esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Validation
### Acquisition of Experimental Data
#### 3.2.1 Experimental Environment  
The object used in this study was a heavy-duty CNC machine tool. The machine tool has a width of 9.7 m and height of 8.5 m and the dimensions of the concrete foundation are 57 m \(\times\) 17 m \(\times\) 7 m (length \(\times\) width \(\times\) height). The ambient environment was a workshop in Shunyi District, Beijing City with ceiling lights and no glass windows. The door of the workshop was located 15 m from the closest end of the machine tool and all other factory operations were halted during the experiment. There was no vibration in the surrounding environment. The experiment was carried out in April of 2019 and the indoor temperature difference was 3-4 \({}^{\circ}\)C (Figure 3).
## 5 Experimental Results and Discussions
### Complexity Analysis of CSI Coloring  
Each basic input data sample \(X_{c}(t:t+h)\) is composed of \(S\times h\) amplitude values. Given \(X_{c}(t:t+h)\), the _Median_ method performs a median operation on \(\{\bar{a}_{c,s}(t),\ldots,\bar{a}_{c,s}(t+h-1)\}\) for each subcarrier \(s\in\{0,S-1\}\). Even though the time complexity of the median operation depends on the sorting algorithm, it is generally supposed to be \(O(h\log h)\). Since there are \(S\) subcarriers, the time complexity of the _Median_ method is \(O(Sh\log h)\). In the case of the _FFT_ method, FFT and IFFT operations are performed on \(\bar{a}_{c,s}(t+i)\)s (\(0\leq i\leq h-1\)) for each subcarrier \(s\). Since the time complexities of both FFT and IFFT are \(O(h\log h)\), the time complexity of the _FFT_ method is \(O(Sh\log h)\). The computational complexity of PCA depends on the applied method and the data size. When the PCA of data \(X_{c}(t:t+h)\) is computed by eigendecomposition, the time complexity becomes \(O(Sh^{2}+h^{3})\). When PCA is obtained by SVD (singular value decomposition), the computation complexity becomes \(O(Sh\min(S,h))\).  
When the CSI amplitude coloring method is applied, each \(\bar{a}_{c,s}(t+i)\) in \(X_{c}(t:t+h)\) is expanded by \(f_{R}(\bar{a}_{c,s}(t+i))\), \(f_{G}(\bar{a}_{c,s}(t+i))\), and \(f_{B}(\bar{a}_{c,s}(t+i))\). To obtain the output values of these functions, only a fixed number of comparison operations are required. For example, as shown in Equation (4), to determine \(f_{R}(\bar{a}_{c,s}(t+i))\), up to just three comparisons with \(\bar{a}_{c,s}(t+i)\) are needed (i.e., comparison with 0.35, 0.66, and 0.89). Since the time complexity of the comparison operation is \(O(1)\), the computational complexity of expanding each \(\bar{a}_{c,s}(t+i)\) into three channels becomes \(O(1)\). Since there are \(Sh\)\(\bar{a}_{c,s}(t+i)\)s in \(X_{c}(t:t+h)\), the time complexity of our CSI amplitude coloring method becomes \(O(Sh)\), which is the smallest among the preprocessing methods.  
We measure the time it takes for each method to preprocess \(X_{c}(t:t+h)\). When we measure the real-time performance, we use the same hardware and software to train the CNN. We show the measurement results in Table 1. In the table, the _Gray Image_ column shows the time to make \(X_{c}(t:t+h)\) from the measured CSI dataset. The gray image shows the smallest amount of time because other preprocessing methods are performed after the gray image (i.e., \(X_{c}(t:t+h)\)) is constructed. In the table, we observe that our method and the _PCA_ method take a similar amount of time to preprocess \(X_{c}(t:t+h)\). Our method is 15.12 faster than the _Median_ method. Compared to the _FFT_ method, our method reduces the preprocessing time by a factor of 37.33. Therefore, considering the sensing accuracy and preprocessing time, we believe that the proposed method is a better choice for resource-constrained devices than other conventional methods.
## 6 Results  
This section presents the results of analyzing the bibliometrics and content of the papers, including the classification made. The results are presented along with each of the research questions that were defined in the methodology.  
_RQ1: What are the trends of publications on terrestrial DTN?_  
The first question includes the number of publications per year found among the papers included in the SLR. A total of 155 papers were included from the period from 2004 to 2023. As it is shown in Figure 2, most of the publications were between 2010 and 2019, with 2014 having the highest number of papers. The main topics in that period revolved around security, sensors, cellular, and vehicular networks. This is a clear demonstration of the interest of finding ways to use this technology in new scenarios to penetrate the market of mobile and wireless technologies even in city areas where telecommunication infrastructure is present, clearly to reduce the costs of data transmission. A key aspect to overcome is to make the interoperability seamless for the final user.  
_RQ2: What are the main issues studied in the literature regarding implementing DTN in terrestrial applications?_  
The results show not only the main categories for the issues addressed in the papers, performance, routing, applications, and architecture but also include the papers that focused on security. Figure 3 below shows the number of papers per category.  
Figure 2: Publications per year.  
An important number of research articles includes performance and routing as their main topic, but to get deeper into each of them, they were separated. The performance of DTN is a main issue, and so almost all the papers have a section about it, and the most recurrent topics were mobility, storage, processing capabilities, and energy.  
This section addresses architecture, performance, and routing, whereas the results related to applications are presented in research question 3.
## 2 Background: Data and Autoregressive Models
### AutoRegressive Integrated Moving Average (ARIMA) Models
#### 2.4.2 ARIMA Variants  
In cases of seasonal time series, it is possible, during the analysis, for the forecasting model to be partially shaped by non-seasonal, short-duration features of the data. Consequently, the formulation of a seasonal ARIMA model is required, which incorporates seasonal and non-seasonal factors into a combined model. The general form of a seasonal ARIMA model is represented by the formula: \(ARIMA(p,d,q)x(P,D,Q)_{S}\) where p is the non-seasonal AR order, d is the non-seasonal differencing order, q is the non-seasonal MA order, P is the seasonal AR order, D is the seasonal differencing order, Q is the seasonal MA order and S is the recurrence time range of the seasonal pattern. The most important step in calculating a seasonal ARIMA model is determining the values of the parameters (p, d, q) and (P, D, Q).  
The ARIMA technique has evolved over the years, resulting in the development of many variants of this model, such as the SARIMA (Seasonal ARIMA) and ARIMAX (ARIMA with Explanatory Variable) techniques. These models perform well in terms of short-term forecasts, but their performance is severely degraded for long-term predictions [8].
## 3 Methodology
### Data
Interestingly, the chargers located in neighborhood facilities in Jeju-do were actively used; it is conjectured that as Jeju-do entered the maturity phase of the EV program, EVs became widely used for general daily activities (not only for tourism or the government's business activities). Figure 2 shows that the charging demand (in our study, occupancy rates) varied depending on the charging time. In particular, the charging demand in Gangneung-si was concentrated around night times, whereas the charging demand in Jeju-do was evenly distributed throughout the time and day of the week. In Gangneung-si, EVs are widely used for tourism, i.e., visitors tend to visit Gangneung-si on weekends. Accordingly, the charging demand appeared to be high on weekends and at night. The trend of charging demand in Jeju-do can be interpreted as EVs being widely used for various travel purposes, including household activities, tourism, and business. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Number of Chargers**} & \multirow{2}{*}{**Average Charger Occupancy Rate**} \\ \cline{2-2} \cline{4-5}  & & **Corresponding to Each Variable** & & \\ \cline{2-5}  & **Jeju-do** & **Gangneung-si** & **Jeju-do** & **Gangneung-si** \\ \hline Total & 1821 (100\%) & 128 (100\%) & 8.5\% & 7.0\% \\ \hline Government offices & 191 (10.5\%) & 6 (4.7\%) & 14.6\% & 15.2\% \\ \cline{2-5}  & Public facilities & 331 (18.2\%) & 33 (25.8\%) & 11.0\% & 5.7\% \\ \cline{2-5}  & Private offices & 142 (7.8\%) & 8 (6.3\%) & 5.7\% & 1.6\% \\ \cline{2-5} Facility type where & Commercial facilities & 78 (4.3\%) & 5 (3.9\%) & 5.2\% & 5.9\% \\ \cline{2-5} charger is installed & Accommodations & 277 (15.2\%) & 13 (10.2\%) & 5.8\% & 5.5\% \\ \cline{2-5}  & Tourism facilities & 543 (29.8\%) & 47 (36.7\%) & 8.6\% & 11.0\% \\ \cline{2-5}  & Neighborhood facilities & 49 (2.7\%) & 7 (5.5\%) & 13.1\% & 3.4\% \\ \cline{2-5}  & Parking facilities & 210 (11.5\%) & 9 (7.0\%) & 15.5\% & 5.6\% \\ \hline \multirow{2}{*}{Charger Type} & Slow charger & 1672 (91.8\%) & 114 (89.1\%) & 8.5\% & 7.3\% \\ \cline{2-5}  & Fast charger & 149 (8.2\%) & 14 (10.9\%) & 8.9\% & 5.3\% \\ \hline \multirow{2}{*}{Parking Fee} & Fee charged & 379 (20.8\%) & 20 (15.6\%) & 6.8\% & 7.1\% \\ \cline{2-5}  & Free & 1442 (79.2\%) & 108 (84.4\%) & 9.0\% & 7.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Frequency of the considered variables and average EV charger occupancy rates.
## 3 Results and Discussion
### Determination of Significant Factors in Moisture Loss during the OD Operation
[29], observed reductions in the percentage of moisture in mango and guava samples of up to 10.5 and 5.88%, respectively, using ratios of the solution in a sample of 1:1 and 2:1 sucrose to glucose and sucrose with immersion times between 2 h and 48 h.
## 3 Results for the Research Questions
### RQ5: What Are the Datasets That Have Been Used for Expert Finding Systems?
Moreover, reference [81] used a real-world breast cancer diagnosis data set, and reference [15] used real data for experts in enterprise. Also, reference [79] used enterprise resource planning (ERP) systems as a source of expertise evidence. UNESCO research projects data has been used by the authors of reference [78] to discover experts. The authors of references [26; 31; 39; 42; 55] used extracted information \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & & & & \\ \hline
**TREC** & enterprise & 2007 & & & & 370,715 & [27; 74] \\
**UvT** & academia & 2006 & & & & 331,037 & [77] \\
**TU** & academia & 2006 & & & & 36,699 & [40; 43] \\
**DU** & academia & 2008 & & & & 31,209 & [46] \\
**DBLP** & academia & 2008 & & & & \(\geq\)2,300,000 & [5,24; 45; 49; 54; 67; 68; 69; 70] \\
**Citeseer** & academia & 2008 & & & & \(\geq\)750,000 & [47; 66; 68] \\
**LExR** & academia & 2015 & & & & 11,942,014 & [6] \\
**Twitter** & Social network & 2009 & & & & - & [88] \\
**Microsoft** & academia & 2016 & & & & \(>\)220,000,000 & [47] \\
**Academic Search** & academia & 2016 & & & & \(>\)1,000,000 & [27; 38; 47; 62; 63] \\
**ACM Digital** & academia & 1950 & & & & \(>\)1,000,000 & [27; 38; 47; 62; 63] \\
**Library** & academia & 2004 & & & & \(>\)389,000,000 & [47; 50] \\
**Google Scholar** & academia & 1988 & & & & \(>\)4,500,000,000 & [47] \\
**IEEE Xplore** & academia & 2006 & & & & - & [47] \\
**Facebook** & Social network & 2007 & & & & - & [20; 22; 25] \\
**Academia.edu** & academia & 2008 & & & & \(>\)21,000,000 & [47] \\
**Wikipedia** & academia & 2001 & & & & \(>\)40,000,000 & [56; 57] \\
**Stack Overflow** & question and answer website & 2008 & & & & - & [16; 19; 83] \\
**Yahoo! Answer** & question and answer website & 2005 & & & & - & [17] \\
**Ask Me Help** & question and answer website & 2003 & & & & - & [85] \\
**Desk** & answer website & & & & & & [85] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Data set characteristics. from university websites. Additionally, data sets from online forums have been extracted; for example, the authors of references [18; 82] extracted data sets from the Microsoft Office Discussion forum and Oracle corporation forum, respectively. Also, the Mozilla Firefox open bug repository has been used by the authors of reference [36].
## 3 Proposed Overload Control Algorithm
This equation calculates the percentage efficiency gained by reducing the number of comparators in the AFP design compared to the conventional flash ADC. A higher efficiency value indicates a greater reduction in hardware complexity. Figure 2(a) demonstrates a significant reduction in hardware complexity while maintaining functional equivalence. At the same time, Figure 2(b) shows a progressive increase in efficiency, reaching up to 72.58% at 8-bit resolution, highlighting the suitability of the AFP for resource-constrained, real-time control systems. These results visually confirm \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Bit**} & \multicolumn{2}{c}{**Conventional ADC**} & \multicolumn{2}{c}{**AFP**} & \multirow{2}{*}{**Efficiency (\%)**} \\ \cline{2-2} \cline{4-5}  & **\# of Comparator** & & & **Power (mW)** & & **\# of Comparator** & **Power (mW)** \\ \hline
4b & 15 & 16.94 & 9 & 16.94 & 15.38 \\ \hline
5b & 31 & 23.01 & 17 & 23.01 & 42.37 \\ \hline
6b & 63 & 34.05 & 33 & 34.05 & 57.20 \\ \hline
7b & 127 & 57.46 & 65 & 57.46 & 63.86 \\ \hline
8b & 255 & 87.32 & 129 & 87.32 & 72.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the conventional flash ADC and the proposed analog function processor (AFP) in terms of comparator count, power consumption, and efficiency. that the proposed AFP architecture not only minimizes hardware complexity but also achieves substantial efficiency gains, validating its effectiveness for real-time, low-resource applications in the DCS.
# Combined MvdXML and Semantic Technologies for Green Construction Code Checking
## 5 Case Study of Green Construction Code Checking
#### 9.2.4 Extra item: Cast-in-place concrete raw materials should be mixed in construction site.  
The constructed ontology model and rules are stored in the knowledge base and rule base, respectively. They are integrated to conduct inference in the JESS inference part. The inference result from the JESS inference engine is shown in Figure 17. The result shows that \"cast-in-place concrete\" is composed of materials \"cement\", \"sand\", \"stone\", and \"water\". The \"Rectangular_Beam\" and its attributes are defined in the conceptual model ontology, while \"cement\", \"sand\", \"stone\", and \"water\" are defined in the work item ontology. The inference process is related to construction condition knowledge. Therefore, the realization of this inference process is based on the concept model ontology, work item ontology, and construction condition ontology.  
SQWRL rule:  
Cast_In_Place_Concrete(?c) ^ hasComponents(?c,?z) ^ hasMaterials(?z,?d) -> sqwrl:select(?d) ^ sqwrl:orderBy(?d).  
Class 4: Code checking based on construction condition reasoning.  
3.2 Extra item: Construction field noise emission at daylight should not exceed 70 dB (A) and should not exceed 55 dB at night (A).  
This kind of clause is closely related to the construction site environment. The noise monitoring in the construction process is embodied in the construction condition ontology. The PM2.5_Sensor and Sound_Sensor are set in ontology (see Figure 18). The diagram of classes and properties in construction condition ontology is shown in Figure 19.  
Figure 17: Inference result.  
For example, if the entry place of the construction site is the fixed position of the Radio-frequency identification (RFID) reader, the component, material, and the person after passing the RFID tag will have the position information, which can be used for ontology inference, along with other attributes, to judge the execution of the service. The construction site will be equipped with noise sensors to collect noise data; when the noise data exceeds the threshold specified in green construction code, an early warning will be triggered automatically. The overrun alarm for noise values is shown in Figure 20. The rule expressions are as follows:  
SWRL rule1 (?Rfid_Tag t1:beOwneredBy?Prefabricate_Component) (?Rfid_Tag t1:IdentifiedBy?entrance_Rfid_Reader) -> (?Prefabricate_Component t1:locatedAt?Entrance).  
SWRL rule2  
Figure 19: The diagram of classes and properties in construction condition ontology.  
Figure 18: The sensor set in construction condition ontology.  
(?Prefabricate_Component t1:locatedAt?Entrance) -> (?Service t1:beCalled t1:Register). SWRL rule3 (EarthRockWork t1:MoniterdBy?Sound_Sensor) (?Sound_Sensor t1:hasStatus t1:is_unnormal) -> (?Service t1:beCalled t1:Alarm). SQWRL rule Sound_Sensor (?p) ~ hasValue(?p,?s) ~ swrlb:greaterThan(?s,?0) -> unnormal(?p).  
Finally, all the code checking results can be summarized to a HTML report (see Figure 21).  
Figure 21: The HTML report of code checking.  
Figure 20: The overrun alarm for noise values.
# Molecular Profile of Skin Cancer
## 4 Gene Mutations in NMSC  
Besides melanoma, there is also the heterogeneous group of NMSC, where two main entities are found: basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). Each has its own genetic and epigenetic pathway of development. While the PTCH1 gene mutation is thought to be the most common cause of the genesis of BCC by inadequately activating the Hedgehog pathway, in SCC, the pathophysiology is still not clearly explained. Activation of RAS is pretty common in human SCC, but mutations in other genes such as XPC are also acknowledged [40; 41; 42; 43; 44; 45]. According to [PERSON] et al. [40], the loss of expression through deletions in the 3p chromosome region and mutations of the XPC gene may happen early during skin carcinogenesis. However, their exact mechanism of action in the tumorigenic process remains unclear. In contrast to melanoma, in NMSC, other potential oncogenic gene mutations were found. For example, an analysis conducted by [PERSON] et al. [41] came to the conclusion that there is a strong correlation between MC1R variants and the development of NMSC. More precisely, V60L, D84E, V92M, R151C, R160W, R163Q and D294H variants of MC1R play a role in the promotion and the sustaining of NMSC development (Table 3) [42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55]. Because of the abnormal sonic hedgehog signaling in BCC, which is caused by the PTCH1 mutation, up-regulation of other molecules such as GLI1 and GLI2 is often observed. The predominance of either GLI1 or GLI2 in relation to the development of BCC is still unclear; however, there seems to be a positive feedback loop in which GLI2 directly activates the expression of GLII. Moreover, there is a small number of sporadic BCCs where SMO mutations are found, also resulting in the up-regulation of this pathway [42; 56; 57; 58; 59; 60; 61]. Not only the classical pathways are meant to be targeted in skin cancer therapy. Nowadays, due to the increasing resistance of tumor cells to conventional chemotherapy, single-target inhibitors are no longer the ideal mean of treatment. This is why multi-target inhibitors could be an attractive alternative, having shown efficacy. One example is the novel therapy implemented by [PERSON] et al. [43] that consists of the combination of doxorubicin and celecoxib that inhibits both the protein kinase B (AKT) and the cyclooxygenase-2 (COX-2) pathway. Finally, we analyzed the articles published in 2021-2020 on BRAF detection, and we found a tendency of new methods to detect BRAF mutation in circulating tumor DNA, which is a more feasible method than isolating it from FFPE samples of melanoma patients. The methods mentioned in Table 3 (e.g., droplet PCR, COBAS RT PCR) have the advantage of being clinically standardized and validated, making it possible to compare results obtained from different medical centers and countries, as was possible for HPV detection [44; 62]. With these new molecular approaches, we hope for optimal detection of melanoma patients with BRAF mutation who are eligible for targeted therapy.
## 3 Results
### Surface Roughness after Brushing  
#### 3.2.1 Effect of the Trochoidal Step on the Surface Roughness  
Figure 13 shows the effect of the trochoidal step on the roughness of the Mg alloys' surfaces upon brushing (Ra, Rz, and RSm, respectively). Considering Ra and Rz, the AZ91D alloy appears to have exhibited higher values of roughness parameters.  
Modifying the trochoidal step has a notable impact on changes in the roughness parameters of the AZ91D alloy. In the trochoidal step range of 5% to 20%, an increase in the roughness parameters occurs along with the increase in the trochoidal step. Comparing the surface roughness parameters from milling (the results in Section 3.1), it can be seen that brushing caused a significant reduction in the  
Figure 12: Effect of the cutting speed, v\({}_{\rm c}\): (**a**) Rku and (**b**) Rsk (\(s_{\rm tr}\) = 15%, \(f_{\rm z}\) = 0.15 mm/tooth, a\({}_{\rm p}\) = 6 mm) measured on the lateral face of the specimen.  
Figure 13: Effect of the trochoidal step, \(s_{\rm tr}\): (**a**) Ra, (**b**) Rz, and (**c**) RSm measured on the lateral face of the specimen after brushing (milling: v\({}_{\rm c}\) = 800 m/min, \(f_{\rm z}\) = 0.15 mm/tooth, a\({}_{\rm p}\) = 6 mm; brushing: n = 5000 rev/min, v\({}_{\rm f}\) = 300 mm/min, a\({}_{\rm p}\) = 0.5 mm).  
roughness. In the case of brushing, the values of the Ra parameter changed accordingly: For the AZ31 alloy in the range from Ra = 0.26 \(\upmu\)m to Ra = 0.35 \(\upmu\)m (after milling from Ra = 1.49 \(\upmu\)m to Ra = 1.91 \(\upmu\)m), and for the AZ91D alloy in the range from Ra = 0.22 \(\upmu\)m to Ra = 0.53 \(\upmu\)m (after milling from Ra = 1.39 \(\upmu\)m to Ra = 2.16 \(\upmu\)m). On average, over the entire range of the trochoidal steps investigated, the brushing treatment caused a 5.6-fold decrease in the value of the Ra parameter in relation to milling for the AZ31 alloy and about 3.9-fold drop for the AZ91D alloy. A similar trend was observed for Rz.
# Low-Cost RSAC and Adsorption Characteristics in the Removal of Copper Ions from Wastewater
## 2 Materials and Methods
### Kinetic Study of Copper Ion Adsorption by RSAC  
Currently, quasi-primary and quasi-secondary kinetic models are often used to describe the adsorption kinetic behavior of adsorbents in solid-liquid static adsorption systems [20].  
The expression for the quasi-level kinetic model equation is (Equation (6)):  
\[\mathrm{ln}\big{(}\mathrm{q_{e}}-\mathrm{q_{t}}\big{)}=\mathrm{ln}\mathrm{q_{ e}}-\mathrm{k_{1}t} \tag{6}\]  
In Equation (6), \(\mathrm{q_{e}}\) is the amount of solute adsorbed on the adsorbent surface at adsorption equilibrium (mg/g), \(\mathrm{q_{t}}\) is the amount of solute adsorbed on the adsorbent surface at the specified moment (t) during the adsorption process (mg/g), and \(\mathrm{k_{1}}\) is the adsorption rate constant (\(\mathrm{h^{-1}}\)).  
The expression for the quasi-secondary kinetic model equation is (Equation (7)):  
\[\frac{\mathrm{t}}{\mathrm{q_{t}}}=\frac{1}{\mathrm{k_{2}q_{e}}}+\frac{\mathrm{ t}}{\mathrm{q_{e}}} \tag{7}\]  
In Equation (7), \(\mathrm{q_{e}}\) is the amount of solute adsorbed on the adsorbent surface at adsorption equilibrium (mg/g), \(\mathrm{q_{t}}\) is the amount of solute adsorbed on the adsorbent surface at the specified moment (t) during the adsorption process (mg/g), and \(\mathrm{k_{2}}\) is the adsorption rate constant (g/(mg\(\cdot\)h)).  
A 150 mL sample of a 100 mg/L copper ion was prepared and 5 g RSAC particles were added. Then, the sample was mixed at room temperature (25 \(\pm\) 1 \({}^{\circ}\)C). The rest copper ion concentration was measured at different times to calculate the adsorbed copper ion. The experimental data of static adsorption of copper ions were fitted to the curve using quasi-primary and quasi-secondary kinetic models in turn.