esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting the Impact of Academic Key Factors and Spatial Behaviors on Students' Performance
## 3 Data Collection
### Fuzzy Delphi Method  
A systematic review of the student performance factors revealed that there are many key factors. As it is impractical to consider all input factors for the student performance prediction process, a proper screening approach must be utilized to identify the significant factors between a larger set of inputs. Delphi is an extensively used method for screening that tries to seek the most critical or influential elements of a phenomenon under consideration. However, there are certain demerits of the traditional Delphi method including less uniformity of fine judgments, increased computational cost, and up-gradation of skilled personal judgments to attain uniform altogether judgments [46]. The Fuzzy Delphi Method [47] addresses these demerits and has received significant popularity since its inception. This research utilizes the Fuzzy Delphi Method to screen out the most significant input factors for student academic performance prediction. The Fuzzy Delphi Method (FDM) was employed in the screening and shortlisting of input features for student performance prediction. The threshold value was determined through this process to seek expert judgments regarding the significance of the evaluation factors in the list [48]. A questionnaire survey is utilized as the main research tool for the data collection in this phase. This questionnaire comprises two sections, where the first section collected the basic information and demographics of the experts. The second section collected the opinions of the experts regarding the significance of a particular input factor for student performance prediction. Considering a large number of factors, the second part of the questionnaire is divided into five sub-sections guided by the underlying perspectives. A Likert scale of 1-5 is used to evaluate the factors where the higher point refers to the
## 3 Application to the MAGIC Mission
### Seven-Day Subcycle  
Table 2 lists the near-optimal orbits of the inclined and polar pair for the 7-day subcycle sampling. As it can be observed, the number of possibilities is reduced with respect to the 5-day scenario. This is due to the ground track homogeneities, which are more sensitive to changes in altitude for longer subcycles. The latter can also be seen from Figure 4, where the altitude ranges, over which small homogeneity values stretch, appears reciprocal to the length of the subcycles.
## 2 Satellites and 'Mega-Constellations'
### Satellites
Small satellites can serve a variety of functions that benefit humankind, such as remote sensing, which can monitor weather, natural disasters, water coverage on Earth and many other functions, which it is hoped will contribute towards humankind's ability to live more sustainably. The existing narrative around satellites highlights the benefits and advancements that these humanmade machines have allowed humankind to make and heralds the era of further advancement that new satellite mega-constellations will bring.
# Bluetooth Low Energy Mesh Networks: A Survey
## 3 BLE Mesh Networks: Standardization
### Bluetooth SIG: Bluetooth Smart Mesh  
In early 2015, the Bluetooth SIG issued a press release announcing the creation of the Bluetooth Smart Mesh Working Group, the purpose of which is to develop an architecture to enable support for the mesh topology with BLE [33]. This initiative has enjoyed significant support since its inception, with 80 participating companies from a wide range of industries, including automotive, mobile telephony, industrial automation, home automation, and consumer electronics. Bluetooth SIG announced in their 2016 road map that mesh support was part of several enhancements to BLE to better support the IoT [34]. While the aim of the Bluetooth Smart Mesh Working Group is to build a common platform that can be useful for numerous use cases, home automation is probably the most clearly identified target application domain for BLE mesh networks as per current commercially available proprietary products (see Section 5). Bluetooth Smart Mesh can leverage the ubiquity of BLE in smartphones and other consumer electronic devices in the home.  
The draft specification being developed by the Bluetooth Smart Mesh Working Group is not yet publicly available. However, Cypress and Mindtree demonstrated a mesh solution that met the latestversion of the proposal under development by the Bluetooth Smart Mesh Working Group available in early 2016 and in early 2017, respectively [24; 35] (see Sections 5.6 and 5.10).
###### Abstract  
Ships and offshore structures are subjected to impact loads, such as slamming and sloshing. High impact pressures can cause permanent hull deformation by a single impact event. In addition, significant fatigue damage can be accumulated via repeated impact pressures. In this study, the plastic deformation behavior of flat plates under slamming impact is numerically investigated using a coupled Eulerian-Lagrangian method. The dynamic impact pressure of the flat plates by weight and drop height is investigated under the assumption of viscous and compressible fluids. To evaluate the plastic deformation of the plate, contact between water and the plate is removed after a certain duration after dropping, and then the remaining deformation is measured. Optimized finite element models for drop simulations are selected via a mesh sensitivity study, and the simulation results are calibrated and compared with experimental data. Results of the simulation and the experiment show good agreement in general in terms of deflection range. However, because the initial condition of the plate is not reflected in the simulation, some discrepancy is observed in maximum deflections. Finally, a discussion is presented for a more accurate fluid impact analysis model based on the comparison results with the experimental data.  
slamming; permanent deformation; coupled Eulerian-Lagrangian method; flat plate Article
# An Information Entropy Based Event Boundary Detection Algorithm in Wireless Sensor Networks
## 1 Introduction  
Wireless Sensor Networks (WSNs) are composed of a considerable number of low-cost, low-power and small-sized wireless sensors and have gained particular interest for many environment applications [1]. These sensor nodes are often utilized to monitor and detect real-world events like oil diffusion, fires, chemical leaks by monitoring various physical parameters such as humidity, concentration, temperature, salinity and so on. In many monitoring tasks, the core goal of WSNs is to detect and track unexpected and abnormal events in real time [2; 3]. Event boundary detection is of particular importance to determine the scope of event occurrence and make preventive measures when unfortunate events happen in the environment. In many cases, an event may spread over a network and sensor-based area of an irregular shape. After a sudden incident such as oil spill pollution, timely and accurate detection of the diffusion range of events requires lots of sensor nodes to work together and consumes large amounts of energy. When compared with the analysis of the entire event area, event boundary detection is more efficient as it provides a proper view of the sensors that will be affected by broadcast messages [4].  
This paper proposes a new lightweight Event Boundary Detection distributed algorithm (EEBD) to identify the real event boundary sensor nodes of a monitoring area using WSNs. Two main principles of Information Entropy, that is, uncertainty and information quantity [5], are applied to figure out theprobability of a node being a boundary node. We introduce an entropy based algorithm that determines the boundary nodes by comparing the values of entropy amongst the neighbouring nodes. A series of simulations show that the EEBD algorithm provides good precision when detecting event boundaries.
## 4 Model and Control of PZT
### Mathematical Model of Hysteresis  
To improve the driving control accuracy of the piezoelectric actuator with a type of NAC2023 whose resonant frequency is 486 kHz, a polynomial model is applied to hysteresis modeling and compensation because of its speed and practicality, and this enables a precise description of the hysteresis phenomenon based on the statistic rule [15; 16]. Calibrating the relationship between the displacement and the direct current (DC) driving voltage within the piezoelectric actuator is performed via the optical platform in a stable experimental environment. The driving voltages of the actuator are provided by a piezoelectric power controller, and the displacements are measured by a linear variable differential transformer (LVDT) of the XSI3000 type, whose resolution is about 0.005 \(\upmu\)m. The test and measurement results for hysteresis, which are marked with the symbol \"\(\circ\)\", and which include rising and descending hysteresis curves denoted by blue and dotted lines, are shown in Figure 3. In Figure 3, it is obvious that each ascending curve has an inflection point, but the descending curves do not have one. Therefore, we use the third and second polynomials, respectively, to fit the ascending and descending hysteresis curves. For example, the fitting results, denoted by the red and solid lines of the outermost ring, are respectively expressed by  
\[y_{as0}=-1.6211\times 10^{-7}u^{3}+4.7907\times 10^{-5}u^{2}+0.0122u-0.0037 \tag{7}\]  
\[y_{ds0}=-4.1799\times 10^{-5}u^{2}+0.0213u+0.1167 \tag{8}\]  
where \(y_{as0}\) and \(y_{ds0}\) are the displacement of the actuator in the ascending and descending stages, respectively, and \(u\) is the driving voltage of the actuator.  
Figure 3: Hysteresis curves of the PZT.  
The results of Figure 3 show that the relationship between the inner ring curves with a smaller range of driving voltages and the outermost ring curve is approximately linear. Therefore, for the descending curves, the relationship of the slope between the inner and outermost rings can be approximately expressed by Equation (9), as follows:  
\[\dot{y}_{dsn}(u)=\dot{y}_{ds0}\big{(}u_{sj}+u-u_{0}\big{)} \tag{9}\]  
where \(u_{0}\) is the start voltage of the inner ring's descending curves, \(u_{sj}\) is the maximum voltage of the outermost hysteresis, and \(y_{dsn}\) is the displacement of the descending curve. The inner descending curve model \(y_{dsn}(u)\) and ascending curve model \(y_{ssn}(u)\) are established by Equation (9) [15].  
The experimental inner loop models are established by the measured ascending and descending displacements, respectively, of the piezoelectric actuator, and these are shown in Figure 4, where the blue lines are the inner loop model curves. The maximum error between the inner loop model and the measured displacement is less than 0.042 \(\upmu\)m.
# Cold Boot Attacks on the Supersingular Isogeny Key Encapsulation (SIKE) Mechanism
## 3 Supersingular Isogeny Key Encapsulation Mechanism
### Mathematical Foundations
#### 3.1.2 The Finite Field \(\mathbb{F}_{p^{2}}\)  
The quadratic field extension \(\mathbb{F}_{p^{2}}\) can be constructed easily from \(\mathbb{F}_{p}\). In particular, the elements of \(\mathbb{F}_{p^{2}}\) are represented by \(e=e_{0}+e_{1}\cdot i\), where \(e_{0},e_{1}\in\mathbb{F}_{p}\), \(i\in\mathbb{F}_{p^{2}}\), with \(i^{2}=-1\). Note that \(e\) can also be represented as a 2-tuple \((e_{0},e_{1})\in\mathbb{F}_{p}^{2}\). The addition and multiplication operations are defined as follows:  
* If \(a,b\in\mathbb{F}_{p^{2}}\), then \((a_{0}+a_{1}\cdot i)+(b_{0}+b_{1}\cdot i)=(a_{0}+b_{0})+(a_{1}+b_{1})\cdot i \in\mathbb{F}_{p^{2}}\), where the additions \((a_{i}+b_{i})\) carries out in \(\mathbb{F}_{p}\). Equivalently, \[(a_{0},a_{1})+(b_{0},b_{1})=((a_{0}+b_{0})\;mod\;p,(a_{1}+b_{1})\;mod\;p).\]
* If \(a,b\in\mathbb{F}_{p^{2}}\), then \((a_{0}+a_{1}\cdot i)\cdot(b_{0}+b_{1}\cdot i)=(a_{0}b_{0}-a_{1}b_{1})+(a_{0}b_{ 1}+a_{1}b_{0})\cdot i\in\mathbb{F}_{p^{2}}\), where the element operations take place in \(\mathbb{F}_{p}\). Equivalently, \[(a_{0},a_{1})\cdot(b_{0},b_{1})=((a_{0}b_{0}-a_{1}b_{1})\;mod\;p,(a_{0}b_{1}+a _{1}b_{0})\;mod\;p).\] Since \(\mathbb{F}_{p^{2}}\) is a field, we can compute the additive inverse of \(a\), denoted by \(-a\), and also the multiplicative inverse of \(a\), denoted by \(a^{-1}\). To see more details on how all field operations are calculated, we refer the reader to [1].
## 3 Results  
### Static Simulation Mode (Pre-Contingency)  
The results of the static simulation before emergency mode allow us to assess the system load and how the load is distributed over the power lines. Figures 6 and 7 show the load scenarios of the Baltic system (in Winter max scenario) for different system loads (5 GW and 6.5 GW). Figure 6 shows the power/capacity distribution of the simulated lines.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Load State**} & \multicolumn{4}{c}{**Load, MVA**} \\ \cline{2-5}  & **Summer Min** & **Summer Max** & **Winter MIN** & **Winter Max** \\ \hline Lithuania & 1160 & 1980 & 1420 & 2860 \\ \hline Latvia & 650 & 1080 & 1050 & 1560 \\ \hline Estonia & 660 & 1460 & 790 & 2080 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Plan 2030 mode load data.  
Figure 6: Capacity distribution of 330 kV lines.  
Figure 7: Graphs of Ratio vs. number of 330 kV lines with system load of 5 GW and 6.5 GW, respectively.  
The cases with system load values of 5 GW and 6.5 GW (+30% from baseline) are presented in Figure 7. These loads correspond to the 2022 and 2030 (predicted) load scenarios of the Baltic States. In the case of the actual 2022 (5 GW) mode, the highest relative load level is 46.1%, and the average load is 15.7%. In the 2030 mode, the maximum load is 54%, and the average load is 20.1%. These changes in maximum and average loads do not appear to be large, but as the system is dominated by high-capacity (800-1000 MW) power lines, such changes are significant. The graphs using the same data as those given in Figure 6 but given in MVA unit are shown in Figure 8.  
As shown in Figure 8, the maximum load on power lines in the base mode is 364 MVA, and the average load value is 133.8 MVA. In the 2030 mode, the maximum load is 515 MVA, and the average load is 171.9 MVA. The relative change between peak values is 41.5%. The relative change in average load is 28.5%, which is close to a 30% increase for the 2030 load mode.  
In the Baltic system, the transmission capacity of the most powerful (highest capacity) power line is 1143 MVA, and the average transmission capacity of all lines is 877 MVA. The capacity of half (50%) of the simulated lines in the network exceeds 940 MVA.  
After the evaluation of the load on the 330 kV transmission lines of the Baltic system, it can be noticed that the system has considerable reserve. In the normal (pre-contingency) operation mode, the most loaded line reaches only 50% of its capacity, which is not much compared to Western European networks. It was found that the transmission lines connected to large generation sources/nodes and interstate lines LTU-LV and LV-EST are the most heavily loaded.  
As mentioned earlier, four different load modes were modeled under two scenarios, with eight variants in total. Winter max load modes are examined in more detail, assuming that other load modes are not so critical for the Baltic system. The results of other load modes are summarized in the conclusions.
## 8 Feature Selection  
To avoid overfitting, feature selection was performed using two methods, Sequential Feature Selector (SFS) and Recursive Feature Elimination (RFE), implemented in scikit-learn (version 1.3.0) [41; 42]. SFS is a method of increasing the number of features from a single-feature model to improve accuracy, while RFE is a method of removing less important features from a model of all features to improve accuracy.  
As there were variations in the features selected between each run, feature selection runs were performed 100 times, and the frequency with which each feature was retained was counted. The most frequently retained features were used in the final model. We set the number of features to three, which yielded the highest F1 score.
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 5 Experiment and Analysis
### The Performance Comparison of WFO and WFO without Priority  
When designing the WFO strategy, priority calculation was considered as it can provide significant performance gains. However, it is unnecessary for WFO, and data can be offloaded directly after calculating the groups. Please refer to Figure 10 for more information.  
The experimental environment was set up with a CC bandwidth of 300 M/S, a tolerance delay of 20 ms, a data volume of 300 MB, and a range of node numbers 40-140. The results showed that the WFO delay calculated with priority (represented by red) was lower than the delay without priority calculation (represented by green) and the delay with HPC (represented by orange). Therefore, WFO with priority calculation is an effective method to reduce data offloading delays. When there are 120 nodes, the delay for priority calculation with WFO is 38.5 s, while the delay without priority is 77.03 s. This results in a 50.1% reduction in delay. Using WFO with HPC can maintain performance between WFO  
Figure 9: The delay is influenced by the number of nodes of the three strategies. The rise in FCFS and FQ is evident. WFO can remain stable. (**a**) Bandwidth = 100 & Date = 150; (**b**) Bandwidth = 150 & Date = 150.  
with and without priority, making it necessary to calculate priorities. For delay-insensitive data-offloading tasks, WFO with no priority can be used.
# Fault Diagnosis of Universal Circuit Breakers Based on Variational Mode Decomposition and WOA-DBN
## 2 Experiment Setup and Data Acquisition
### Experimental Dataset
For universal circuit breakers, the closing spring saves energy for the opening spring to use during the closing process. It was discovered that the vibration signal energy was higher during the closing process than during the opening process. Moreover, additional notable failure signs may be seen in the vibration signal characteristics during the closing operation. Therefore, this study collects and analyzes vibration signals, especially when the universal circuit breaker is closed. A total of 100 sets of vibration signals were measured for each fault type using the data acquisition system. Table 2 provides an overview of the data distribution. Figure 3 displays the time-domain vibration signals of the universal circuit breaker under different fault simulation scenarios. These signals exhibit prominent nonlinear and unsteady characteristics. It is worth noting that the vibration signal generation process in universal circuit breakers is brief, typically lasting around 20 ms, as depicted in Figure 3. Despite the short duration, these signals contain significant characteristic information. However, directly analyzing the time-domain signals poses challenges in achieving effective fault diagnosis [35]. Therefore, performing VMD decomposition and feature extraction operations on the vibration signals becomes crucial [36].
# Towards Robust Neural Rankers with Large Language Model: A Contrastive Training Approach
## 3 Methodology
### Main Framework
#### 3.2.3 Query Intent Alignment  
We assume that text representations based on the same query intent should be highly similar. Due to the possibility of user-generated adversarial text in the wild, we need to enhance the defensive capabilities of language models when facing adversarial attacks. After obtaining high-quality query variations generated by ChatGPT, we prefer to use contrastive learning to align various expressions of the same query intent in the latent representation space.  
For Dense Retriever, \(H(q_{i})\) could be directly used as the representation of query \(q_{i}\). For Reranker, we add a BERT encoder layer to obtain the query representation:  
\[v_{q}=Encoder(H_{last}). \tag{9}\]  
We use the underlying BERT as a shared module and design task-related modules on its top layer according to the ranking and alignment task, respectively. Ultimately, we obtain an underlying model that takes multiple task objectives into account through joint training with multi-task learning.  
In the alignment task, the in-batch contrastive learning approach is adopted. For each input query intent \(q_{i}\), \((i\in[1,batchsize])\) within the batch, assuming there are \(K\) query variations \(q_{i}^{k}\), we use the Normalized Temperature-Scaled Cross Entropy [12] as the query intent alignment loss:  
\[\mathcal{L}_{cl}=-\sum_{q_{i}\in\mathcal{Q}}\sum_{j=1}^{K}\log\left(\frac{ \begin{subarray}{c}\mathit{sim}\left(v_{q_{i},p_{i}^{\prime}}\right)/\tau\\ \mathit{estim}\left(v_{q_{i}^{\prime},p_{i}^{\prime}}\right)/\tau\\ \mathit{estim}\left(v_{q_{i}^{\prime},p_{i}^{\prime}}\right)/\tau+\sum\limits_ {q^{\prime}\
otin q_{i}}\mathit{e}^{sim\left(v_{q_{i},p_{i}^{\prime}}\right)/ \tau}\end{subarray}}\right). \tag{10}\]  
Normalized Temperature-Scaled Cross Entropy is a loss function frequently employed in contrastive learning. It is a modified form of the cross-entropy loss, primarily utilized for learning meaningful representations in unlabeled data. While its initial applications were predominantly in image processing [12; 56], it has also found a wide range of applications in the fields of IR [11; 34]. In Equation (10), for any query intent \(q\) in the batch, we assume that the original query representation of \(q\) and its augmented query variation representation should be close to each other in the latent space, while it should be distant from the representations of other query intents within the input batch in the latent space. This alignment task enhances the PLM capability of natural language understanding but also contributes to the performance of the retrieval ranking task. Finally, we add the loss of the retrieval ranking and the query intent alignment task to conduct our contrastive ranking loss and train both Dense Retriever and Reranker:  
\[\mathit{Loss}=\mathcal{L}_{BPR}+\alpha\cdot\mathcal{L}_{cl}, \tag{11}\]  
where \(\alpha\) is proposed to weight the training loss of both tasks.