esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Forecasting Potential of Photo-Voltaic Electricity Generation and Demand Using R
## 2 Materials and Methods
### Approach  
Machine learning is the ability of a computer to learn from data without explicit programming. The machine learns from the existing data and predicts future data. In supervised learning, algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Source** & **Parameter Name** & **Parameter Unit** \\ \hline NanoGREEN/WPI-MANA micro-grid & total\_pv\_ac & kWh (alternating current) \\  & purchased\_electricity\_kwh & kWh \\ \hline \multirow{6}{*}{Japan Meteorological Agency (JMA)} & temperature & \({}^{\circ}\)C \\  & precipitation & mm \\  & sunlight & numeral (between 0–1 h) \\  & wind\_speed & m/s \\  & solar\_irradiation & W/m\({}^{2}\) \\  & humidity & \% \\ \hline \multirow{6}{*}{Time parameters} & month & month (1–12) \\  & day\_of\_month & day (1–31) \\ \cline{1-1}  & hour\_end & hour (1–24) \\ \cline{1-1}  & day\_of\_week & day (1–7, starting on Monday) \\ \cline{1-1}  & holiday & numeral (0 for no holiday, 1 for holiday) \\ \hline \multirow{2}{*}{
\begin{tabular}{c} National Institute of Materials Science \\ (NIMS) Facility Management Office \\ \end{tabular} } & electricity price & JPY (Japanese Yen) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter names and units.  
Figure 1: Electricity costs per time period (Japanese Yen/kWh).  
so their classification is known [12]. Supervised machine learning can be applied to the data in two forms:  
* Predictive modeling to determine what would be the output if a new (future) input is applied (to estimate future demand and solar power generation)
* Prescriptive modeling to optimization what actions should be taken given the data (to estimate the impact of additional storage devices)  
In this study we want to examine the potential of predicting two parameters: solar power generation and building energy use. We endeavor to evaluate the prediction potential of both parameters with one model using the same input parameters for weather and time, to see to which extent accurate electricity cost forecasts can be achieved.
## 3 Results
### Montesaglioso Test Site  
Figure 2 shows the mean LOS displacement rate maps derived by processing, through the SPINUA algorithm, the S1 ascending (Figure 2a), S1 descending (Figure 2b), and CSK ascending (Figure 2c) datasets over the Montesaglioso test site.  
Differences between previously published MTInSAR results [36; 37], covering a period up to 2013, and ours, reaching 2018, are minimal: all three maps show mostly stable points, i.e., green dots, with low displacement rates, ranging between \(-2\) and \(+2\) mm/y. This means that no major, large-scale processes are ongoing on the area. The target spatial distributions confirm that CSK is more effective over artificial structures (buildings, roads). S1 data are able to capture signals from targets located on the landslide body (indicated by the red contour on the maps). Both CSK and S1 provide interesting deformation time series with nonlinear trends, which are investigated in detail in the following.  
A few points with high displacement rates, reaching in some cases \(\pm 10\) mm/y (red and blue dots), can be discerned on the maps in Figure 2. Starting from these local  
Figure 2: Mean LOS displacement rate maps over Montesaglioso village: (**a**) Sentinel-1 ascending dataset; (**b**) Sentinel-1 descending dataset; (**c**) COSMO-SkyMed ascending dataset. Dashed arrows represent orbit directions. The ground projection of the LOS vector is also shown. The red line delimits the known landslide body.  
displacements, we focused on four different situations: (i) post-landslide monitoring; (ii) monitoring of displacement evolution over a built-up area (a cemetery); (iii) monitoring of a structurally damaged building; (iv) monitoring of displacement evolution over a section of a connecting road.
# Heat Generated during Dental Implant Placement: A Scoping Review
## 2 Materials and Methods
### Cylindrical, Conical, and Trephine Drills  
Another feature that can influence heat generation during implant preparation is the type of cutter employed. In general, milling cutters fall into two macro-categories: cylindrical and conical. [PERSON] et al. conducted a study comparing the effects of cylindrical and conical cutters, examining both continuous and intermittent motion. They collected important data such as mean thermal variation, new bone formation, and polymorphonuclear cell count ([PERSON] et al., 2018). These findings revealed that the conical burr, whether used with continuous or intermittent motion, showed better results, with reduced thermal variation, increased new bone formation, and fewer polymorphonuclear cells after osteotomy. Measurements were carried out using K-type thermocouple sensors, positioning the gauge exactly 1 mm from the implant preparation area.  
[PERSON] et al. evaluated several variables in the context of implant preparation, also using K-type thermocouple sensors. They found that conical cutters generated heat without significant variations. In cylindrical drills, however, a relatively consistent increase in temperature was found with increasing depth ([PERSON] et al., 2018).  
In a different study by [PERSON] et al., traditional burs were compared to trephine hollow burs and manual bone expanders. This study found that trephine burs produced the highest temperature values, followed by conventional burs and then bone expanders ([PERSON] et al., 2018).  
[PERSON] et al. compared conventional and hollow cutters (trephine drills) and found that the absence of irrigation led to much higher temperatures compared to the groups  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Pubmed** & **Embase** & **Web of Science** \\ \hline Drills for dental implants & \(n=788\) & \(n=160\) & \(n=880\) \\ Speed and force during osteotomy & \(n=24\) & \(n=28\) & \(n=22\) \\ \hline Heat generated by drills for implants & \(n=108\) & \(n=11\) & \(n=41\) \\ \hline External and internal irrigation of drills for dental implants & \(n=5\) & \(n=4\) & \(n=9\) \\ \hline Heat generated during osteotomy & \(n=88\) & \(n=26\) & \(n=40\) \\ \hline Effect of heat on osseointegration & \(n=90\) & \(n=51\) & \(n=150\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Database search.  
with irrigation. The only difference identified was that the effectiveness of irrigation was lower with hollow drills, likely due to their different shape and smaller cutting surface [27].  
Notably, despite presenting significant differences in terms of heat generation among milling cutters, none of the cases analyzed exceeded the limit value set in the literature.
## 4 Experiment and Results
### Exploring the Effects of Different Impact Factors on Arctic Sea Ice Prediction  
We investigated how typical oceanic influencing factors affect the prediction of Arctic sea ice, which aids in enhancing the selection of model input variables, thereby improving the model's accuracy and effectiveness. We selected three typical oceanic factors: sea surface temperature (SST), sea water salinity (SALT), and sea ice thickness (THIC). The method of integrating oceanic influencing factors involves overlaying SIC with SST, SALT, and THIC, respectively, and then inputting these data into the PC module. The output feature map is then fed into the main network to obtain the final prediction results. We evaluated the impact of specific parameters using RMSE (root mean square error) and MAE (mean absolute error), with a lower RMSE value indicating a greater influence of that parameter on the PDED-ConvLSTM model's predictions.  
Figure 12 displays the RMSE trends for different input parameters in the PDED-ConvLSTM model during the 2018 melt season. SST consistently shows a noticeable negative contribution throughout all times, while SALT and THIC have both positive and negative impacts. During the May to July, the inclusion of SALT and THIC positively  
Figure 10: Spatial distribution of PDED-ConvLSTM predicted and ground truth values with a five-month lead time. (**a**) Ground truth for 2018. (**b**) Prediction for 2018. (**c**) Ground truth for 2019. (**d**) Prediction for 2019. (**e**) Ground truth for 2020. (**f**) Prediction for 2020.  
In summary, during the melting season, SST has a negative impact on SIC predictions with a five-month lead time. SALT and THIC positively contribute to predictions before August and negatively after August. Therefore, if we are predicting SIC from May to July, THIC and SALT should be added as inputs. If the prediction is from August to September, only SIC should be used as an input.
## 3 Pretreatment Methods in the Chain of Biogas Production
### Limitations  
It can be observed that the efficiency of pretreatment methods depends on the feedstock and its composition. It is a complex issue to determine the optimal pretreatment method(s) of lignocellulosic biomass. Regardless of the pretreatment method to be employed, the particle size reduction of lignocellulosic biomass is the most common primary pretreatment method employed. Though, in general, the mechanical pretreatment is significantly related to increase methane yield, one of the disadvantages of mechanical process is its inability to remove the lignin, a critical barrier with regard to bioavailability of carbohydrates for biomethanation. [PERSON] et al. [176] suggested that lignocellulosic biomass must be reduced to 1-2 mm to eliminate limitations during hydrolysis; however, size reduction is a very expensive operation that consumes about 33% of the total electricity demand  
for the whole process. Considering the high energy requirement of mechanical pretreatment and the rise of energy cost, it is not economically sustainable. Thus, reducing the energy requirements and increasing efficiency of grinding and milling of biomass would help to improve the economics of the whole process.  
In the case of chemical pretreatment, cost of reagents, and operation such as the extra step for neutralization and the requirement for corrosion resistant reactors are the known limitations [178]. Additionally, the formation of inhibitory compounds is a point to be considered since it can inhibit or significantly reduce the conversion efficiency of hydrolyzates of lignocellulosic biomass to methane. Thus, achieving higher efficiency and lowering the formation of inhibitory compounds at by combining lower concentration of chemical reagents with other pretreatments could aid in cost reduction. In the case of alkali pretreatment, it is effective in solubilization of lignin and the presence of a small amount of residual alkali in the treated biomass aids in neutralizing the pH reduction during the acidogenesis phase of biorethanation process. Therefore, alkaline pretreatment is more compatible with subsequent anaerobic digestion when compared to acid pretreatment [179].  
On the other hand, organic solvents permit the recovery of individual components of lignocellulosic biomass, which make an improvement in capital cost in a biorefinery concept. However, large amounts of downstream wastes and specialized equipment are limitations [102]. Ionic liquids are superior to organic solvents for use in a wide range of applications and in extreme conditions [100], however, the cost and the need for recycling need is a limitation.  
In spite of the advantages, biological pretreatments also have several major drawbacks such as specific growth conditions, larger space, longer treatment time, and loss of carbohydrates [178]. Biological pretreatment presents low formation of inhibitory compounds and in general its inhibition effect in the subsequent step of anaerobic digestion is low, when compared with chemical and physiochemical pretreatments.
## 3 Results and Analysis
### Mechanical Properties  
Figure 10a shows the tensile curves of the welded joints. Figure 10b shows that the yield and the tensile strength of the base metal are 242 MPa and 327 MPa, respectively, and that the elongation is about 16.9%. The yield strength, tensile strength, and elongation of the welded joint are much weaker than that of the base metal. For three kinds of welded joints at 3, 4, and 5 Hz, there is no significant difference in tensile strength, yield strength, and elongation.  
Figure 10: \(Cont.\)  
Figure 9: Microhardness distribution in welded joints.  
Figure 11 shows photographs of fractured tensile samples. When the thermal frequency is 3, 4, and 5 Hz, tensile fractures of the welded joint are mainly composed of a large number of irregular dimples. In the bottoms of the dimples, inclusions and second phase particles are observed, indicating that the specimens have obvious plastic deformation before fracturing, and that the fracture belongs to a ductile fracture mode.  
Three point bending test was adopted to the DP-GMAW welded samples to check the ductility of samples. The samples were carried out until 180\({}^{\circ}\). A macroscopic photograph of the bended samples is shown in Figure 12. After bending up to 180\({}^{\circ}\), no tearing, crack, and other defects are observed through a careful visual examination, which indicates the good ductility of the samples. As shown in  
Figure 11: Photographs of fractured tensile samples.  
Figure 10: Tensile properties of the base metal and welded joint. (**a**) Tensile curves; and, (**b**) Result of tensile tests.  
Figure 7, the gain size of fusion zone is mostly concentrated in a small range from 20 \(\upmu\)m to 50 \(\upmu\)m, which may be the main reason for the excellent bending result.  
In this research, notches for the impact test of the welded joints were placed at the fusion zone and base metal, respectively. The impact properties of the base metal and the welded joint are shown in Table 4. It is observed that the absorbed energy of the base metal is 7.08 J, and that the absorbed energy of the welded joint is much lower than that of the base metal, which is about 30.8-31.9% of the latter. The SEM fractographs of Charpy impact test specimen are shown in Figure 13. It
## 2 Materials and Methods  
### Subjects and Eligibility Criteria  
This retrospective study evaluated 28 patients aged 120-48.4 years (mean age, 20.4 \(\pm\) 73 years; 12 males, 16 females). The patients were diagnosed as having transverse maxillary deficiency and received orthodontic treatment at Wonkwang University Daejeon Dental Hospital in Daejeon, South Korea. This study was approved by the Institutional Review Board of Wonkwang University Daejeon Dental Hospital (IRB number: W2205/002-001).  
The inclusion criteria were as follows: (1) patients who were treated with MSE, (2) the successful opening of the midpalatal suture, and (3) having CBCT images taken at pre-expansion (T1), post-expansion (T2), and 6-months after expansion (T3). The exclusion criteria were as follows: (1) patients with congenital craniofacial anomalies, (2) a history of facial or head trauma, and (3) previous orthodontic or orthopedic treatment and/or orthognathic surgery.  
In this study, the same type of MARPE appliance, a maxillary skeletal expander (MSE) (BioMaterials Korea, Seoul, South Korea), was used on all patients. The MSE had four holes and was fixed with four mini-implants (1.5 mm in diameter and 11.0 mm in length). The activation was performed according to the manufacturer's instructions. The activation rate was 1 turn per day for early teens (<15 years), 2 turns per day for late teens (\(\geq\)15 years), and 4 turns per day for adult patients (>20 years) before the midpalatal suture opening. Once the diastema was identified, the activation rate was set as 2 turns per day for all patients regardless of age until proper expansion was achieved (Figure 1).  
CBCT images were taken at T1, T2, and T3. A CBCT scanner, Alphard-3030 (Asahi Roentgen Ind, Kyoto, Japan), was set at 80 kVp and 7.0 mA for adults, and 80 kVp and 3.0 mA for adolescents. The images were taken in a cranial mode (scan size, 200 \(\times\) 179 mm; scan time, 17 s; voxel size, 0.39 mm; slice thickness, 1.0 mm).
## 3 Results
### Analysis of Clusters
#### 3.1.2 Initiating Causes of Outages  
The 6942 outages in clusters are divided into 2007 momentary outages and 4945 sustained outages (i.e., outages lasting at least 1 min). The percentage of sustained outages in clusters is significantly higher than in the total population of automatic outages for years 2013-2019 (71% versus 58%). Figure 2 lists the outages by TADS initiating cause. Several of the smallest groups are not shown (together they contain less than 1% of outages in clusters).  
Lightning initiates the largest number of outages in clusters, but the majority of them are momentary. In contrast, Failed AC substation equipment is the leading cause of sustained outages in clusters, but it initiates a relatively small number of momentary outages. Power system condition is the third largest group. Next, we compare rankings of causes for outages in all clusters, in the largest clusters (size five and above), and for all TADS outages for the 7 years (Figure 3).  
Figure 1: Distribution of cluster sizes (2013–2019).  
Figure 2: Outages in clusters by initiating cause (2013–2019).  
Lightning, the top cause of outages in clusters, is the second leading cause of all automatic outages in TADS, but it initiates only 8% of outages in large clusters. Unknown, the leading cause of TADS outages, ranks relatively low for clusters: it initiates 9% of outages in clusters and only 3% of outages in large clusters, because causes of larger transmission events tend to be better investigated and reported.  
Prominently, Power system condition causes 25% of outages in large clusters while in TADS it ranks low (4% of TADS outages). This cause is reported for automatic outages caused by power system conditions such as instability, overload trip, out-of-step, abnormal voltage, abnormal frequency, or unique system configurations (e.g., an abnormal terminal configuration due to existing condition with one breaker already out of service) [9].  
A variety of malicious attacks included Vandalism, Terrorism, or Malicious Acts, which caused only five outages in the clusters for the 7 years and no outages within large clusters--that being the reason why this cause is not shown in Figure 3.
# Satellite Edge Computing for the Internet of Things in Aerospace
## 5 Performance Evaluation
For energy consumption, in general, as the number of terminals increases, the total energy consumption increases. As the number of orbit planes decreases, the total energy consumption decreases. This is because of the reduction in the number of satellites. When the number of satellite orbital planes is six and four, the energy consumption in the conventional mode is more significant than that in the edge computing mode. When the number of satellite orbital planes is two, the result is reversed. This is because when the number of satellites decreases, in the space edge computing mode, the energy consumed by a single satellite and the working time increase. Therefore, in order to obtain a better quality of service, the system needs to be optimized for the number of satellites. Next, we evaluate the impact of the number of the VM on the satellite on the task time spent and energy consumption, as shown in Figure 7. In the traditional mode, the time and energy consumption of the satellite is almost independent of the number of virtual machines on the satellite. In space edge computing, the time spent decreases as the number of VMs increases and is much smaller than the time spent in the traditional mode. However, as the number of virtual machines continues to increase, time consumption will tend to be a constant value. For satellite energy consumption, it increases as the number of virtual machines increases. Furthermore, when the task generation rate of the terminal is low (\(\lambda\) = 0.1), the increase in energy consumption is not apparent. This is because the satellite performance is strong enough to meet the mission requirements. Additionally, when the task generation rate is high and the number of VMs is large, the energy consumption in the edge computing will be higher than the traditional mode. Therefore, it is not possible to reduce mission execution time by unrestrictedly increasing satellite performance, and it also leads to a sharp increase in cost. It is necessary to find a balance between time spent and energy consumption. Finally, we will discuss the impact of the task offloading strategy on the time spent and energy consumption, as shown in Figure 8. Other things being equal, the task time spent is different under different offloading strategies. When the tasks are all performed on the satellite, it takes the shortest time. However, as the number of VMs on the satellite increases, the energy consumption will rise sharply. In other offloading strategies, the offloading strategy by task category takes less time than the random offloading strategy. The energy consumption of the two is similar. Therefore, for different task scenarios, the task offloading strategy is also one of the crucial factors that affects the quality of service. Figure 6: **(a)** the total time spent in terms of the number of terminals; **(b)** the total energy consumption in terms of the number of terminals.
## 2 Methods
### Inversion Methods  
The theoretical basis of travel time tomography is the Radon transform. Assuming that \(v(x,y)\) is a continuous velocity field, the travel time can be expressed as  
\[t=\int\limits_{L}s(x,y)dL \tag{24}\]  
where \(t\) is travel time, \(s(x,y)=\frac{1}{v(x,y)}\) is the slowness field, and the integral path \(L\) is the ray path from the source point to the receiving point. The first step in solving this problem is to discretize the slow field. In this way, Formula (25) can be obtained:  
\[t_{i}=\sum_{j}t_{j}^{i}S_{j} \tag{25}\]  
where \(i\) is the ith ray, \(j\) is the jth discrete slowness grid, and \(l\) is the length of the ray segment in each slowness grid. \(I_{j}^{i}\) is the distance traveled by the ith ray in the jth grid, as shown in Figure 7.  
Figure 6: Absolute error contour map: (**a**) first-order FMM; (**b**) first-order MSFM; (**c**) second-order FMM; (**d**) second-order MSFM.  
Therefore, we can transform the nonlinear problem of travel time tomography into a linear problem. When there are \(M\) rays passing through the region to be inverted, Equation (25) becomes a linear system of \(M\) equations:  
\[\left[\begin{array}{c}t_{1}\\ t_{2}\\ t_{3}\\ \dots\\ t_{M}\end{array}\right]=\left[\begin{array}{cccc}L_{10}&L_{11}&\dots\dots&L_{ 1N}\\ L_{20}&L_{21}&\dots\dots&L_{2N}\\ L_{30}&L_{31}&\dots\dots&L_{3N}\\ \dots&\dots&\dots\dots&\dots\\ L_{M0}&L_{M1}&\dots\dots&L_{MN}\end{array}\right]\left[\begin{array}{c}s_{0} \\ s_{1}\\ s_{2}\\ \dots\\ s_{N}\end{array}\right] \tag{26}\]  
where \(t_{i}(i=1,2,\dots,M)\) is the travel time vector, \(s_{i}(i=1,2,\dots,N)\) is the slowness vector, and the distance matrix \(L_{M\times N}\) is generally a large sparse matrix.  
Then the Tikhonov regularization function [42] is introduced to transform the inversion problem into the minimum value problem for solving the following objective functions:  
\[\Phi(s)=\Phi_{d}(s)+\lambda^{2}\Phi_{m}(s) \tag{27}\]  
where \(\Phi_{d}(s)\) and \(\Phi_{m}(s)\) are the objective functions in the data space and the model space, respectively. \(\lambda\) is the regularization factor, which is used to adjust the weight between the data space \(\Phi_{d}(s)\) and the model space \(\Phi_{m}(s)\).  
In the travel time tomography inversion of refracted waves, \(\Phi_{d}(s)\) can be expressed as follows:  
\[\Phi_{d}(s)=\left\|W_{d}[t-d(s)]\right\|^{2}=\frac{1}{2}\sum_{i,j}^{N_{d}}[t_{ i}-d_{i}(s)]W_{ij}^{d}[t_{j}-d_{j}(s)] \tag{28}\]  
Further, \(\Phi_{m}(s)\) can be expressed as follows: