esa-sceva/satcom-chunk-collection huggingface dataset:
# Solutions to Increase PV Hosting Capacity and Provision of Services from Flexible Energy Resources
## 1 Introduction
In general, DER control can be roughly divided into local, distributed, and centralized control methods [10; 11; 12]. The management scheme can be also a hybrid i.e., combination of centralized and distributed control features. In the literature, different BESS control schemes and sizing principles have been proposed in [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. Different control methods have been also proposed to improve the BESS dynamic inertial response [36]. It can be noted that some of the proposals, such as [10; 36], consider only the use of active power \(P\) control and do not use the reactive power \(Q\) control for local voltage control. It has been shown however that more effective voltage control can be achieved by using both \(P\) and \(Q\) control capabilities of BESS [8; 32]. Due to their rapid response and dynamics, BESSs are also capable of providing system-wide (TSO level) frequency control related services. These services can be realized by an individual large-scale BESS or by aggregating multiple small-scale BESSs [37; 38]. The optimization of BESS for multi-purpose use in order to provide different local (DSO) and system level (TSO) services should be also taken into account [37]. This paper studies and proposes new solutions and adaptive settings and management schemes in order to increase DER (PV) hosting capacity and the capability of distribution network connected DER to provide flexibility services, predominantly frequency support for system-wide (TSO) needs. In particular, this paper deals with problems such as (1) the coordination between DER units functions and OLTC settings, (2) interactions between different DER units and their control functions, and (3) multi-purpose use of BESS to increase their short- and long-term viability for grid integration (stationary applications). Simulations focus mainly on studying the potential DER (PV and BESS) units _QU_-, _PU_-, and _Pf_-control interactions in different situations. Based on the case studies results, the paper proposes frequency adaptive _PU_-droops and role of _PU_-droops implementation logic with different DER configurations. The way unwanted interactions between DER units and their control functions can be avoided is shown. In addition, novel real-time adaptive management of DER _PU_-and _QU_-droops and HV/MV substation transformer's OLTC settings is proposed. Section 2 of the paper presents the simulated case studies and explains the differences with previous research, the DER droop control settings, and the simulation sequence. Section 3 shows the PSCAD simulation results from the main study cases, presents simulation results by utilizing real-life frequency data, and proposes new frequency adaptive _PU_-droops for PVs and BESSs. In Section 4 new real-time adaptive management schemes for _PU_-droops, _PQ_-flow dependent OLTC settings, and OLTC setting dependent _QU_-droops are proposed. Finally, conclusions are drawn in Section 5.
## 4 Experimental Results and Discussion
### Droplet Size and Velocity Distribution on Axis Direction
Based on the above discussion, it can be concluded that in the range of Z = 3 cm to Z = 13 cm, the droplet size is the smallest, and the velocity is the largest when the surface tension is 33.6 mN\(\cdot\)m\({}^{-1}\) and the viscosity is 1.12 \(\times\) 10\({}^{-3}\) Pa\(\cdot\)s. In the interval from Z = 13 cm to Z = 19 cm, there is no significant difference in droplet velocity and size among the three solutions, and they are in a fluctuating state. Changes in surface tension and viscosity within the core atomization region (Z = 3 cm to Z = 13 cm) can significantly affect droplet velocity and size. Near the nozzle (Z = 3 cm), viscosity has a more significant effect on droplet size and velocity than surface tension. In the edge region of atomization, neither viscosity nor surface tension have significant effects.
## 3 Results and Discussion  
### Validation of IrigWQ Output  
The Tier 1 output is based on the same or similar relationships to those used in established guidelines and, thus, produces similar results [6; 7; 8]. IrigWQ uses established criteria for both Tier 1 and Tier 2 assessments to interpret the effect of irrigation water constituents on FFU and for establishing WQRs. However, its treatment of the soil-plant-atmosphere continuum at the Tier 2 level is substantially more mechanistic and dynamic than traditional approaches for the assessment of irrigation water quality. For most conditions, the outcome of Tier 2 assessments can, thus, only be accepted or rejected on the basis that the results make intuitive sense, or not. However, to increase confidence in the output produced by IrigWQ, a virtual experiment was run to compare the crop yields predicted by IrigWQ with those of the authoritative FAO water quality guidelines [2]. To establish site-specific conditions for IrigWQ that would be comparable to the assumptions made by the FAO water quality guidelines, IrigWQ simulations were conducted with the following settings:1. Precipitation was artificially set to zero in the weather station database (the Douglas, South Africa, weather station was used. Latitude (S) -29.05. Longitude (E) 23.77);
2. Irrigation management was adjusted to achieve leaching fractions of approximately 15% and 20%, which is in line with the leaching fraction range assumed by the FAO guidelines for their calculations;
3. IrrigWQ was run for a period of 40 years using the irrigation water ECs, which were calculated by the FAO guidelines to produce relative yields of 75%, 90%, and 100% for five crops with widely varying salinity tolerance (namely, barley, wheat, soybeans, maize, and carrots). While the FAO guidelines [2] report a single crop-specific yield for a specific irrigation water EC using their calculation procedure, IrrigWQ calculates 40 different annual crop yields and leaching fractions (as determined by varying weather conditions) over the 40-year simulation period. Table 3 reports the irrigation water ECs calculated with the FAO guidelines procedure to produce the relative crop yields of 75%, 90%, and 100%. Also reported are IrrigWQ calculated ranges of crop yields, and their means, using the same irrigation water ECs as the FAO guidelines and the extremes (15% and 20%) of the leaching fraction range that apply to the FAO guideline calculations. It is postulated that agreement between yields published by the FAO guidelines as an authoritative publication and those calculated using IrrigWQ for the same irrigation water ECs would create confidence in the ability of IrrigWQ to correctly simulate complex conditions in the soil-climate-atmosphere continuum. When the relative crop yields calculated with the two approaches are compared, IrrigWQ yields using a leaching fraction approaching 20% appear to be very similar to
# University Student Dropout Prediction Using Pretrained Language Models
## 3 Methodology
### Modeling
#### 3.2.3 Student Dropout Prediction  
Figure 2 illustrates the architecture of the proposed model. We utilized BERT\({}_{base}\)[27] and SimCSE-BERT\({}_{base}\)[31], popular transformer-based PLMs, to extract demographic and academic representations. We input a given token sequence \(S\) into the model, which produced a sequence of contextualized token representations \(\tilde{S}\) as follows:  
\[\tilde{S}=\{[CLS],\hat{p}_{1},\hat{p}_{2},\ldots,\hat{p}_{m},[SEP],\tilde{d}_ {1},\tilde{d}_{2},\ldots,\tilde{d}_{k},\hat{a}_{1},\hat{a}_{2},\ldots,\hat{a}_ {l}\}, \tag{4}\]  
where \(\tilde{S}\) is a sequence of contextualized token representations, \(\hat{p}_{1},\hat{p}_{2},\ldots,\hat{p}_{m}\) denote the representation of the continuous hypothesis and \(\tilde{d}_{1},\tilde{d}_{2},\ldots,\tilde{d}_{k},\hat{a}_{1},\tilde{a}_{2}, \ldots,\hat{a}_{l}\) denote the representation of the premise. To predict the probability of a student dropout, we used the contextual representation of the \([CLS]\) token as input to the two-layer MLPs. The output of the network was passed through a sigmoid function, producing a probability score between 0 and 1, where higher values indicated a higher likelihood of dropping out. The network's parameters were trained to minimize the binary cross-entropy loss, which was calculated using the predicted probability and true label for each training instance. The binary cross-entropy loss formula is as follows:  
\[L=-\frac{1}{N}\{\sum_{i=1}^{N}y_{i}log(\hat{y}_{i})+(1-y_{i})log(1-\hat{y}_{i} )\}, \tag{5}\]  
where \(N\) is the number of training instances, \(y_{i}\) is the true label, and \(\hat{y}_{i}\) is the predicted probability for the \(i\)th instance.  
Figure 2: Illustration of the architecture for our proposed framework of STUD.
# Noise Cancellation Method for Mud Pulse Telemetry Based on Discrete Fourier Transform
## 2 Methods  
### MPT System Model  
Figure 1 shows a schematic illustration of a typical MPT system with sensors and mud pumps installed. In such a system, the measurements are generally taken by instruments mounted within drill collars above the drill bit, and the drill bit is controlled by electric machines [25; 26]. Once generated, the information signal is transmitted uphole to the surface. Then, the ground signal processing unit will analyze the signal. Since the downhole signal is generated remotely from the uphole sensors near the mud pumps, the information signal is overwhelmed by the noise generated by the mud pumps [27; 28]. Figure 2 displays the layout of pressure sensors we used in the real field test. There are six sensors that should be used for data measurement, including four pressure sensors (S1-S4) and two pump impulse sensors. The four pressure sensors are distributed along the mud pipelines between the mud pumps and the top drive. Two pump impulse sensors are installed at the switches of the mud pumps, indicating the operating cycle of the mud pump piston, helping to determine the period and start time of the pump noise in the pressure signal, playing a key role in the noise elimination.  
Considering that the data transmission speed of the mud pulse telemetry system is generally much faster than the drilling speed, the MPT system can be regarded as a linear time invariant system, and the mud communication system model can be expressed as  
\[r(t)=x(t)*h(t)+n(t) \tag{1}\]  
where \(r(t)\) is the received signal, \(*\) means convolution, \(x(t)\) is the input transmitted signal, \(h(t)\) is the impulse response of the mud channel, and \(n(t)\) represents the noise.  
As mentioned before, pump noise is the main factor of noise in the mud channel. For this reason, the received noise signal is modelled as the sum of periodic noise and random non-periodic noise:  
\[n(t)=p(t)+w(t) \tag{2}\]  
Figure 1: A sketch of an MPT system.  
Figure 2: Layout of pressure sensors.  
where \(p(t)\) represents the periodic component, and \(w(t)\) is the non-periodic noise, which is usually assumed to follow Gaussian distribution. The interference signal \(p(t)\) can be represented as  
\[p(t)=\sum_{i=1}^{p}A_{i}sin(2p\pi f_{i}t+\varphi_{i}) \tag{3}\]  
where \(A_{i}\) is the amplitude of the \(i\)th noise component, \(f_{i}\) is the frequency of the \(i\)th harmonic signal, \(\varphi_{i}\) is the initial phase of the \(i\)th harmonic signal, and \(p\) is the number of harmonic signals.
# Methodology for the Efficient Progressive Distribution and Visualization of 3D Building Objects
## 2 Related Work
### Visualization Frameworks for City Models  
The fast visualization of 3D building models is essential for several applications, especially for online situations. The use of mobile devices, such as smart phones, to view city models also creates an extra demand for efficient methods of visualization. Due to the diversity of mobile devices, browser-based 3D visualization is widely required. X3 DOM [55] can integrate X3D with HTML5 and create interactive 3D scenes for the Web, similar to the way SVG currently works in 2D. [PERSON] et al. [56] indicate that X3 DOM provides a single declarative developer interface, based on current web standards, and supports various back ends through a powerful fallback model for runtime and rendering modules. However, according to [PERSON] and [PERSON] [57], the system performance of X3 DOM reduces significantly when the number of complicated 3D buildings increases in online visualization. Therefore, it is important to find methods to generate lower LODs from higher LODs automatically by generalizing city objects. In addition, unnecessary details need to be hidden to avoid visual cluttering.
## 2 Related Work
### Attention Mechanism  
One disadvantage of convolution is that all spatial-spectral pixels undergoing convolution have equal weights in both spatial and spectral domains. However, in fact, different spectral bands and spatial pixels have different contributions to the extraction of spectral-spatial information. The proposed attention mechanism emphasizes the most influential information, inhibits the influence of useless information on the result, and thus makes the classification result more superior. Therefore, the attention mechanism is a powerful technique in processing hyperspectral remote sensing image classification tasks. A spatial attention mechanism is added to the channel attention mechanism, which extracts the  
Figure 3: The comparison between Mish and ReLU.  
spatial position correlation of the target image, and this joint spatial-spectral attention mechanism enables the network to extract more abstract and decisive features. The convolutional block attention module (CBAM) [42] is a representative module in the joint spatial-spectral attention mechanism.  
Thus, we propose 2D depth-wise separable convolution attention (2D-DCAtention) by combining CBAM and DSC, as shown in Figure 4. Specifically, the input is first extracted by 2D-DWC for spatial dimension, then further processed by BN and the Mish activation function. The obtained features are sent into CBAM to strengthen features in the spatial dimension and spectral dimension, and the enhanced features are processed by PWC to enhance the features between each channel.  
The channel attention module and spatial attention module are described in detail as follows.
# Q-Learning Based Fair and Efficient Coexistence of LTE in Unlicensed Band
## 5 Conclusions  
The use of LTE-U in an unlicensed spectrum is an auspicious alternative to satisfy the multimedia data demand foreseen by upcoming IoT systems. For coexistence between LTE-U and Wi-Fi systems,  
Figure 10: Efficiency versus number of steps.  
Figure 9: Fairness versus number of steps.  
fair coexistence and a maximization of the channel utilizations are two important design goals. However, achieving both of these design goals together is extremely difficult. This study presented a Q-learning based joint adaptive duty cycling (ADC) mechanism and a dynamic channel switch (DCS) mechanism to facilitate a fair and efficient coexistence. The adaptive DC mechanism supports a fair channel access opportunity by muting a certain number of subframes for Wi-Fi STAs, whereas the DCS mechanism offers more access opportunities for LTE-U UEs by avoiding the UEs to occupy a crowded channel. The simulation results demonstrate a fair coexistence and reveal the benefit of using the proposed mechanism over other DC-only and COT-based mechanisms.  
Conceptualization and methodology, R.B.; simulation and writing, R.B. and R.S.; supervision, S.W.K.  
This research was supported in part by the Brain Korea 21 Plus Program (No. 22A20130012814) funded by the National Research Foundation of Korea (NRF), in part by the MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program(IITP-2019-2016-0-00313) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation), and in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1A09082266).  
The authors declare no conflicts of interest.
# A Systematic Review of Cybersecurity Risks in Higher Education
## 9 Analysis and Discussion of Cyber Risks and Countermeasures in HE
### Loss of Service Availability  
Universities are high availability organizations considering services such as the internet connection, email systems, and digital libraries. For most universities, core processes will immediately suffer if critical services become unavailable. Weak risk management regarding not identifying essential systems and not making contingency plans is a significant vulnerability in HE. Under-staffing when operating security-critical systems is also a vulnerability, especially when personnel is unaware of its critically. Random errors can cause prolonged downtime and service level reduction if the HE institution is missing the appropriate workforce to restore the system quickly.  
A large attack surface means many targets for both hacking and DoS attacks. The possibilities for sabotage are many--Intentional DoS attacks can cripple parts of a poorly protected institution's IT service delivery. Hacking attacks can cripple and shut down servers. Random errors can also lead to critical incidents in systems supporting IT communication, such as email. Furthermore, the Corona-pandemic in 2020 forced the HE sector to digitize. This development increased the requirements of IT-system availability to conduct core functionality, such as teaching.  
_Countermeasures:_ Potential countermeasures for mitigating loss of availability can include conducting backups, updating and patching programs, and implementing incident response and management policies [42; 72]. Identifying and prioritizing mission-critical systems is essential when planning for redundancies and business continuity.
## 4 Discussion
Improvements in the ergonomic design of the VRDS equipment were also suggested for better access, vision and improved control of the virtual handpiece and dental mirror. Dental students represent the key stakeholders in dental education and their feedback can be fed back to the manufacturers to further enhance the VRDS technology. VRDS was perceived to provide a less stressful learning environment for novice students as it allows correction of errors and deficiencies without the need to replace the models. The use of a virtual handpiece offers a low-risk learning environment compared to training on models involving the use of a physical handpiece. Moreover, minimal running expenses of VRDS make it a useful learning tool to introduce endodontic procedures before the students start consolidating their skills on dental models. Participants in this study expressed the need to have additional opportunities for training on VRDS and suggested that in addition to the automated feedback provided by the software, additional advice from supervisors would be helpful for the students. A recent study also supports the use of VRDS to provide clinically relevant and qualitative feedback to undergraduate dental students [34]. A critical review on the use of VR in dentistry also emphasizes timely feedback and deliberate practice to translate the benefits of VRDS into competence of dental students [35]. These recommendations are in accordance with the findings of the present study. Based on these findings, we aim to provide a more structured preclinical training on VRDS to our future cohorts at an earlier stage of the undergraduate dental curriculum. Suchan approach may allow more time for the development of core skills before they translate them on physical models and real patients in preclinical and clinical settings, respectively. A few limitations of the study need to be mentioned. Firstly, the sample size of this study was small due to the size of the study cohorts. Future studies involving a larger sample size from multiple institutions may serve to enhance the generalizability of the findings. Secondly, the intervention evaluated the experience of students on VRDS involving a single exercise in pre-clinical settings. In addition to standard models available in the VRDS library, it might be helpful to provide additional training on simulated natural teeth. Moreover, longitudinal follow-up of students in their transition to clinical settings, and triangulation of their performance on real patients may allow educators to capture a more holistic picture of their competency development in endodontic access cavity preparation. Nevertheless, the findings of this study provide useful insights into the strengths and limitations of VRDS training in endodontic access cavity preparation and recommendations to further improve the learning experiences of students.
## 4 Techno-Economic Assessment
Finally, focusing on the electricity price, strong impacts are observed, reducing the DPB from 12.5 years to 4.8 years as the electricity price increases from EUR 0.05/kWh to EUR 0.30/kWh. A strong dependence with the electricity price is also found for the IRR and NPV.
###### Abstract  
In this study, different data types are employed according to the objectives of each experiment. Simulated orbit data are used for analyzing the effects of atmospheric drag modeling in order to ensure controlled and consistent comparisons. In contrast, real on-orbit data from the Gaofen-7 satellite are used for evaluating gravity field models with the aim of validating the accuracy and adaptability of the proposed method in real orbital environments. By examining the effects of various gravity field orders on orbit determination accuracy, computation time, and other factors using spaceborne GNSS navigation receivers, the study concludes that setting the gravity field model order to 60 is the best option for LEO satellites at 500 km altitude. With this setup, the orbit determination accuracies in the radial, tangential, and normal directions are 0.420 m, 0.191 m, and 0.225 m, respectively, for a three-dimensional position accuracy of 0.522 m. The computation time is 286 ms.  
spacestar: gravity field model; system resources; sub-meter level orbit determination; engineering applications Article