esa-sceva/satcom-chunk-collection huggingface dataset:
# Fine-Tuning BERT-Based Pre-Trained Models for Arabic Dependency Parsing
## 5 Methodology  
This section describes our methodology, including the evaluation metrics and a detailed description of the experimental results. In this study, we aimed to demonstrate the best possible options for fine-tuning BERT models for Arabic dependency parsing by conducting the following experiments.  
* The most effective head position encoding method for each treebank is identified in Section 6.1.
* The accurate pre-trained model for the syntactic parsing task on each corpus was differentiated by tuning all Arabic pre-trained models under study. In the first experiment, head position encoding methods with higher scores were utilized. In this process, out-of-vocabulary (OOV) words and the average number of tokens per word (P/W) created by tokenizers were verified (see Section 6.2).
* A comparison of fine-tuning techniques is presented in Section 6.3. Based on the previous sections of the experiments, we utilized the best encoding method and pre-trained model for each treebank.  
For evaluation metrics, two standard evaluation metrics for dependency parsing were used: unlabeled attachment score (UAS), which represents the percentage of tokens assigned to the correct head, and labeled attachment score (LAS), which is the percentage of tokens assigned to both the correct head and the correct dependency label between the token and its head [37]. In addition, in some sections, the label score (LS) was used as the number of correct labels for dependency.  
In all experiments, we relied on the training code for [28], but we allowed multiple roots while it was seen in the corpora. We used GPU in Google Colab and conducted the experiments in April 2022 (before the Python packages upgrading in November 2022, in case of future reconsideration). The same experimental settings were applied in all runs: maximum length = 512, batch size = 8, epoch = 10, and learning rate = 5 \(\times\) 10\({}^{-5}\).
## 1 Introduction  
Since 1969, the LC-based phase-locked loops (PLLs) have been developed for over fifty years [1]. Several designs of LC-based PLLs are reported recently to save power consumption [2, 3, 4, 5, 6, 7, 8] as urgent demands of low power requirements for integrated circuits (ICs) appear. Decreasing supply voltage is an effective way to achieve low power. However, low voltage always limits the performance of circuits. Therefore, improving the noise feature with lower supply voltage is very attractive.  
In a classical charge pump PLL (CPPLL) system [9, 10, 11, 12, 13, 14, 15], phase noise is mainly generated from two parts: out-of-band noise which is dominated by the voltage-controlled oscillator (VCO) (noise of low pass filter is neglected); and in-band noise which is dominated by the phase detector, charge pump and divider. Several efforts have been addressed to study the phase noise of VCOs [16, 17, 18] and designs with ultra-low power consumption have been published [19, 20, 21]. Hence, this paper concentrates on the improvement of in-band noise at low supply voltage.  
From the literature review, compared with widely used CPPLLs, sub-sampling PLL (SSPLL) and type-I PLL can suppress in-band noise effectively. In an SSPLL system, the multiplication factor N of the noise of charge pump is rejected and the noise of divider is removed [22, 23, 24]. However, the sub-sampling charge pump (SSCP) is not suitable for low voltage application, since it suffers from deteriorated current noise. In a type-I PLL, although the exclusive-OR gate and the master-slave sampling filter (MSSF) can work well with low voltage [25], the suppression of out-band noise is limited. Furthermore, locking at non-zero phase offset which causes spur noise is a serious trouble for type-I PLL. Therefore, the architectures of SSPLL and type-I PLL cannot be utilized for low voltage application directly.  
Some previous published low-voltage PLL designs [2, 3, 4, 5] demonstrate that transferring the PLL loop into a dual path loop which includes a proportional path (P-path) and an integral path (I-path) is a beneficial method to mitigate the limitation of circuits performances at the architectural level. Therefore, the dual path system might provide a chance for designers to take full advantage of the SSPLL and type-I PLL and, meanwhile, avoid the drawbacks of these two structures.  
Based on the problems and the possible idea of the solution, we present a hybrid dual path type-II SSPLL which can work at 0.8 V supply voltage with 196.5 f\({}_{rms}\) integrated and \(-251.6\) dB FoM (figure of merit as expressed in Equation (1) [26]).  
\[FoM_{PLL}=20\log\left(\frac{litter_{rms}}{1\;\text{s}}\right)+10\log\left(\frac {Power}{1\;\text{mW}}\right) \tag{1}\]
# Real-Time Indoor Positioning Based on BLE Beacons and Pedestrian Dead Reckoning for Smartphones
## 4 Performance Evaluation
### Experimental Results
#### 4.2.1 Step Detection and Heading Direction Estimation Results  
Since step detection and heading direction estimation greatly affect the accuracy of PDR positioning, we first conduct experiments to separately test their performances. Figure 5 shows the original sampled acceleration data and the data after filtering. The sampling frequency is set to 30 Hz, the window size for the moving average filtering is set to 20, the acceleration threshold is set to 1.75 m/s\({}^{2}\), and the step frequency threshold is set to 1 Hz. A total of 300 measurement points are plotted in Figure 5, and the red circles mark out the detections of steps, which are consistent with the ground truth. Figure 6 shows the estimated heading direction compared with the ground truth. It is observed that the estimated results are, in most cases, stable, and the deviations are less than 5 degrees when the user walks a straight line. It is worth noting that a sharp U-turn leads to a large fluctuation, but the estimation accuracy is restored in a short time.  
Figure 4: The devices used in the experiment and the deployment scenario. (**a**) The iBeacon tag used in the experiment. (**b**) The smartphone used in the experiment. (**c**) The deployment scenario of the iBeacon tags.  
Figure 5: The synthetic acceleration while walking.
## 2 NB-IoT System Detailed Design and Implementation
### NPDSCH Receiver Chain Implementation
#### 2.2.6 Channel Equalizer  
After estimating the channel effect, the equalizer plays its role to cancel the intersymbol interference (ISI) and the noise by divided the channel effect over all the transmitted symbols as follows [9]:  
\[y_{k}=s_{k}\times\frac{h_{k}}{h_{eq}}+\frac{n_{k}}{h_{eq}} \tag{33}\]  
where \(y_{k}\) is the equalized symbol, \(s_{k}\) is the received symbol, \(h_{k}\) represents the channel, \(h_{eq}\) is the estimated value of the channel, and \(n_{k}\) is the AWGN value.  
The block diagram of the channel equalizer block is shown in Figure 12. The main sub-block of the equalizer block is the complex divider but instead, the complex multiplier can be used to reduce the area utilization and the power consumption.  
Figure 11: Channel estimation block diagram.  
Figure 12: Equalizer block diagram.  
2.2.7. Parallel to Serial and NRS Removal  
In this block, the inputs should be applied serially starting from the modulation block to the CRC block. Additionally, the NRS symbols need to be removed from the equalizer's output, as the data at the transmitter do not contain them.
## 4 Discussion  
There are few papers devoted to the detection of BCC in the literature [10; 11; 12; 13], and less that apply deep neural networks to solve this problem. One of the main reasons is the lack of public databases with BCC lesions, with contours delineation or with labelled dermoscopic criteria.  
From a clinical point of view, it is very convenient to distinguish between BCC and nevus, due to the high incidence of these two types of lesions. Specifically, in primary health centers, it would be desirable to have an automatic tool, in order to help the non-specialist in the diagnosis and to establish a good priority in the attendance at the dermatology services. To the best of our knowledge this is the first time that a classification between BCC and nevus has been performed.  
Most works devoted to segment skin lesions claim that an accurate segmentation is necessary to achieve a proper extraction of features and consequent lesion characterization [47]. However, in this paper, we demonstrated that, when using deep learning methods, it is not advantageous to include a segmentation before classifying the lesion. Actually, we get worse results when segmenting the lesion previously to the classification step, showing that, when using a large database, the previous segmentation of the lesion does not improve the classification results. This suggests that the healthy skin surrounding the lesion may contain information significant for the classification. In this sense, other works, such as the one by [PERSON] et al., support this statement [48].  
The main limitation of our method is the lack of explainability of the classification. An explanation of the classification, by providing the automatic detection of dermoscopic criteria of BCC, would considerably improve the utility of the method for physicians. To this purpose, we are working on developing a database with the dermoscopic criteria of BCC and a system for the automatic detection of these dermoscopic criteria.  
As future research, a clinical study to assess the validity of the methods in a real clinical scenario would be desirable.
## 2 Results and Discussion
### Adsorption
#### 2.5.5 Adsorption Thermodynamics  
The thermodynamic parameters, including the Gibbs free energy change (\(\Delta G^{\circ}\)), enthalpy change (\(\Delta H^{\circ}\)), and entropy change (\(\Delta S^{\circ}\)), were calculated from the slopes and intercept of plots of Van't Hoff plot (\(\ln b\)_versus_\(1/T\)) as shown in Figure 9 by using the following equations:  
\[\ln b=-\frac{\Delta H^{\circ}}{RT}+\frac{\Delta S^{\circ}}{R} \tag{8}\]  
The Gibbs free energy change (\(\Delta G^{\circ}\)) was calculated from the following equations:  
\[\Delta G^{\circ}=\Delta H-T\Delta S^{\circ} \tag{9}\]  
where \(b\) represents the thermodynamic equilibrium constant, which can be obtained from Langmuir isotherms model at different temperature; \(R\) is the universal gas constant (8.314 J mol\({}^{-1}\cdot\) K\({}^{-1}\)); and \(T\) is absolute solution temperature (K).  
The values of \(\Delta H^{\circ}\), \(\Delta S^{\circ}\) and \(\Delta G^{\circ}\) for Cr(VI) adsorption on EDA-MPMs are presented in Table 4. The negative value of the \(\Delta G^{\circ}\) confirms that the adsorption of Cr(VI) onto EDA-MPMs is a spontaneous process. The Gibbs energy of the interactions demonstrates that the processes are favorable for the Cr(VI)\(\cdots\)(-NH\({}_{2}\)) electrostatic interaction. The positive value of \(\Delta H^{\circ}\) indicates that the adsorption process is an endothermic process. A higher temperature is favourable to the adsorption, which may explain the increase of \(q_{m}\) with the raise of temperature. The positive value of \(\Delta S^{\circ}\) reflects an increase in the randomness at the solid/solution interface during the adsorption process of Cr(VI) on the EDA-MCMs.
## 2 Leakage Model of Shield Joints Based on the Persson Model
### Influencing Factors of Shield Joints
#### 2.1.2 Sealing Principle and Influencing Factors of Shield Joint Leakage  
EPDM gaskets are affixed to grooves on lining segments by a waterproof sealant (Figure 2), so leakages may occur only in-between the area where a pair of gaskets contact each other, on the premise that the sealant-affixed areas are impermeable. Under assembly forces, the EPDM gaskets are compressed and undergo large deformations. Since it has properties of rubber with the ability of resilience, in a pair of gaskets, both of them tend to recover, which generates an elastic restoring force that presses the opposite gasket and generates contact stress, leading to close contact with each other. Consequently, the surfaces of gaskets that are rough on the microscale are squeezed and become even and smooth, which limits the development of potential leaking channels and achieves the goal of sealing. The contact pressure is inevitably affected by assembly errors during the construction. According to the GB50446--2017 \"Code for construction and acceptance of shield tunnelling method\", there is usually an allowable assembly error of less than 6 mm for subway tunnels [21].  
As noted above, the better sealing performance is achieved by squeezing EPDM gaskets to make the contact surface more compact. However, many factors could cause the poorer performance of joint sealing, such as deformation of the tunnel structure and relaxation or aging of EPDM gaskets. The joint gap and offset between lining segments caused by a poor assemblage or tunnel deformation (from the change of geology condition, all kinds of loads above the tunnel and ongoing surrounding construction), will change the contact stress and contact area of the sealing interface and further affect the sealing performance. The relaxation or aging of EPDM gaskets over the service time will cause some asperities on the sealing interface to show up again and further form new percolation channels. Additionally, for an underground sealing project, the roughness of contact surfaces and groundwater pressure will also influence leakage.  
The geometry and stress distribution of the sealing interface are determined by the roughness of the contact surface, and their expression under stress can be given by the Persson model [22; 23; 24; 25; 26].  
Figure 1: Configuration of the shield segment. It can be seen from the figure that each concrete segment is hooped by an ethylene-propylene-diene monomer (EPDM) gasket. During the construction stage, the EPDM gasket will be compressed into the groove on the concrete segment by high-strength bolts.  
Figure 2: The waterproof structure design of shield lining joints. The joint gap is the assembly clearance between the concrete segments, which determines the compression deformation of EPDM gaskets. The joint offset is the dislocation caused by a seam assembly error or tunnel structural deformation. It is subject to the joint gap and determines the length of the leakage path on the sealing interface.
## 2 Design of the CRLH Resonant Antenna
Since the connector is of comparable size to the antenna, during the measurements some of the waves from the transmitting antenna were reflected by the connector, which also contributed to the difference between the simulated and measured radiation patterns in Figure 6 in addition to the fabrication errors. Figure 1.0 Photograph of the fabricated CRLH antenna. Figure 1.1 (a) Structure of CRLH unit cell used to find the dispersion diagram (b) dispersion diagram of the CRLH unit cell used for the antenna. For the size comparison, the designed antenna is compared with the patch antenna at 30 GHz in Figure 1.2 The area of the patch antenna is 3.38 mm \(\times\) 2.545 mm, and the area of the designed CRLH resonant antenna is 2.46 mm \(\times\) 0.8 mm, which becomes 0.25 \(\lambda_{0}\)\(\times\) 0.08 \(\lambda_{0}\) in terms of free space wavelength \(\lambda_{0}\). Thus, a reduction of 22.8% is achieved. The comparison of the designed CRLH antenna with the previously reported CRLH resonant antennas is given in Figure 1.3 Patch-like radiation is achieved at the 30 GHz band in this work. Figure 1.4 Simulated and measured S parameter response of the antenna. Figure 1.5 Radiation pattern in the YZ plane. The solid lines denote the measured patterns, and the dotted lines denote the simulated patterns. The red color denotes the co-pol patterns, and the blue color denotes the cross-pol patterns. Figure 1.6 Size comparison. (**a**) Patch antenna at 30 GHz with \(l_{p}\) = 3.38 mm and \(w_{p}\) = 2.545 mm; (**b**) CRLH resonant antenna with \(l\) = 2.46 mm and \(w\) = 0.8 mm.
# Anticancer Efficacy of Antibacterial Quinobenzothiazazines
## 1 Introduction
Subsequent studies resulted in the design of angularly fused quino[3; 4-b][1, 4]benzothiazines, which demonstrated significant in vitro anticancer activity against a spectrum of human cancer cell lines [50; 51; 52]. In an earlier work, a unique, original method for the synthesis of azaphenothiazazine derivatives was presented [53], which allows the formation of derivatives containing specific substituents at different positions of the tetracyclic quinobenzothiazzinium system. The method involves the reaction of thiquinanthreenediinium bis-chloride (**1**) with substituted isomeric hydroxyanilines (**2**). The intermediate product of these reactions is a betaine system with the structure of 1-methyl-4-(phenylamino)quinoline-3-thiolate (**3**), the cyclization of which leads to the formation of a thiazine ring. The control of the parameters of the cyclization reaction enables its selective course and allows the unique introduction of various types of substituents in the 9, 10 and 11 positions of the quino[3, 4-b][1, 4]benzothazine scaffold (see Scheme 1). Using this method of synthesis, a series of propoxy, allyoxy, propargyloxy and benzyloxy derivatives were obtained (see Table 1). Recently, antimicrobial activity against Gram-positive bacteria and mycobacteria has also been reported [53]. From the point of view of the treatment of oncological patients in whom anticancer therapy causes general immunosuppression, in addition to resistant nosocomial pathogens, common infections or opportunistic pathogens pose a great threat to life for these patients, it is advantageous if the compounds show anticancer and antimicrobial activities [54; 55; 56]. This fact led us to the goal of this study, namely, to perform a primary in vitro anticancer screening of active antimicrobial agents investigated recently [53]. Furthermore, the molecular docking approach was used for the most potent agents against the human breast cancer line T47D in order to gain insight into the aromatase-inhibitor binding mode, which might be potentially valid for the antitumor characteristics of new quinobenzothiazazine derivatives.
## 3 Results
### Spatiotemporal Dynamics of LST and ET during the 2009 Drought  
To explore more potential thermal dynamics of the 2009 drought from satellite remote sensing, we analyze seasonal variations in the 16-day mean and multiyear mean and change percent of LST and ET from July to November 2009 over the entire study area (Figure 6). During the 2009 drought, LST started to become higher than the multiyear mean on the day of year (DOY) 209 (which corresponds with the second half of July), which lasted until November. ET showed negative anomalies from the multiyear mean on DOY 257 (which corresponds with the first half of September), and the anomaly magnitudes were approximately \(-18\%\) to \(-2\%\) from September to November 2009.  
The spatial distributions of monthly LST and ET anomalies from July to November 2009 are also analyzed, and given in Figure 7. LST began to be higher than the multiyear mean in August and was continuous until November 2009 in almost the entire study area. During this period, the LST was much higher than the multiyear mean in most of the central and eastern parts in October. ET started to show slight negative anomalies in most parts in September 2009, and presented moderate-to-severe negative anomalies in most parts from October to November.  
Figure 6: Seasonal variations in the 16-day mean and multiyear mean (**a**) LST and (**b**) LST change percent, (**c**) ET and (**d**) ET change percent from July to November 2009 over the entire study area.
## 3 Results and Discussion
### Consideration of the ESC Market Benefit  
If the benefit of the ESC market is added to the benefit of the generated electricity, there would be a probability of achieving positive NPV during all the periods of 5, 7, 9, and 11 years. The probability of achieving positive NPV during the periods of 5 and 7 years are 44% and 99.1%, respectively, and during the periods of 9 and 11 years, it will be carried out with certainty due to the considered assumptions in the calculations. The expected value of NPV during the periods of 5, 7, 9, and 11 years are \(-\)0.02, 0.71, 1.34, and 1.87 million dollars, respectively, which are better from the NPV/I viewpoint compared to the case where the certificates sale is not considered.  
Although the expected NPV/I increased considerably by the benefit of certificates, it is less than the one in the mentioned time periods. This means, the market regulator or ESCO should perform other actions to improve it further. As mentioned at the beginning of the article, an ESCO should remain motivated to try for better solutions such as developing more cost-effective technologies, and because of this, the market regulator should make a decision to include or not include more support by consideration of how the risk in ESCO projects changes by implementation of the different risk management methods that ESCO or market regulators can implement. So, if the ESCO is able to manage the risk by practical and exceptional solutions, it is better that more supports are avoided. Otherwise, the regulator can consider more supports such as setting the floor price for certificates to limit its price volatility.  
In this study, the effect of the reduction of the investment cost of ORC is analyzed as a risk management action of ESCO and is shown in Figure 11. To investigate this issue, 10%, 20%, and 30% reduction in the investment cost of ORC were applied. The investment cost was considered $2000 per kilowatt in the assumptions that it will be $1800, $1600, and $1400 per kilowatt in these cases, respectively.  
With a 30% reduction of the investment cost, the expected value of NPV during the periods of 5, 7, 9, and 11 years will be $0.62 M, $1.37 M, $2.04 M, and $2.65 M, respectively, which leads to a value for NPV/I that is more than one during the 7 and 9 year periods. So, if the regulator determines that this amount of the reduction of investment cost is achievable by the ESCO, then it can determine the right time period by consideration of this issue. It is notable that the recognition of the possibility of reduction of investment cost could be done by analyzing the related issues to the technology such as the historical cost curve of the technologies by the experts.