, its gain is 17 dB higher than Repeaters 2 and 3.  
The outdoor GPS antenna directions are the same as Experiment 1.  
The real values of indoor distances \(d_{1}\), \(d_{2}\), and \(d_{3}\) are determined with a physical measurement using a laser pointer for a later comparison with the estimated values. The distance measurements are performed with respect to the well-known coordinates of the building.
## 4 Indoor Environment Modeling and Optimization of the Received Power
### An Optimization Algorithm for Mirror Placement in an Indoor VLC Environment  
The formulated optimization problem is nonlinear and can be solved using numerical optimization techniques, such as global optimization algorithms (e.g., genetic algorithms, particle swarm optimization) or gradient descent methods, depending on the complexity and search space. The main steps to solve this maximization problem can be summarized as follows:  
* Initialization: Generate an initial distribution of mirror positions \((x_{ij},y_{ij},z_{ij})\) within the dimensions of the indoor space.
* Received power calculation: For each mirror configuration, calculate \(P_{r}\) using the equations defined for flat or convex mirrors. Sum the contributions of all mirrors to obtain the total received power.
* Evaluation and adjustment: Evaluate the objective function for the current configuration. Adjust the mirror positions iteratively using the selected optimization method. Repeat until convergence or until the stopping criteria are met.
* Optimal result: The final configuration of the mirror positions \((x_{ij},y_{ij},z_{ij})\) is the one that maximizes \(P_{r}\). Evaluate the system performance with this optimal configuration in terms of received power, the bit error rate (BER), and other relevant performance metrics.  
Algorithm 1, presented below, is a generic implementation and can be adjusted according to the specific requirements of the environment or mirror capabilities.  
```
Input: Dimensions \(L_{x},L_{y},L_{z}\),  Transmitter position \(\mathbf{S}=(x_{s},y_{s},z_{s})\),  Receiver position \(\mathbf{R}=(x_{r},y_{r},z_{r})\),  Number of mirrors \(M\times N\),  Initial mirror positions \((x_{ij},y_{ij},z_{ij})\),  Max iterations,  Tolerance. Output: Optimal mirror positions \((x_{ij},y_{ij},z_{ij})\). Initialization: Define the search space \(X=[0,L_{x}]\), \(Y=[0,L_{y}]\), \(Z=[0,L_{z}]\).  Initialize random mirror positions \((x_{ij},y_{ij},z_{ij})\). Compute Initial Received Power: For each mirror \((i,j)\)  Calculate distance \(d_{ij}\) and incidence angle \(\theta_{ij}\). if mirror is flat  Calculate power \(p_{r,ij}^{\text{flat}}\) else  Calculate power \(p_{r,ij}^{\text{convex}}\) endif endfor  Compute total received power \(P_{r}\). Optimization Loop: While Stopping criteria not met For each mirror \((i,j)\)  Perturb mirror position \((x_{ij},y_{ij},z_{ij})\).  Recompute \(P_{r}\). if \(P_{r}\) improves  Accept new position; otherwise, revert. endif endfor endwhile Return: Optimal mirror positions \((x_{ij},y_{ij},z_{ij})\).
```  
**Algorithm 1** Optimization Algorithm for Mirror Placement in an Indoor VLC Scenario
###### Abstract  
Soil salinity, due to natural phenomena or human activities, alters the water potential, which in turn affects plant growth, negatively influencing their nutrient and water uptake. Plant-growth-promoting rhizobacteria (PGPR) can be used to counteract these negative effects, especially in glycopytes. The aim of our study was to characterize physiologically, genetically, and biochemically the novel halotolerant/halophilic bacteria isolated in our previous work. We evaluated the plant-growth-promoting (PGP) features and NaCl regulation's roles in them. In this regard, analysis based on 16S rDNA sequences confirmed that our isolates are distinct bacterial strains, probably belonging to new species, which we named _Bacillus_ sp. M21 and M23 and _Halomonas_ sp. QH23 and QH24. In literature, it is known that many species of _Halomonas_ and _Bacillus_ genera produce factors regulating plant development, such as indole-3-acetic acid (IAA), ammonium, and siderophores; and their efficiency in promoting plant growth and productivity was also demonstrated in vivo. We demonstrated that the newly isolated strains exhibit different PGP activities, highlighting how the latter are regulated by NaCl and in a strain-dependent manner. In particular, the main results showed that NaCl negatively affects the production of IAA in QH23, M21 and M23, whereas it promotes it in QH24, where it is strictly salt-dependent. Both _Halomonas_ strains produce ammonium only in the presence of NaCl, directly correlated to its concentration. The opposite occurs in _Bacillus_ strains, where salt reduces its production up to ten times. Overall, the results underline how halotolerance is a preferable, but not sufficient, condition for considering a PGPR potentially useful in applications aimed at improving the tolerance and productivity of crops in saline soils.  
s _Applied_  
_sciences_
## 4 Experiment Validation  
### Experiment Set-Up  
In this study, to validate the proposed method, a moving-vehicle laboratory experiment was executed on a large-scale steel continuous beam bridge. The bridge features a span arrangement totaling 22 m, composed of segments measuring 6, 10, and 6 m, respectively, as depicted in Figure 10. The southwest end support is equipped with a fixed bearing, whereas the remaining supports are sliding bearings. Steel platforms, illustrated in Figure 11, have been installed at both ends of the bridge to serve as starting and stopping points for the vehicle. The bridge's cross-sectional details are presented in Figure 12. The bridge's fundamental and second bending frequencies are recorded at 9.854 Hz and 20.015 Hz, respectively.  
Figure 8: Comparison between the estimated acceleration using the proposed method and the simulation result at Point 6 in Scenario 2 with double vehicle mass.  
Figure 9: Comparison between the estimated acceleration using the proposed method and the simulation result at Point 6 in Scenario 2 with higher moving speed.  
An electric vehicle, as displayed in Figure 12(a), was employed to apply a moving load to the bridge. The vehicle's geometric parameters are detailed in Figure 12(b). Mass blocks, shown in Figure 12(c), were affixed to the vehicle's platform. The front axle weighs 168.5 kg, and the rear axle weighs 218.5 kg. The vehicle traverses the bridge at a constant speed of 0.51 m/s. Throughout multiple trips, the vehicle's path is varied, following random patterns: along the right-side lane, the middle lane, and the left-side lane, as well as navigating an \"S\" route.  
For the monitoring system, five mid-span cross-sections of the bridge have been instrumented with inclinometers at the following coordinates: 0 m (the bridge's geometric center), \(\pm\)2.4 m, and \(\pm\)4.4 m. The arrangement of these sensors is depicted in Figure 13(a), indicated by triangular and cylindrical markers. The sensors operate at a sampling frequency of 20 Hz. In contrast, accelerometers, which are installed at three cross-sections--specifically at 0 m and \(\pm\)2.4 m--are also shown in Figure 13(a) and function at a higher sampling frequency of 200 Hz. Figure 13(b) provides a detailed view of the sensor placements on each cross-section. The inclinometers, denoted by triangles in Figure 13(b), are mounted on the inner side of the bridge's floor plate, while the accelerometers, represented by cylinders, are attached to the outer side. At each cross-section, a single inclinometer is positioned on the left side, with accelerometers installed in symmetrical pairs for balanced data acquisition.  
Figure 11: Steel platform and steel bridge [16].  
Figure 12: Cross-section of the steel bridge (unit: mm).  
Figure 10: Three-span steel continuous bridge [16].
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 3 New Energy-Aware Routing Protocols
### Remaining Required Energy (RRE) Algorithm  
The Threshold algorithm allows users to control the performance of the routing protocol by defining the energy threshold parameter. However, it can be a tedious task to find the optimal value of the energy threshold as it depends on the operating conditions, such as changes in the energy harvested per day, or changes in the motion patterns of the tracked objects. The RRE algorithm derives the best value of the threshold denoted by \(\mathrm{E^{Th}}_{\mathrm{RRE\;Fwd}}\) automatically.  
The underlying principle is to conserve sufficient energy required to allow a node to transmit packets currently stored in its buffer to a base station at the time of their next encounter. Effectively, the strategy is to prioritize transmission of direct packets to the base station over multi-hop data packets and over sensing additional data.  
The RRE forwarding threshold (\(\mathrm{E^{Th}}_{\mathrm{RRE\;Fwd}}\)) depends on two factors: (1) the energy required to transmit the packets collected since the last encounter with a base station (\(\mathrm{E_{tx,packetToday}}\)); and (2) the energy required for the node to run its basic functions (such as scanning for a base station)until its energy is recharged (Escan). The energy threshold for entering the energy conservation state is therefore:  
\[\mathrm{E}^{\mathrm{Th}}_{\mathrm{RRE\ FWd}}=\mathrm{E}_{\mathrm{tx\_packetToday} }+\mathrm{E}_{\mathrm{scan}} \tag{5}\]  
where Etx_packetToday estimates the energy needed to deliver packets to a base station based on the number of packets currently buffered at the node and Escan estimates the energy baseline required until the next energy-harvesting event.  
The following equations define the energy required for forwarding packets and radio scanning:  
\[\mathrm{E}_{\mathrm{tx\_packetToday}}=\mathrm{N}_{\mathrm{packetToday}} \times\mathrm{E}_{\mathrm{tx\_per\_packet}}\times\mathrm{C}_{\mathrm{tx\_ loss}} \tag{6}\]  
\[\mathrm{N}_{\mathrm{packetToday}}=\mathrm{N}_{\mathrm{currentPackets}}\text{ - }\mathrm{N}_{\mathrm{PreviousDayPackets}} \tag{7}\]  
\[\mathrm{E}_{\mathrm{scan}}=(\mathrm{t}_{\mathrm{remaining\_time}}\div\mathrm{ t}_{\mathrm{scanniterval}})\times\mathrm{E}_{\mathrm{rx\_scan}} \tag{8}\]  
where NpacketToday is the number of packets that the node received and/or generated today Etx_per_packet is the amount of energy needed to transmit one packet. As some packets may be lost due to collisions, we use a multiplication factor Ctx_loss to reserve enough energy for packet retransmissions. Finally, \(\mathrm{t}_{\mathrm{remaining\_time}}\) is the time until the node starts harvesting energy next time, \(\mathrm{t}_{\mathrm{scanniterval}}\) is the time interval that the node scans for the presence of another node, and Erx_scan is the amount of energy required for channel scanning.  
Similar to the Threshold algorithm, once the energy falls below a predetermined threshold, the node stops GPS location tracking to reserve its energy for transmitting stored packets directly to the base station.
# Crack Initiation and Propagation Fatigue Life of Ultra High-Strength Steel Butt Joints
## 1 Introduction  
### Fatigue Assessment of Welds Considering Crack Initiation and Propagation  
The fatigue life of a component is a very complex physical process which could be divided to five stages [1]: dislocation movement, crack nucleation, micro- and macro crack propagation and the final fracture. For fatigue assessments of welded joints, the individual consideration of each phase is usually too complicated. Most procedures focus on the stages up to a crack of detectable size. Fracture mechanical approaches, on the other hand, neglect all stages before macro crack propagation. In order to increase the accuracy of technical fatigue assessments, two-phase models considering both crack initiation and propagation are introduced [2]. Hereby, the first three stages are treated as the crack initiation phase (index init) followed by a crack propagation phase (index cp). Thus, the total fatigue life up to burst fracture \(N_{f}\) sums up the cycles of both phases \(N_{init}\) and \(N_{cp}\), respectively. The transition between these two phases is referred to as technical crack initiation at a crack depth \(a_{th}\); its value varies depending on the selected model [3; 4; 5; 6]. In the literature, various approaches exist for an assessment of the crack initiation phase, such as the fatigue notch factor \(K_{f}\)[7; 8; 9], strain life approaches [10; 11] or the notch stress intensity factor [12; 13]. The crack propagation phase, on the other hand, is generally assessed using linear elastic fracture mechanics.
## 3 Results  
### OLI Landsat-8 and PlanetScope NICFI Images of Blowdown Disturbance  
If classified according to the shape of canopy gaps, the blowdowns identified in this study can be divided into three categories: (1) single large gaps (Figure 3(A1,A2)); (2) fan-shaped aggregated gaps (Figure 3(B1,B2)); and (3) discrete clusters of small gaps, whose gaps may not have obvious directional linear characteristics (Figure 3(C1,C2)). Figure 3 also shows the visibility of blowdowns at the different spatial resolutions of Landsat-8 and PlanetScope NICFI images. The single large gaps will usually have a distinct canopy gap which is above 30 ha in size. Fan-shaped aggregated gaps, on the other hand, are fan-shaped aggregations of canopy gaps, which can vary in size from large to small. For discrete clusters of small gaps, they can be seen as a collection of small gaps.  
When comparing the ability of Landsat-8 and PlanetScope NICFI satellite datasets to map the area affected by 25 blowdown events using a \(\Delta\)NPV threshold greater than 0.3 or 0.5 (Table 2), Landsat-8 mapped a larger area (total area = 3809 ha) than PlanetScope NICFI (total area = 3244 ha) when using a threshold of 0.3. However, the use of a higher threshold (\(\Delta\)NPV \(>\) 0.5) reduced the mapped area and led to a similar affected area being mapped by Landsat-8 and PlanetScope NICFI (1962 and 1963 ha, respectively). Furthermore, the number of individual canopy gaps (clusters of pixels mapped as disturbed areas) mapped by PlanetScope NICFI was greater than that of Landsat-8, with 1.4 million pixels of gaps in PlanetScope versus 42 thousand pixels of gaps in Landsat-8. This difference can be observed in Figure 4, where Landsat-8 displays larger contiguous areas of gaps compared to PlanetScope NICFI.