esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Distributed Computation Offloading and Resource Allocation
### Computation Offloading is Modeled as a DRL Problem
#### 4.1.2 Action Space  
Each agent performs a sequential action in the given environment based on the condition. The A3C is suitable for both the continuous and discrete state space and action space [33; 37]. Furthermore, since each agent learns from its piece of the environment, we assume that the system will be faster.  
Theoretically, the agent is capable of performing numerous actions. However, for performing a significant number of actions, the system requires enormous computation, which produces a tremendous amount of delay with high usage of energy, decreasing the system's performance. Therefore, to avoid the computation complexity, it was considered that, based on the current state \(s_{t}\), the agent performs the action \(a_{t}\) including the mode selection and power allocation for each time slot \(t\in T\). The action space is represented as \(a_{t}=\{s_{k}^{m},p_{k}\}\). Based on the offloading decision, the controller allocates the power for the E-AP \(p_{k,l}\). Furthermore, in each decision epoch \(t\), the controller rigorously considers the computing resources \(\sum_{l\in L}f_{l,k}\) at the edge, which are allocated through the greedy algorithm. In each action, the controller needs to satisfy the constraints.
## 6 Experimental Results
### Comparison of Detection Performance
#### 6.5.2 Visualization of the Detection Results  
To assess the more intuitive overall performance of the CDAENet model, we give a visual analysis of the classification accuracy. Figure 13(a)-d show the results of the comparison of the D1-D4 data sets, respectively. The higher bars represent better overall detection performance of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Evaluation Index**} \\ \cline{2-5}  & **Accuracy (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline SVM [13] & 85.99 & 47.64 & 45.50 & 50.00 \\ DT [14] & 82.30 & 68.14 & 67.72 & 68.60 \\ CNN [15] & 87.13 & 58.17 & 72.25 & 55.99 \\ CSAE & 88.45 & 63.44 & 72.94 & 59.36 \\ \hline
**CDAENet** & **91.27** & 65.01 & **74.65** & 63.31 \\ \hline \hline \end{tabular}  
* The number in bold denotes it is the best result.  
\end{table}
Table 4: Performance comparison between CDAENet and other methods on the D4 dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Evaluation Index**} \\ \cline{2-5}  & **Accuracy (\%)** & **F1-Score (\%)** & **Precision (\%)** & **Recall (\%)** \\ \hline SVM [13] & 90.38 & 57.91 & 72.48 & 55.42 \\ DT [14] & 87.29 & 71.27 & 71.63 & 71.48 \\ CNN [15] & 93.06 & 63.46 & 85.36 & 61.70 \\ CSAE & 94.29 & 67.47 & 86.40 & 65.47 \\ \hline
**CDAENet** & **95.45** & **74.33** & **86.56** & **73.21** \\ \hline \hline \end{tabular}  
* The number in bold denotes it is the best result.  
\end{table}
Table 3: Performance comparison between CDAENet and other methods on the D3 dataset.  
As shown in Figure 14, CDAENet has higher classification accuracy than the other four basic methods (i.e., SVM, DT, CNN, and SCAE) on all four datasets. Meanwhile, the classification performance of CNN and SCAE is better than that ofSVM and DT. This not only proves the superiority of our proposed model but also shows that the DL method is superior to the traditional ML methods.
# SCMC: An Efficient Scheme for Minimizing Energy in WSNs Using a Set Cover Approach
## 3 Approach  
The proposed algorithm passes through three main phases: (1) information gathering; (2) set cover formation; and (3) path determination.  
During the information-gathering phase, all of the sensor nodes including the base station are assigned a unique ID. They then broadcast their IDs using the Carrier-Sense Multiple Access with Collision Avoidance (CSMA/CA) MAC layer protocol, which allows the base station to discover sensor node locations. Now, with the help of the received information about the network, the base station starts the creation of set covers, electing cluster heads and determining optimal routing paths using network layer information. It is to note that once the set cover creation and path determination phases are over, all cluster heads are aware of the next hop to the base station and sensor nodes are also informed about the cluster head ID that they belong to. Then, each cluster head provides a TDMA schedule to its member sensor nodes for intra-cluster communication.  
We modeled the energy conservation problem as an optimization problem where a \"single objective function\" is used for the minimum set covers, and for the min-cost max-flow model too. Our key contribution is that the proposed model guarantees a longer network lifetime.
## 4 Results
### Deep-Learning Methods
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author \& Year** & **Classification Task** & **Dataset** & **Data Augmentation** & **Methods Used** & **Cross Validation** & **Results** \\ \hline
**[PERSON] et al.** [85] 2022 & Binary: benign vs. malignant & ISIC archive & - & Parameter transfer of a pre-trained network to a CNN & - & ACC = 0.96 \\ \hline
**[PERSON] et al.** [86] 2022 & Binary: benign vs. malignant & ISIC archive, ISIC2020 & Mixup and CutMix techniques & Pre-trained CNNs: EfficientNetB, SERESNetC100 & 3-fold & ACC = 0.962, SP = 0.988, and DenseNet264 & AUC = 0.99 \\ \hline
**[PERSON] et al.** [87] 2022 & Multi-class: BCC, MM, N, SK & ISIC archive, PH\({}^{2}\) & Image rotation: 45, 90, 135, 180, 210; horizontal and vertical flipping & Ensemble of three pre-trained CNNs: EfficientNetB, SERESNetC100 & - & ACC = 0.879 on ISIC, ACC = 0.94 \\ \hline
**[PERSON] et al.** [88] 2022 & Multi-class: N/AN, MM, SK, BCC, DF, AK, VL & HAM10000 & Random clipping, flipping flipping flipping and translation & Use of TL on InceptionV3, ResNet50 & - & ACC train = 0.99, ACC val = 0.869 \\ \hline
**[PERSON] et al.** [89] 2022 & Binary: MM vs. non-MM & ISIC2017, ISIC2018, PH\({}^{2}\) & - & F\({}_{EX}\) and F\({}_{SF}\) (based on GOA) + custom CNN & - & ISIC2018, ISIC2018, and ACC = 0.98, SE = 0.97, SP = 0.99, SP = 0.99, PR = 0.98, PH = 0.97, PH\({}^{2}\); ACC = 0.98, PH = 0.96, SP = 0.99, PR = 0.97, F1 = 0.96 \\ \hline
**[PERSON] et al.** [90] 2022 & Binary: benign vs. malignant & ISIC archive & Resizing, vertical and horizontal flipping and rotation (45 degrees) & Pre-trained SqueezeNet re-trained by AWO algorithm & 5 and 9-fold & ACC = 0.925, SE = 0.921, SP = 0.917 \\ \hline
**[PERSON] et al.** [91] 2022 & Binary: benign vs. malignant & ISIC archive & - & Custom CNN & - & TPR = 0.94, TNR = 0.98, F1 = 0.96 \\ \hline
**[PERSON] et al.** [92] 2022 & Multi-class: N/AN, MM, SK, BCC, DF, AK, VL & HAM10000 & Details are missing & Custom CNN & - & ACC = 0.963, REC = 0.96, F1 = 0.957 \\ \hline
**[PERSON] et al.** [93] 2022 & Binary: benign vs. malignant & ISIC archive & Brightness and contrast alteration of images & Custom shallow CNN & 5 and 10-fold & ACC = 0.987, PR = 0.989 \\ \hline
**[PERSON] et al.** [94] 2022 & Multi-class: N/AN, MM, SK, BCC, DF, AK, VL & HAM10000 & - & Pre-trained DL method & - & ACC = 0.997 \\ \hline
**[PERSON] et al.** [95] 2022 & Binary: MM vs. benign & ISIC2020 & Image rotation: 90, 180, 270 degrees; center cropping, brightness change, and mirroring & New DL model based on VGG16 & Leave-one-out & ACC = 0.87, F1 = 0.922, AUC = 0.923 \\ \hline
**[PERSON] et al.** [96] 2022 & Multi-class: N/AN, MM, SK, BCC, DF, AK, VL & HAM10000 & - & Custom CNN based on AlexNet & - & ACC = 0.878, SP = 962, PR = 0.787, RE = 0.774, F1 = 0.778 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overview of cited works using DL approaches (results have been rounded). F\({}_{EX}\) and F\({}_{SE}\) abbreviations are used for feature extraction and selection, respectively. The symbol “-” indicates that no information is provided on a particular operation.
# Cleaning Phenolic Compounds Present in Water Using Salting-Out Effect with DCA-Based Ionic Liquids
## 1 Introduction
In some published works, DCA-based ionic liquids were used to recover napthentic acid from crude oil; the ability of the anion to attract the acid is crucial, as well as the number of the anion molecules available for the removal [14]. These ionic liquids are widely used for the separation of compounds derived from fuels, the desulfurization of fuel oils, and the separation of olefins and paraffin or alcohols from fermentation media [5; 15; 33; 34]. Based on the improvement of the properties of this kind of ILs (low cost, more environmentally friendly, lower viscosity values), in this work, the capability of two DCA-based ILs to remove phenolic compounds from the water was studied, and the influence of the concentration of salt (K\({}_{3}\)PO\({}_{4}\)) in the extraction efficiency was evaluated. This salt was selected based on its favorable properties, such as its low price as a non-pollutant substance, and due to the fact that it is a trivalent salt with a stronger salting out ability than divalent or monovalent salts [27]. For this investigation, phenol, o-cresol, and 2-chlorophenol samples prepared at different initial concentrations (from 0.003 to 15 g L\({}^{-1}\)) in water were used as extractable substances, and a phenolic compound mixture (PCM) was prepared, and the feasibility of the two extraction agents to remove this mixture from the water was analyzed. Three different concentrations of salt in water 20, 30, and 40% (\(w/v\)), with respect to the water volume, were prepared, and their influence on the extraction process was discussed.
# A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery
## 2 Materials and Methods
### Data Acquisition
#### 2.2.2 Satellite Data  
In the present investigation, we used a Sentinel-2A Level-2A cloud-free multispectral image (consisting of 13 spectral channels) acquired on 12/07/2017 (local time 09:00:21). The Level-2A data was downloaded directly from the open-source ESA/Copernicus Science Hub, and is ortho-corrected to the bottom-of-atmosphere (BOA) reflectance. The ESA atmospheric processing consists of combining state-of-the-art techniques for performing atmospheric corrections, including cirrus clouds [13], customized to the Sentinel-2 environment. The former correction is representative of the atmospheric and climate conditions in the study area (i.e., the strait of Paros and Antiparos Islands).  
The imagery was referenced onto the UTM cartographic projection and on the WGS84 geodetic reference system, with a spatial resolution of 10 m (for channels 2, 3, 4, and 8 at 490, 560, 665, and 842 nm, respectively); 20 m (for channels 5, 6, 7, 8a, 11, and 12 at 705, 740, 783, 865, 1610, and 2190 nm, respectively); and 60 m (for channels 1, 9, and 10 at 443, 945, and 1375 nm, respectively). The 10 m granule channels were used for assessing the Sentinel-2A imagery at its highest spatial analysis. The data analysis, processing, and evaluation were carried out with the use of the ENVI Software, SNAP Desktop/ESA, ArcGIS Pro, IBM SPSS and EXCEL toolboxes.
# Postharvest Evaluations of Blackcurrant Fruits with Chitosan and Ultraviolet A Treatments
## 2 Materials and Methods
### Monomeric Phenolics Quantification  
Monomeric phenolics was analyzed by ultra-high-performance liquid-chromatography coupled with tandem mass spectrometry (UPLC-MS/MS). The samples were frozen at \(-20\)\({}^{\circ}\)C and then lyophilized using a freeze dryer (SCANVAC CoolsafeTM, model 110-4, Lynge, Denmark) at a condenser temperature of \(-110\)\({}^{\circ}\)C with the pressure of 10 Pa. One hundred milligrams of each sample were weighed and added into a centrifuge tube. A quantity of 1 mL of methanol solution (methanol-water-formic acid = 70:30:1, \(V\)/\(V\)/\(V\)) was added into each tube. All the samples were vortexed for one minute, sonicated for 20 min, and then centrifuged at 2000\(\times\)\(g\) for 10 min. This process was repeated twice for each sample. The combined supernatant was then lyophilized and reconstituted in 200 \(\mu\)L methanol solution. Afterward, 2 \(\mu\)L aliquots of the resultant solutions were injected into a Waters HSS T3 column (2.1 \(\times\) 50 mm, 1.8 \(\mu\)m) to run LC-MS on a Vanquish ultra-high-performance liquid chromatography (UHPLC) system coupled to a Q Exactive mass spectrometer, which was operated in the positive ion detection. The limit of detection (LOD) was 1 mg/mL.  
The mobile phase for LC separation was 0.1% formic acid in water (A) and 0.1% formic acid acetonitrile (B) for gradient elution (0-1 min, 5% B; 1-6 min, 5-30% B; 6-7 min, 30-95% B; 7-8 min, 95% B; 8-8.1 min, 95-5% B; 8.1-10 min, 5% B), at 0.3 mL/min and 40 \({}^{\circ}\)C. Concentrations of the analytes were calculated by the constructed linear regression calibration curves (Supplementary Table S1).  
High-resolution mass spectrometry (HRMS) data were recorded on a Q Exactive hybrid Q-Orbitrap mass spectrometer equipped with a heated ESI source (Thermo Fisher Scientific) utilizing the SIM MS acquisition methods. The ESI source parameters were set as follows: spray voltage, 3.0 kV; sheath gas pressure, 40 arb; aux gas pressure, 10 arb; sweep gas pressure, 0 arb; capillary temperature, 320 \({}^{\circ}\)C; and aux gas heater temperature, 350 \({}^{\circ}\)C.
## 4 Empirical Evaluation
### Dataset  
We have run our experiments on a dataset consisting of reviews that self-identified as systematic reviews. To obtain these references, we searched the following well established databases that are popular in the software engineering field: Google Scholar, IEEE Software and ACM Digital Library. We used the following query and specifying a year range varying from 2012 and 2022:  
1. **IEEE** (using the Command Search, and searching in metadata): _(((\"All Metadata\": \"systematic review\") OR (\"All Metadata\": \"PRISMA review\")) AND (\"All Metadata\": \"software engineering\"))_
2. **Google scholar** (using the _Publish or Perish_ tool, and searching in the titles): _(\"systematic review\" OR \"PRISMA review\") AND (\"software engineering\")_
3. **ACM** (using the Advanced Search option and searching in the titles): _(\"systematic review\" OR \"PRISMA review\") AND (\"software engineering\")_.  
Searching the three databases using that query identified 267 reviews. We imported these references into Endnote (a reference manager) by creating an EndNote library of them. We then eliminated the duplicates in the so-obtained EndNote reference list. After that, we analyzed the content of the corresponding reviews and decided to include in our dataset the reviews that met the following inclusion criteria:  
\begin{table}
\begin{tabular}{c c} \hline _OConfidence_ **Range (Interval)** & _OConfidence_ **Interpretation** \\ \hline \([0,1]\) & Invalid \\ \hline \([1,5]\) & Extremely Low \\ \hline \([5,20[\) & Very Low \\ \hline \([20,40[\) & Low \\ \hline \([40,60[\) & Medium \\ \hline \([60,80[\) & Medium High \\ \hline \([80,95[\) & High \\ \hline \([95,99[\) & Very High \\ \hline \([99,99.9[\) & Extremely High \\ \hline \([99.9,100]\) & Valid \\ \hline \end{tabular}
\end{table}
Table 2: Interpretation of the Overall Confidence, from [PERSON] et al. [41].  
* It was written in English
* It was a conference paper, a journal paper, or a workshop paper
* It was peer-reviewed
* It was published between 2012 and 2022
* It self-identified as a systematic review
* It discussed software engineering concepts. If the reference deals with interdisciplinary concepts i.e., focuses on several fields, then software engineering should be part of these fields. We did not include in our dataset the reviews that met the following exclusion criteria:
* It was the reference of a magazine, a chapter of the book, a book, conference Protocol, or a tutorial
* It was the reference of a paper that does not present results
* It was the reference of a paper that cannot be downloaded and/or is not available
* It was not related to software engineering
* It was the reference of a very short paper (i.e., a paper that has less than four pages).  
Applying these inclusion and exclusion criteria allowed us to include 151 reviews in our dataset. Eight of these reviews have complied with PRISMA; 143 of them complied with other reporting procedures/methods such as the ones discussed in [32; 34; 42; 43].
# A Review of Vertical Shaft Technology and Application in Soft Soil for Urban Underground Space
## 4 Technological Advancements in Mechanized Systems
### Blind Shaft Boring
#### 4.2.3 Shaft Boring Cutterhead (SBC)  
The SBC is the third machine in Herrenknecht's range of blind boring equipment. This machine, measuring 40 m in length and weighing 350 tons, is ideal for excavating deep blind shafts ranging from 7 to 12 m in diameter in hard rock conditions up to 2000 m deep, achieving a progress rate of 6 m per day. The SBC employs a conical full-faced cutterhead assembled with disc cutters, featuring a highly automated cutting sequence. Shaft ropes are utilized for the suspension and movement of this mechanical shaft sinking unit [53]. A summary of the shafts drilled using raise boring technology and blind boring shafts is provided in Table 2.  
From the revised case histories, there is significant advancement in mechanized technologies utilized in hard grounds, particularly in mining. However, well-documented case histories for their application in soft UUS are lacking. Despite advancements in mechanized shaft sinking technologies, which exhibit varying degrees of automation, challenges persist when excavating in soft UUS. The progress achieved remains unsatisfactory compared to performance in hard grounds. The primary obstacle to achieving proper progress stems from various activities related to shaft lining construction technology, soft grounds, and confined urban spaces. Therefore, there is a pressing need to explore mechanized shaft sinking systems that meet customer expectations and to identify solutions for shaft excavation in soft UUS. To address this challenge, the following paragraphs present the PIAUS construction technologies, a novel approach designed to optimize shaft sinking in soft UUS environments.
# A Coal Mine Excavation Tunnels Modeling Method Based on Point Clouds
## 4 Experiments
### Experiment Results  
#### 4.2.1 Tunnel Extraction Results  
Based on the threshold of the tunnel contour, the tunnel is extracted, as shown in Figure 14. The algorithm in this paper effectively filters out the point clouds of equipment and pedestrians inside the tunnel while preserving the shape of the tunnel, demonstrating good performance. The interval \(d\) for slicing the tunnel along the Y-axis was set to 0.05 m, while the interval \(l\) for calculating the point cloud density distribution map of the tunnel slices was set to 0.01 m.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Tunnel** & **Length (m)** & **Width (m)** & **Height (m)** & **Point Number** \\ \hline a & 198.76 & 5.13 & 2.74 & 15957544 \\ b & 71.72 & 5.41 & 3.24 & 22242423 \\ c & 288.59 & 5.72 & 3.95 & 41824105 \\ d & 106.84 & 4.72 & 3.55 & 17766469 \\ \hline \end{tabular}
\end{table}
Table 2: Information about experiment tunnels.  
Figure 13: Original point clouds from four different mines. (**a**) Tunnel a. (**b**) Tunnel b. (**c**) Tunnel c. (**d**) Tunnel d.
###### Abstract  
A 56d incubation experiment was conducted to explore the effects of the silk-worm excrement biochar (500 \({}^{\circ}\)C, BC) combined with different iron-based materials (FeCl\({}_{3}\), FeSO\({}_{4}\) and reduced iron powder) on the speciation of cadmium (Cd) and lead (Pb) in a contaminated soil. Application rate of BC and iron-based materials is 1% (\(W\)/\(W\)) and 0.2% (\(W\)/\(W\)) of the soil, respectively. At the same time, the soil physicochemical properties, such as pH, cation exchange capacity (CEC), and the structure of soil, were determined in order to explore the influence mechanism of amendments to forms of Cd and Pb in soil. The results show that the stabilization effects on Cd is (BC + FeSO\({}_{4}\)) \(>\) (BC + FeCl\({}_{3}\)) \(>\) (BC + Fe) \(>\) (BC) and Pb is (BC + Fe) \(>\) (BC + FeSO\({}_{4}\)) \(>\) (BC + FeCl\({}_{3}\)) \(>\) (BC) at the end of incubation, compared with the effect of the control group. The treatment of (BC + FeSO\({}_{4}\)) is the most effective in terms of the stabilization of Cd and Pb, which makes the percentages of organic-bound and residual Cd and Pb increase by 40.90% and 23.51% respectively. In addition, with different ways of treatment, the pH value and CEC of soil see a remarkable increase by 1.65-2.01 units and 2.01-2.58 cmol-kg\({}^{-1}\) respectively. X-ray diffraction (XRD) patterns show that the soil imprisons Cd and Pb in different mineral phases. As such the treatment of (BC + FeSO\({}_{4}\)) can significantly improve soil environment, increase soil pH value & CEC and exert a relatively good stabilization effect on both Cd and Pb.  
Keywords:silkworm excrement; biochar; ferrous materials; soil contamination; stabilization +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Preliminary Consideration  
From a general and qualitative analysis of the results, germination occurred for almost all the basil seeds and any morphological or developmental abnormalities were observed, thereafter, all the tested light conditions resulted suitable for basil growth, even if with different performances. Figure 4 shows a visual comparison of the plants obtained at the final harvest of six different samples (Table 6), from which qualitative observation can be drawn and discussed. For each sample, all three repetitions were shown to stress the overall reliable repeatability of each experiment, considering that biological systems were investigated. Sample 6, 12 and 19 (Figure 4a-c) have in common the same LEDs combination (LR33) but are different from each other for the light-plant distance that is equal to 80, 70 and 60 respectively. In strong similarity, sample 2, 8 and 15 (Figure 4d-f) are representative of the same light combination (LR14) but with different light intensity, thereafter, moving from greater to smaller LEDs-plants distance. For both the sets, thereafter, independently of the LED combination, an increasing in plants height, number of leaf and leaf area can be observed by reducing placing the plants nearer to the artificial lights. In Figure 4c,\(f\) plants' height is clearly over the reference of 12 cm (also considering the pot), whereas in Figure 4b,e plants' height is approximately equal to 12 cm and in Figure 4a,d the overall plants' height is below the value of reference. Nevertheless, regarding the evaluation of the different LEDs combination from a qualitative point of view, is not possible to clearly detect any difference, even if the set of samples employing LR14 combination (Figure 4d-f) seems to better enhance plant growth, with respect to the other set of LEDs. Only observing sample 15 (Figure 4f) it is possible to suppose that this light condition (LR14 at a distance of 60 cm) seems to be the best to promote the basil height and leaf area, among those considered in Figure 4 but further confirmation must be drawn from the statistical analysis. Considering that other variables must be evaluated, such as more different light combination and more growth performance parameters, a statistical  
In addition, a control group of plant grown without any type of light has been tested, obtaining a not significant number of plants to derive any specific comparison among data. For the control group, only the plants' height was detectable and was measured equal to 2.03 \(\pm\) 0.62 mm. This result suggests that LEDs light combinations investigated in this study play a valuable role as a different source of enlightenment from daylight to improve the basil growth performance.