esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Renewable Energy Planning in the Indonesian Context: A Lombok Island Study Case
The community microgrid can be used at the end of the branches of the feeder to improve the consumer power supply, resiliency, flexibility, and stability of the electrical distribution network [12; 13]. This energy management system was developed with the approach of balancing generation and consumption within the renewable energy community [14]. The main objective of this paper is the planning of MGs for the entire energy system to supply critical loads during natural disasters on Lombok Island. There are two case-study areas: Mandalika (located in southern Lombok) and Gili Trawangan Island (in northwestern Lombok). The Mandalika area and Gili Trawangan Island represent a grid-connected MG, and an island MG is part of the Lombok electricity system. The simulation and modelling of the distribution network have just been considered for Gili Trawangan Island in this paper. The main method applied in this research work is the planning of MGs with consideration of electrical constraints. DIgSILENT, as a practical tool, has been used for determining the location and capacity of MGs. In this methodology, at first, the Gili Trawangan feeder (inclusive single line diagram, SLD) is implemented in DIgSILENT, and then the planning of MGs based on electrical constraints is performed in order to supply the critical loads (such as hospitals, clinics, hotels, etc. ), especially in a natural disaster. Depending on the state of the investigated MG planning, the expansion planning can be divided into the location and capacity of MGs. The location deals with improving buses' voltage, i.e., which bus voltage needs to be increased with a MG. Comparatively, the capacity is more focused on supplying the critical loads that require renewable resources for a generation. The expected outcomes of this paper are an analysis of the location-capacity feasibility of a distribution network in Indonesia (the Gili Trawangan feeder on Lombok Island) based on MG technologies. This paper investigates the feasibility of MG deployment in the Lombok electricity system with the following two elements:  
* An overview of the electricity production units and main transmission lines on the island;
* The Indonesian grid codes' compatibility with MG installations. The performance of the long-term planning of the community MGs is evaluated on the Gili Trawangan feeder that is connected to the upstream network via the Tanying substation and supplies some critical loads over several years from 2022 until 2030, corresponding with our assumptions. It should be mentioned that MGs have tried to use actual data for the planning. The rest of this paper is arranged as follows: Section 2 highlights the proposed method of planning. To obtain these, the input data are discussed in Section 3 and demonstrated with a case study in Section 4. Finally, Section 5 concludes this paper.
# Foot strike Pattern and Cadence of the Marathon Athletes at the Tokyo 2020 Olympic Games
###### Abstract  
Background: The footstrike pattern and cadence are two crucial variables associated with sports performance and injury risks. This study aimed to analyze the footstrike pattern and cadence of male elite athletes who participated in the Tokyo Olympic Games marathon. Methods: Two independent researchers examined the footstrike pattern of the first 51 participants at the 5 km mark of the race. Additionally, the cadences of the top eight athletes (finalists) were analyzed in three different segments of the race (10-20 km, 20-30 km, and 30-40 km). Descriptive statistics were used to present the main variables, and a repeated-measures ANOVA was conducted to explore cadence differences among race sections (\(p<0.05\)). Results: The mean cadence of the eight finalists was 185.5 steps per minute (SD \(\pm\) 5.1), and no significant differences were observed among race sections. The most common footstrike pattern was midfoot strike, followed by rearfoot strike, then forefoot strike. The cadence results are consistent with previous studies examining elite athletes, indicating higher values compared with research involving recreational runners. Conclusions: Most elite marathon athletes adopt a non-rearfoot strike pattern and maintain a cadence of more than 185 steps per minute.  
m arathon; performance; footstrike pattern; Olympic Games; cadence; running
## 4 Uplink Reception With Proposed Cooperation
In Figure 6, we plot the average per-layer quantization distortion of the proposed scheme in Section 4 with respect to the fronthaul capacity \(C_{F}\) for a two-cluster C-RAN uplink system with \(M=2\)  
\[R_{\text{C},j,r,1}+R_{\text{C},j,r,2}\leq C_{F},\ \forall i\in\{1,2\},r\in \mathcal{M},\]0 Average sum-rate \(R_{\text{sum}}\) versus the SNR \(P/\sigma_{z}^{2}\) for a two-cluster C-RAN uplink system with \(M=2\), \(K=6\), \(C_{B}=1\), and \(C_{F}=4\). \[R_{\text{C},j,r,1}+R_{\text{C},j,r,2}\leq C_{F},\ \forall i\in\{1,2\},r\in \mathcal{M},\]1 Average sum-rate \(R_{\text{sum}}\) versus the backhaul capacity \(C_{B}\) for a two-cluster C-RAN uplink system with \(M=3\), \(K=10\), \(C_{F}=2\), and \(20\) dB SNR. \(K=6\), \(C_{B}\in\{1,2\}\), and \(P/\sigma_{z}^{2}=20\) dB. We define the quantization distortion \(D_{j}\) of layer \(j,j\in\{1,2\}\), as \(D_{j}\triangleq\sum_{i\in\{1,2\},r\in\mathcal{M}}\omega_{i,r,j}\). The figure shows that, as the fronthaul capacity \(C_{F}\) increases, the quantization distortion of Layer 2 signals, which are described by both basement and enhancement layers, keeps decreasing. However, the distortion of Layer 1 signals, which are described by only the basement layer, is saturated to a certain level if \(C_{F}\) exceeds a threshold value. This is because the basement layer descriptions are transferred on the backhaul links of fixed capacity \(C_{B}\). In Figure 7, we observe the sum-rate cumulative distribution functions (CDFs) of the schemes considered in Figure 5 for a two-cluster C-RAN uplink system with \(M=2\), \(K=6\), \(C_{F}=C_{B}=1.25\), and \(20\) dB SNR. In the figure, we choose \(C_{F}=C_{B}=1.25\) bit/symbol to reflect the system parameters of 5G New Radio (NR) [23] and Common Public Radio Interface (CPRI) specification [24]: Bandwidth per component carrier considered in 5G NR is scalable up to 800 MHz [23], and the fronthaul capacity supported by the CPRI specification ranges from 500 Mbit/s to 12 Gbit/s [24]. We focus on a relatively challenging case where the bandwidth and fronthaul capacity are equal to 400 MHz and 500 Mbit/s, respectively, so that the fronthaul capacity \(C_{F}\) in bit/symbol is approximated to 1.25. The backhaul capacity is assumed equal to the fronthaul capacity, i.e., \(C_{B}=C_{F}\). Figure 7 shows that the proposed cooperation scheme significantly outperforms the conventional cooperation scheme. In particular, in terms of 50%-ile sum-rate, the gain amounts to 38%. \[R_{\text{C},j,r,1}+R_{\text{C},j,r,2}\leq C_{F},\ \forall i\in\{1,2\},r\in \mathcal{M},\]2 Average per-layer quantization distortions of the proposed scheme (Section 4) versus the fronthaul capacity \(C_{F}\) for a two-cluster C-RAN uplink system with \(M=2\), \(K=6\), \(C_{B}\in\{1,2\}\), and \(P/\sigma_{z}^{2}=20\) dB.
## 3 Experimental Testing Results  
### Hysteretic Graph of Specimens  
Figure 4 shows the load-drift hysteretic relationships. Q\({}_{\text{max}}\)(+ve) and Q\({}_{\text{max}}\)(\(-\)ve) represent the maximum positive and negative lateral load-carrying capacities of each specimen, respectively. All specimens had a greater negative lateral load-carrying capacity, i.e., Q\({}_{\text{max}}\)(\(-\)ve), than the positive lateral load-carrying capacity, i.e., Q\({}_{\text{max}}\)(+ve). In the case of positive Q\({}_{\text{max}}\)(+ve), the peak load reached the maximum load-drift response (i.e., deformation capacity) at the drift ratio of 2.25% for BCJ-1, 2.50% drift ratio for BCJ-6, 2.75% drift ratio for specimens BCJ-2 and BCJ-5 and 3.00% drift ratio for specimens BCJ-3, BCJ-4, and BCJ-7. The applied displacement load for the control specimen indicated that BCJ-1 fell under tension failure mode, then the applied displacement load was forced to pull it. This resulted in the maximum negative loading obtained is similar to the maximum positive loading. This indicates that a small load is needed to pull BCJ-1 to the origin, due to low reinforcement area without any seismic strengthening provision. However, in BCJ-2 to BCJ-7, the maximum negative loadings obtained were approximately twice the maximum positive loading, compared to the control specimen. The additional shear link spacing and the increased anchorage length enhanced the strength of specimens BCJ-2 to BCJ-7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Num** &
\begin{tabular}{c} **Parameters Obtained before and after** \\ **Experimental Testing** \\ \end{tabular} & **Abbrev.** & **Unit** \\ \hline
1. & Reinforcement area & A\({}_{8}\) & mm\({}^{2}\) \\
2. & Shear link spacing at beam & SL\({}_{\text{B}}\) & mm \\
3. & Shear link spacing at column & SL\({}_{\text{C}}\) & mm \\
4. & Shear span for additional shear links at beam & SSL\({}_{\text{B}}\) & mm \\
5. & Shear span for additional shear links at column & SSL\({}_{\text{C}}\) & mm \\
6. & Anchorage length at joint & AL & mm \\
7. & Concrete compression strength & C\({}_{\text{c}}\) & MPa \\
8. & Concrete tensile strength & C\({}_{\text{t}}\) & MPa \\
9. & Tensile bar strength & T\({}_{\text{t}}\) & MPa \\
10. & Drift ratio & DR & \% \\
11. & Maximum positive load-carrying capacity & Q\({}_{\text{max}(+\text{ve})}\) & kN \\
12. & Maximum negative load-carrying capacity & Q\({}_{\text{max}(-\text{ve})}\) & kN \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of data opted as potential input parameters for modeling purposes.  
Figure 3: Lateral cyclic loading history for RC beam-column joint specimen.
## 3 Results and Discussion
### Degradation of Reactive Black 5
#### 3.2.2 Effect of Initial Concentration of RB5  
Experiments addressing the effect of the RB5 initial concentration were performed for pure manganese oxides (MnO\({}_{2}\) and Mn\({}_{3}\)O\({}_{4}\)), pure GO (for comparison) and NGO as well as for NGO-Mn\({}_{3}\)O\({}_{4}\) and NGO-MnO\({}_{2}\) nanocomposites in order the maximum removal ability of the catalysts to be estimated; the results are presented in Figure 4. Since the RB5 removal mechanism on the nanocomposite is expected to be complex and to involve adsorption and degradation, the RB5 removal curves were not fitted to any theoretical isotherm. The removal results in Figure 4 indicate that the application of the nanocatalysts at pH 3 presented an increase dye removal with the NGO-MnO\({}_{2}\) to present the highest one. The removal for NGO-MnO\({}_{2}\) was about 600 mg RB5 per gram of the solid material while the order of the removal was GO, Mn\({}_{3}\)O\({}_{4}\) < NGO < MnO\({}_{2}\) < NGO-Mn\({}_{3}\)O\({}_{4}\) < NGO-MnO\({}_{2}\). The removal for NGO found to be about 300 mg RB5/g while for pure GO was about 150 mg/g this increase can be due to the surface chemistry changes of GO after the NaOH modification that led to lower oxygen surface functional groups enhancing this way the adsorption of RB5 molecules. For both nanocomposites, the maximum removal they achieved was higher than that of their precursors.
# Power Control and Channel Allocation Algorithm for Energy Harvesting D2D Communications
## 3 Optimal Joint Power Control and Channel Allocation Algorithm
### Optimal Joint Power Control and Channel Allocation Algorithm
In second step, allocating power to the users, the complexity is \(O\big{(}LNM^{2}\big{)}\) in the worst case, where \(L\) is the iteration number. In third iteration, matching channels between D2D clusters and cellular users by KM algorithm, the complexity is \(O\big{(}M^{3}\big{)}\). The overall computational complexity of the proposed algorithm is \(O\big{(}LNM^{2}\big{)}\).
###### Abstract  
Plastic pollution in the marine environment has turned into an important research topic in recent decades. Until recently, studies were often based on visual assessment only, which is not enough to draw any conclusion about the chemical nature of found plastic items and could lead to incorrect results. Standardized, fast, and efficient low-cost methods for marine plastic litter identification are urgently needed to monitor the occurrence and distribution worldwide. In this paper, we demonstrate that a miniaturized handheld near-infrared spectrometer--MicroNIR--can be used for on-site identification of different plastic polymers. A database containing polymer spectra of the most produced and reported polymer types in the marine environment was created including polyethylene (PE), polypropylene (PP), polyethylene terephthalate (PET), polystyrene (PS), polyvinyl chloride (PVC), polyamide (PA), polycarbonate (PC), polyurethane (PUR), and Silicone. Using spectral match value (SMV, included in the instrument software) for spectra analysis resulted in an accurate classification of all nine polymer types. The method was used for the identification of marine macro-, meso-, and microplastic litter collected on beaches in sediments and seawater and enabled the correct identification of marine plastic litter for macro-, meso- (96%), and microplastics (73%) with exception of totally black items and items less than 1 mm in size. The method and instrumentation presented here are very well suited to support \"Citizen Science\" marine litter monitoring projects during beach cleaning and similar activities.  
plastic identification; marine plastic litter; plastic pollution; near-infrared (NIR) spectroscopy; MicroNIR +
Footnote †: journal: Applied
## 2 Materials and Methods
### Structural and Physical Parameters  
The weight of the prepared glass samples was measured in air and distilled water using a sensitive microbalance based on the Archimedes principle. The density, \(\rho\), of the samples was calculated by using the following equation  
\[\rho=\left(\frac{W_{A}}{(W_{A}-W_{B})}\right)*\rho_{B} \tag{1}\]  
where \(W_{A}\) and \(W_{B}\) denote the sample weight in the air and distilled water, respectively, and \(\rho_{B}\) is the density of distilled water, \(\rho_{B}=0.999 gcm^{-3}\). Molar volumes (\(V_{m}\)) for all glass samples were determined using the following equation  
\[V_{m}=(M_{av}/\rho)(cm^{3}/mol) \tag{2}\]  
where \(M_{av}\) represents the average molecular weight. The average boron-boron separation \(\langle d_{B-B}\rangle\) was calculated by applying the following formula  
\[\langle d_{B-B}\rangle=\left[v_{m}^{b}/N_{A}\right]^{\frac{1}{3}} \tag{3}\]  
where \(V_{m}^{b}\) refers to the volume of boron atoms per mole and is given by  
\[V_{m}^{b}=\
icefrac{{V_{m}}}{{2(1-X_{B})}} \tag{4}\]  
where \(X_{B}\) is the mole fraction and \(V_{m}\) is Molar volume for the glass samples [15]. The ion concentration can be acquired using the next expression  
\[N=(\frac{X\%*\rho*N_{A}}{M_{T}})(\frac{ion}{cm^{3}}) \tag{5}\]  
where \(X\%\) is the mole percent of dopant, \(N_{T}\) is the Avogadro number, and \(M_{T}\) is the molecular weight. Based on the ion concentrations, it is also possible to compute three essential physical parameters, such as Polaron radius (\(r_{p}\)), inter-nuclear distance (\(r_{i}\)) and field strength  
\[r_{p}(A^{\circ})=\frac{1}{2}[\
icefrac{{n}}{{6N}}]^{\frac{1}{3}} \tag{6}\]  
\[r_{i}(A^{\circ})=[\
icefrac{{1}}{{N}}]^{\frac{1}{3}} \tag{7}\]  
\[F=(\frac{Z_{m}}{r_{p}^{2}}) \tag{8}\]  
where \(Z_{m}\) is the atomic mass of the dopant [16; 17].  
The non-crystalline phases for the selected samples were observed using X-ray diffraction (XRD) system PANalytical X'pert PRO (PW3040/60 MPD, Philips, EA, The Netherland). The system was combined with the software of diffraction analysis based on the 2\(\theta\) range from 10\({}^{\circ}\) to 80\({}^{\circ}\) with the steps 0.02\({}^{\circ}\). Perkin Elmer Spectrum 100 (Waltham, MA, USA) instrument was utilized to obtain the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Glass Samples**} & \multicolumn{4}{c}{**mol\%**} \\ \cline{2-5}  & **Li\({}_{2}\)O** & **Al\({}_{2}\)O\({}_{3}\)** & **B\({}_{2}\)O\({}_{3}\)** & **Dy\({}_{2}\)O\({}_{3}\)** \\ \hline
**LABD-0.0** & 23 & 7.5 & 69.5 & 0.0 \\
**LABD-0.2** & 23 & 7.5 & 69.3 & 0.2 \\
**LABD-0.4** & 23 & 7.5 & 69.1 & 0.4 \\
**LABD-0.6** & 23 & 7.5 & 68.9 & 0.6 \\
**LABD-0.8** & 23 & 7.5 & 68.7 & 0.8 \\
**LABD-1** & 23 & 7.5 & 68.5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition ratios for all studied glass samples.  
FTIR spectrum absorption for studied glasses with the size \(<\)63 \(\upmu\)m in Attenuated-Total Reflectance (ATR) mode within wavenumber range of 400-4000 cm\({}^{-1}\).
## 8 Conclusions/Outlook  
In this paper we have presented a novel solution for interacting with a group of CoAP resources across multiple smart objects. It provides an interesting alternative to multicast-based solutions, which are challenging to realize in a constrained environment. It is also an alternative to application-based solutions, which simply program the required functionality. An Entity Manager, which can reside anywhere, turns groups of resources into entities. A strong point of our approach is that it nicely integrates the important aspects of entity management: creation, validation, usage and manipulation. At the side of the constrained devices, it requires no additional complexity, except optional support for profiles in order to realize more powerful validation. The introduction of entity profiles introduces a lot of flexibility and opportunities for further extensions regarding how entities should behave. We have implemented our proposal and demonstrated and validated its feasibility. We have also performed a detailed performance evaluation of our solution. We explored several aspects (overhead, timing, scalability, _etc._) related to the creation, validation and usage in realistic sensor networks and compared it with existing multicast-based solutions. As such, we think that our solution is a powerful enabler for group communication in LLNs and an interesting building block for IoT applications.  
As future work we would like to test our solution on larger test-beds and with different use-cases. We will add more intelligence to the Entity Manager to make it optimize the response time and network overhead. One such optimization could be the adaptation of the CoAP retransmission parameters for certain members based on the history of communication with these members. Also, additional ways to interact with entities, by extending the profiles, will be investigated.
## 4 Penetration of L-Band Signal and Its Influencing Factors
### Influencing Factors in L-Band Penetration  
[PERSON] et al. used the functional relationship between incidence angle and ground penetration depth and found that penetration ability was related to incidence angle, wavelength, and soil characteristics, including water moisture content and structural composition [62]. The longer the wavelength, the stronger the penetration ability, but the penetration ability decreased with soil depth. [PERSON] et al. studied the sensitivity of different canopy densities, mean stand height, and mean diameter at breast height to L-band backscattering [63]. They concluded that canopy density had a greater effect on L-band backscattering than average height and mean diameter at breast height. HV is more sensitive to forest structural parameters than HH, also depending on the tree species. [PERSON] et al. found that the L-band signal was not only related to the optical depth of vegetation, but also to the canopy roughness of tree species and canopy density. [PERSON] et al. found that the L-band was related to ground roughness and even mineral composition, such as hematite reducing the penetration depth. These studies indicate that there are many factors affecting the penetration of the L-band in forests, mainly including incidence angle, polarization mode, forest canopy density, tree species (canopy roughness), crown height (age structure) and moisture. Moreover, slope, season, meteorological factors, forest LAI, leaf direction and so on, also affect the penetration effect.
# Application of Machine Vision Technology in Citrus Production
## 3 Fruit Grading
### Defect Grading  
The external defects of citrus fruits affect the storage and internal quality of citrus fruits [43], so the detection of defects is of great significance for the quality and value assurance of citrus. [PERSON] et al. [44] calculated the probability of pixel distribution in different color intervals of citrus fruits of pests and diseases, and used the calculated complexity measure C(Y) and Shannon information entropy H(Y) as search terms to carry out machine recognition of defective fruit on the search table of pests and diseases. [PERSON] et al. [45] used the illuminance reflection model [46] to perform brightness correction on citrus images with uneven contrast. The corrected image can be fully segmented into defect areas on the fruit surface using threshold segmentation methods. [PERSON], [PERSON] et al. [47] used the dual-light  
Figure 5: Citrus grading system.  
system to capture the invisible defect images of citrus, and optimized the YOLOv5 model by integrating the attention mechanism CBAM and using the DIoU loss function, which improved the average precision by 5.8% compared with the original model.
# Production of Sorbitol via Catalytic Transfer Hydrogenation of Glucose
## 3 Results and Discussion
### Influence of Catalyst Doping Agents
Interestingly, low amounts of fructose were observed during the overall reaction test (<6%), suggesting that, despite the Lewis acid features shown by molybdenum sites, its catalytic activity in glucose isomerization seems quite limited, and the beneficial influence of this metal on the capability of nickel to promote CTH becomes dominant. Figure 4: Kinetic profiles obtained for glucose conversion to sorbitol in the presence of Raney nickel type catalysts in ethanol: (**a**) unpromoted Raney nickel, (**b**) Fe–Cr-promoted Raney nickel, and (**c**) Mo-promoted Raney nickel. Reaction conditions: [glucose] = 90 mM, catalyst to substrate weight ratio = 1:4; reaction temperature = 150 \({}^{\circ}\)C.  
This better performance of Mo as compared to Fe-Cr dopants seems to be related to its stability in the catalyst, something that can be ascertained from the negligible amount of leached Mo detected in the reaction media after the catalytic tests. In this way, the influence of Mo dopant is interpreted based on its capacity to bind glucose to the metal surface. Its oxophilicity leads to the interaction with the aldehyde group of glucose and, thus, an upward configuration is assumed for the carbonyl group, in which the \(\eta\)1 (O) adsorbed species is formed by the donation of a lone electron pair from the oxygen atom at the C=O group to the molybdenum atom. As a result, the C=O bond gets polarized and the carbon atom can undergo a nucleophilic attack from a hydride group located at adjacent nickel atoms, thus leading to its reduction [22; 40]. Hydride groups can be formed via the dehydrogenation of the sacrificing alcohol anywhere over the surface of the Raney nickel catalysts, before migrating through a spillover effect to the CHT sites. These results prompt Mo-promoted Raney-nickel catalysts as a promising selective catalyst for the catalytic transfer hydrogenation of glucose to sorbitol.