esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
PBCCux (PrBaCo\({}_{2-x}\)Cu\({}_{x}\)O\({}_{5+\delta}\), \(x\) = 0, 0.2, 0.5) powders were synthesized through an EDTA-citrate complexing process. Pr(NO\({}_{3}\))\({}_{3}\cdot\)6H\({}_{2}\)O (99.9%, Sigma-Aldrich, St. Louis, MO, USA), Co(NO\({}_{3}\))\({}_{3}\cdot\)6H\({}_{2}\)O, Cu(NO\({}_{3}\))\({}_{2}\cdot\)2.5H\({}_{2}\)O (98%, Sigma-Aldrich, St. Louis, MO, USA) and Ba(NO\({}_{3}\))\({}_{2}\) (99%+, Alfaesar, Haverhill, FL, USA) metal precursors were dissolved in deionized water. EDTA (99.5%, Alfaesar, Haverhill, FL, USA) was added to a 1 N NH\({}_{4}\)OH (Junsei Chemical Co., Tokyo, Japan) solution to obtain an NH\({}_{3}\)-EDTA buffer solution. The NH\({}_{3}\)-EDTA solution and crystallized citric acid (99.5%, Samchun Chemical, Pyeongtaek, Korea) powders were applied to the metal precursor solution to make a sol for a total metal ion:EDTA:citric acid molar ratio of 1:1:2. The solution was heated to 75 \({}^{\circ}\)C while adjusting the pH to 9 with NH\({}_{4}\)OH, and the solvent was evaporated to obtain clear gels. These gels were precaliced at 450 \({}^{\circ}\)C and calcined in air at 850 \({}^{\circ}\)C. Thermal stress was applied to PBCCu powder at 1050 \({}^{\circ}\)C for 100 h in electronic furnace in air atmosphere.  
Symmetric cells (PBCCu | SDC(Sm doped Ceria, Sm\({}_{0.2}\)Ce\({}_{0.8}\)O\({}_{2}\)) | PBCCu) were prepared to investigate the electrochemical properties of the PBCCux. The SDC pellets were sintered at 1400 \({}^{\circ}\)C for 4 h using SDC powder (SDC20-HP, Fuelcellmaterials, Lewis Center, OH, USA). The PBCCu powders were mixed with a binder prepared from \(\alpha\)-terpineol and ethyl-cellulose to form PBCCu pastes, which were screen-printed onto both sides of the SDC pellets. After drying, the symmetric cells were calcined at 950 \({}^{\circ}\)C for 2 h in air.  
To examine the crystal structures of the calcined powders and sintered powders, powder X-ray diffraction (XRD, PANalytical X'pert-Pro MPD PW3040/60) was performed at room temperature using a step scan procedure (0.02\({}^{\circ}\)/20 step, time per step 0.5 s, Cu K\(\alpha\) radiation, \(\lambda\) = 1.54A) in the 2\(\theta\) range of 10-90\({}^{\circ}\). The PBCCu surface was characterized by XPS using Al K\(\alpha\) radiation (hv = 1486.6 eV) under ultrahigh vacuum conditions.  
The electrical conductivity was measured using a DC 4-terminal technique between 300 and 950 \({}^{\circ}\)C in air. Four terminals were formed to wind the Pt electrode wires, and Pt paste was applied to the wires and terminals to ensure a tight connection between the specimen and electrode. The samples were calcined at 1000 \({}^{\circ}\)C for 1 h. Direct current was suppliedto them by a current source (Keithley 2400, Cleveland, OH, USA), and the corresponding voltage drops were collected using a multimeter (Agilent, 34401A, Santa Clara, CA, USA). Impedance measurements were conducted using an IviumStat (Ivium, Noord-Brabant, Eindhoven, The Netherlands) instrument over the frequency range from 106 to 0.01 Hz with 10 mV excitation voltage at operating temperatures of 600 \({}^{\circ}\)C under open circuit conditions in air.
# Experimental Study of LoRa Transmission over Seawater
## 3 Materials, Experimental Setup and Methods
### RSSI Correction Factors and SNR Measurement Chain Linearity
As can be noticed in the Figure 8, SNR measurement chain enters saturation when the RSS approaches \(-100\) dBm for all three transceivers. Figure 8: Signal-to-Noise ratio vs. Received signal strength for ODT (434 MHz), ODT ((868 MHz), Libelium (868 MHz) and Uptronics (868 MHz). Figure 7: Received signal strength vs. channel attenuation for reference RSS and (**a**) ODT(868 MHz), Libelium (868 MHz) and Uptronics (868 MHz), (**b**) ODT(434 MHz).
## 3 Preliminaries and Motivations
### Problems Definition  
From RFC 6550, the non-storing mode and the storing mode are mutually excluded on a single node. Furthermore, the way to make the two modes hybrid in a single DODAG is still an open issue. The separation and mutual exclusion of the non-storing mode and the storing mode in RPL produce difficulties in implementing P2P communication among nodes. In the non-storing mode, the upward routing is first asked to find a path from source node to LBR, and then a downward routing is performed to set a path from LBR to the destination node. In the storing mode, an upward routing just needs to find a common parent (node C in Figure 3 for example) of the two P2P nodes (nodes D and E in Figure 3 for example); then, a downward routing is executed to the destination node. Next, we focus on the detailed definitions of the three problems.
## 3 Challenges of Blockchain-Based Energy Trading  
Currently, the research on blockchain-based energy trading has made great progress and solved many limitations of the traditional energy markets mentioned above. Meanwhile, there still exist many challenges:* Low efficiency [62] and high transaction costs [63; 64]. Due to the shortcomings of blockchain itself, the transaction speed cannot meet the system requirements. At the same time, the transaction costs of blockchain can raise the overall cost of the system.
* Privacy protection and security issues [62; 64; 65]. How to avoid big data statistical prediction and behavior model analysis when the transaction is completed, as well as how can the rights and interests of both parties in the transaction be guaranteed are both severe challenges. There are also potential risks such as private key leak [15].
* Real-time communication [63]. The challenge of the real-time communication of a large amount of sensor data.
* Price model. How should a deal properly be priced to help buyers and sellers reach an agreement quickly.
* Lack of a complete incentive mechanism[15; 62]. It is similar to the incentive mechanism for miners in Bitcoin. Under the premise that the system may face privacy and security problems, how to motivate more nodes to join the system is a problem that needs to be solved.
* Cost minimization, multi-scene welfare maximization. In the case of multiple variables, how to ensure the minimization of the overall energy cost of the system, as well as the assurance of trust, rationality of independent individuals, and computational efficiency must be considered [60].
* Lack of a regulatory framework with flexibility [38; 43; 63; 66].
* There is also the issue of environmental energy consumption, because the system requires a large carbon footprint [63].
* Because of the tight network structure, there may be a cascade of physical faults [15]. In addition, there are many physical hardware limitations to consider [67].
# Inference of Drawing Elements and Space Usage on Architectural Drawings Using Semantic Segmentation
## 4 Experiment  
This study used DeepLabV3+ to conduct an experiment on the automatic recognition and inference of the drawing elements and space usage on architectural drawings. Given the varying purposes or sizes of buildings and the resultant differences in the composition of planes, this study was limited to apartment houses. The elements on architectural drawings were classified into five types, viz. walls, windows, hinged doors, sliding doors, and evacuation doors, and the space usage into 8 types, viz. rooms, entrances, balconies, dress rooms, bathrooms, living rooms, evacuation space, and pantries (with backgrounds excluded). The experiment process is shown in Figure 2.
# A Multi-Modal Profiling Fraud-Detection System for Capturing Suspicious Airline Ticket Activities
## 4 Challenges
### Imbalanced Dataset  
Non-fraud records are far more frequent than fraud ones in almost all fraud-detection domains. Therefore, this problem is encountered in almost all fraud-detection studies in the literature [20; 34; 35]. Aviation is one of these domains that has a high level of dataset imbalance in their fraud-detection processes. Due to this problem, fraud-detection systems tend to make negative decisions (prone to non-fraud decisions) to acquire higher accuracy values. However, this fact will result in lower recall and F1 scores, which means that the fraud-detection system cannot detect fraud transactions properly. Sampling techniques are the most common ways to solve this problem:  
* In the undersampling method, all fraud records are preserved and some of the legitimate records are selected according to a target fraud/legitimate balance. This target balance can be determined depending on the domain, interconnected transaction density, and dataset structure. Also, legitimate records can be selected randomly, homogeneously, or with a custom algorithm that decides considering the links between transactions in a smart way [36; 37].
* In the oversampling method, all legitimate records are preserved and some synthetic fraud records are produced in order to achieve the target fraud/legitimate balance. These synthetic fraud records can be duplicates of current fraud records or completely new records based on the values of fraud records [38; 39].
* Hybrid sampling methods are commonly used when undersampling methods are ineffective but oversampling methods are not possible to apply because the dataset is too big to reproduce new records [22; 40; 41]. In this approach, the undersampling method is used to some extent; thereafter, oversampling is applied to selected legitimate records. In other words, there is a two-phased sampling process in hybrid sampling methods.
## 3 Results
### Generation of CRISPR/Cas9-Mediated PARP7 Knockout MCF-7 Cells  
To further study the interplay between PARP7 and ER\(\alpha\), we generated CRISPR/Cas9-mediated PARP7 knockout (PARP7\({}^{\text{KO}}\)) MCF-7 cells. Sequencing of a portion of the PARP7 gene surrounding the gRNA binding site after puromycin selection identified insertions/deletions resulting in frameshift mutations in PARP7 (Figure 3A). To confirm PARP7 knockout, MCF-7 wildtype and PARP7\({}^{\text{KO}}\) cells were treated with E2 or/and RBN-2397 in order to induce expression of, and stabilize PARP7. When probed with our lab-generated anti-PARP7, there were no visible bands in the PARP7\({}^{\text{KO}}\) samples (Figure 3B). However, when probing the membrane with anti-PARP7 (ab84664), we observed a band at 100 kDa in all lanes. In line with previous observations, E2-dependent decreases in ER\(\alpha\) protein levels were reduced in the PARP7\({}^{\text{KO}}\) cells. To provide further verification of PARP7 knockout, MCF-7 wildtype and PARP7\({}^{\text{KO}}\) cells were treated with TCDD for 24 h, a potent AHR ligand, and the relative mRNA levels of the AHR target gene CYP1A1 were determined. CYP1A1 mRNA was significantly higher in the knockout cells, indicating that the repressive role of PARP7 on AHR activity was abolished (Figure 3C).  
Figure 3: Confirmation of MCF-7 PARP7 knockout cells. (**A**) Schematic representation of the gRNA binding site, showing insertions/deletions resulting in frameshift mutations. The deleted bases are represented as dashes. The data are from 45 independent sequences. (**B**) PARP7 is not detected in the PARP7\({}^{\text{KO}}\) cells when blotting the membrane with our lab generated anti-PARP7 antibody. MCF-7 wildtype and PARP7\({}^{\text{KO}}\) cells were treated with DMSO, E2 or/and RBN-2397 for 24 h. Membranes were blotted with lab generated anti-PARP7, anti ER\(\alpha\) and anti-PARP7 (Abcam; ab84664). Blotting with anti-PARP (ab84664) resulted in bands across all lanes at approximately 100 kDa. (**C**) Loss of PARP7 increases expression of AHR target gene CYP1A1. MCF-7 wildtype and PARP7\({}^{\text{KO}}\) cells were treated with 10 nM TCDD for 24 h. Relative mRNA levels of CYP1A1 was determined with RT-qPCR. Significant differences (\(p<0.05\)) compared to DMSO are denoted with \({}^{\star}\), and differences due to PARP7 are denoted with the hash mark #. Data are shown as means \(\pm\) S.E.M. for three independent experiments.
# Natural Photosensitizers in Clinical Trials
## 3 Natural Photosensitizers in Clinical Trials
### Riboflavin  
Riboflavin (RF) is an organic chemical compound that is a combination of ribitol and flavin. Its rich source is leafy vegetables [57]. The RF spectrum has two peaks in the UVA (360 nm) and blue (440 nm) wavelength regions [58]. Riboflavin, after activation and generation of ROS, mainly leads to apoptosis, which is the result of, among other things, damage to mitochondria by ROS. It can also activate MAPK (mitogen-activated protein kinase) pathways, such as p38 and JNK, leading to further activation of apoptotic mechanisms [59]. Previous studies that have been carried out in vitro have demonstrated the usefulness of riboflavin as a photosensitizer in cervical cancer therapy, antibacterial therapy, and dental therapy [60; 61; 62].  
Riboflavin in Clinical Trials of Photodynamic Therapy  
We located three clinical trials with this compound. All of them focused on the use of RF-PDT in dentistry. [PERSON] et al. evaluated the effect of RF-PDT as a surface pretreatment method and also different types of luting cement on the extrusion bond strength of polyetheretherketone (PEEK) inlays bonded to root canal dentin. The therapeutic regimen involved conditioning the surfaces of the posts with RF at a concentration of 25 mol/L, followed by irradiation with a green laser at 540 nm for 60 s. After the irradiation process, the samples were washed with distilled water to remove residual dye and then dried thoroughly to ensure complete drying. This work showed that RF-PDT can be an effective means of conditioning the surface of PEEK inlays [63]. [PERSON] et al. demonstrated that RF-PDT provides an alternative for conditioning the surface of PEEK posts. In their work, the therapeutic regimen consisted of pretreating PEEK posts with RF and exposing them to light with a wavelength of 632 nm, a power of 150 mW, and a power density of 23.43 J/cm\({}^{2}\). The light was exposed continuously for 1 min, after which the post was rinsed with deionized water for 1 min [64]. [PERSON] et al. investigated the push strength of a fibrous post into the root dentin using RF-PDT as a final root canal irrigator. The therapeutic regimen involved brushing the canals with 150 g/mL of riboflavin solution activated using a 660 nm LED light at 150 mW for about 60 s. The LED light tip was placedin the canal parallel to its long axis. However, RF-PDT showed significantly lower bond integrity values than all other groups studied in this work [65].
## 3 Methodology
### Data Preprocessing  
Many machine learning algorithms compare attributes of data points to detect trends in the data. However, problems would arise when features were on different scales. Therefore, data are normalized before being sent for training in machine learning models. One of the most prevalent methods of data normalization is the min-max method in which values are transformed to possess values between 0 and 1. Therefore, if \(x\) and \(x^{\prime}\) are the actual and normalized values of the feature and max and min are maximum and minimum values of the feature, then the normalization can be represented by Equation (25)  
\[x^{\prime}=\frac{x-min}{\max-min} \tag{25}\]  
Figure 3: Error correction algorithm for simulated wind speed using Weibull and Rayleigh distribution function.  
After the normalization process, the dataset is divided into training and testing datasets. Usually, 70% of the data are used for the training of the model and the remaining 30% are used for testing the model. Out of the testing dataset, 10% of the total dataset is used for the validation of the model performance. However, we have train and test datasets for short forecast horizons of 6 h. For this, we have taken the last six time-step data as a test set and the remaining data as a training dataset.
## 11 Further Research Directions and Future Considerations  
There have been considerable research efforts on the simplified optical transceivers as expatiated in the previous sections. Nevertheless, some research directions on simplified optical transceivers are still underexplored. This section focuses on research opportunities and associated technical challenges. In this context, as illustrated in Figure 24, advancement towards high-baud-rate transmission, PIC exploitation, and software-defined optical transmission will be discussed.  
Figure 24: Future research directions on simplified optical transceivers. O/E: optical-to-electrical, E/O: electrical-to-optical.
## 4 Field Test
### Threshold Test  
The test evaluated the influence of distance on RSSI and packet loss ratio by different DRs, and 100 packets were sent by the cluster head at a time, the size of the packet is 100 bytes. The cluster head node was placed at the target position of the UAV and then the UAV moved to the cluster head along its initial heading over a straight distance of 1400 m in steps of 50 m measured by GPS. At each step, the receiver of the UAV acquired the RSSI sample mean and packet loss ratio by 7 different DRs, respectively; the routine was repeated 20 times.  
Figure 7 presents the average results of RSSI attained from the test. It is obvious that the RSSI results by all DR modes quickly decreased with increase in the distance, when the distance was less than 800 m. When the distance exceeded 800 m, with increase in the distance, the RSSI value decreased correspondingly, and the downward trend became gradually slower. It is worth noting that with the increase in DR, when the distance exceeded a certain value, the UAV can hardly receive data packets.  
Figure 6: Real-world test. (**a**). Experimental site. (**b**). Sensor node and UAV air platform.  
Figure 8 shows the average packet loss ratio with different distances. It can be seen that the packet loss ratio of all DRs increases as the distance increases. When the distance was 300 m, the packet loss rate of DR6 was 6.9%, and then the packet loss ratio increased rapidly as the distance increased. When the distance was 500 m, the packet loss ratio of DR6 reached 100%. DR5, DR4, and DR3 also have a similar trend. When the distance exceeded 400, 600, and 800 m, the packet loss ratio of DR5, DR4, and DR3 increased rapidly as the distance increased. When the distance was 1000 m, the packet loss ratio of DR2 was 8.8%, and the packet loss ratio of DR1 was 5.0%. Subsequently, the packet loss ratio of DR2 increased rapidly until the distance was 1100 m, which had reached 43.3%. When the distance was 1100 m, the packet loss ratio of DR1 was 5.0%, and then it increased rapidly as the distance increased, and the changing trend was slower than that of DR2. Moreover, the packet loss ratio of DR0 was always less than 5% when the distance was within 1300 m.  
Figures 7 and 8 both show that different DRs are only meaningful within the certain distance range. We selected the distance when the packet loss rate is 5% as the DR distance threshold \(Dist\) in (13). Through the above tests, the threshold values of SNR, RSSI, and \(Dist\) of each DR in this environment can be obtained, as shown in Table 3.  
Figure 8: Evaluation of the packet loss ratio.  
Figure 7: Evaluation of the RSSI.
## 4 Methodology and Algorithm Explanation
### Data Preprocessing Phase
#### 4.2.2 Dataset Scaling  
The raw data found in datasets coming from the real world frequently have problems that make it impossible to use them for machine and deep learning tasks. The multiple scales used to portray the different dataset features are one of the most noticeable problems. As a result of this problem, the algorithms in deep learning and predictive machine learning learn less accurate models [30]. This is because algorithms tend to rely on features that fluctuate within a larger range of the dominant features, even if they may not necessarily be the most enlightening or significant elements for the appropriate categorization of data examples. To solve this problem, scaling techniques are used for the relevant data such that each feature fluctuates within the same range. Scaling is, therefore, one of several preprocessing steps that are usually necessary prior to applying a deep learning model to a dataset [31]. The Z-score normalization is implemented using the _Standard Scaler_ approach, which standardizes features by taking the \"mean\" of each value and dividing the result by the standard deviation of the attribute. This yields a distribution with unit variance and zero mean. Let \(\overline{x}\) be the mean of the \"\(x\)\" variable; Equation (1) transforms (scales) a value \(x_{i}\) into \(\overline{x}_{i}\).  
\[\overline{x}_{i}=\frac{x_{i}-\overline{x}}{S} \tag{1}\]  
The sample mean of the property in this case serves as the translational term, while the standard deviation serves as the scaling factor. This approach has the potential to transform attributes with positive and negative values into a distribution that is substantially similar. When compared to a characteristic without any outliers, the final distribution of inliers narrows excessively in the presence of outliers [32].
## 4 Results and discussion
### Effect of van der Waals force on pull-in parameters  
In Figures 4 and 5, we plot the pull-in parameters versus \(\mu\) in the range \([0,\mu_{\rm cr}]\) for \(\alpha=1\) and \(\beta=0\) for a square and a rectangular plate. As \(\mu\) increases the pull-in parameter \(\lambda_{\rm PI}\) decreases monotonically from its maximum value \(\lambda_{\rm PI}^{\rm max}\) corresponding to \(\mu=0\) to its minimum value \(0\) corresponding to \(\mu=\mu_{\rm cr}\)The value \(\mu=\mu_{\rm cr}\) represents the intersection of the curves with the horizontal axis. With an increase in \(\mu\), the non-dimensional maximum transverse displacement decreases monotonically from its maximum value \(\|w_{\rm PI}\|_{\infty}^{\max}\) attained for \(\mu=0\) to its minimum value corresponding to \(\mu=\mu_{\rm cr}\). For a MEMS made of a specified material, equation (11)\({}_{5}\) implies that \(\ell^{4}/(h^{3}g_{0}^{4})\) is proportional to \(\mu_{\rm cr}\). Thus, \(\mu_{\rm cr}\) provides a quantitative indication of the devices' size that can be safely fabricated. This means that reduced deflection ranges are allowable for devices having a large value of \(\mu_{\rm cr}\) since \(\mu_{\rm cr}\) is inversely proportional to \(g_{0}^{4}\). By comparing results depicted in Figures 4 and 5, we conclude that a change in the aspect ratio of a plate from \(1\) to \(1/2\) significantly increases \(\lambda_{\rm PI}^{\max}\) and it does not noticeably affect the difference \(\|w_{\rm PI}\|_{\infty}^{\max}-\|w_{\rm cr}\|_{\infty}\).  
For \(\mu=0.3\mu_{\rm cr}\) and \(\alpha=4\), we depict in Figure 6 deformed shapes of a square plate when \(\lambda\simeq 200\) and \(\|w\|_{\infty}\simeq 0.59\), and of a rectangular plate with \(\varphi=1/2\) when \(\lambda\simeq 1500\) and \(\|w\|_{\infty}\simeq 0.59\). In both cases thermal effects are discarded by selecting \(\beta=0\). Fringe plots of the van der Waals pressure (cf. equation (8)) are also exhibited in the same plots. For the same value of \(\|w\|_{\infty}\) the maximum magnitude of the van der Waals pressure for the rectangular plate is nearly an order of magnitude higher as compared to that for the square plate; note that \(F_{\rm vdW}\) in equation (8) does not depend upon the applied voltage. Its value is negative because it acts along the negative \(z\)-axis.  
Figure 3: Variation with \(\alpha\) of the function \(f(\alpha)\) in equation (40).