esa-sceva/satcom-chunk-collection huggingface dataset:
# Fully Open-Source Meeting Minutes Generation Tool
## 3 Proposal of Meeting Minutes Generation System
### Information Extraction Function
#### 3.5.2 Keywords Extraction  
The _keyword extraction_ process automatically ranks and extracts the most relevant words from a source text. It determines the ranks based on word correlations, frequencies, and semantic meaning [38]. The _rank-based keyword extraction via unsupervised learning (RaKUn2)_ model is downloaded through GitHub repositories [39]. In this study, _RaKUn2_ is employed due to its ability to deliver fast and accurate results across different keyword datasets [25]. This selection aligns with the findings of our previous work, where various keyword extraction methods were evaluated, and the effectiveness of _RaKUn2_ in diverse scenarios was confirmed [19].  
The keyword extraction divides each sentence from the transcribed text into words and characters called _tokens_. This process is called _tokenization_. The relationship between tokens is determined by the frequency of their co-occurrence. It was calculated based on the frequency of appearance of each token in a sentence. Then, it creates a graph where the tokens are the nodes, and the edges are the co-occurrence frequencies. It organizes the tokens' ranks based on the graph's number of edges. Finally, it performs the post-processing steps to remove the duplicated keyword candidates.
## 1 Introduction
An evaluation of a PM-based TSEB (similar to [38]) showed improvements only under the more extreme advective conditions and over-estimated T/ET under the remaining conditions. Although work related to TSEB-based E and T partitioning has led to many insights, most notably the relationship between canopy transpiration (i.e., T) and VPD, it has relied on ground-based thermal infrared measurements as an input and has only been evaluated at individual sites. Such approaches omit the capable spatial component of TSEB through the utilization of satellite-derived thermal infrared imagery. Spatially distributed ET estimates from TSEB can be produced using the Atmosphere-Land Exchange Inverse model (ALEXI; [52; 53; 54]) and the associated disaggregation technique (DisALEXI; [55; 56]). As with TSEB, the combined ALEXI/DisALEXI modeling framework has been applied in vineyards as part of the GRAPEX project [15; 16; 17; 19; 57; 58; 59; 60]. DisALEXI is also implemented within the OpenET framework on Google Earth Engine, which supplies ET information across 17 western U.S. states in support of water management and decision making [61]. Although ALEXI/DisALEXI produces reliable ET estimates in vineyard settings, there has yet to be a thorough analysis of the individual contributions of E and T to the total ET flux in the context of DisALEXI. Additionally, given the operational capabilities of ALEXI/DisALEXI (OpenET, irrigation decision making) and the increasing importance of individual flux contributions in water-limited agricultural systems requiring irrigation-such as vineyards and almonds orchards--the evaluation of E and T partitioning will be necessary for developing robust applications. This study evaluates the partitioning of E and T within the context of the ALEXI/DisALEXI modeling framework. Analysis was performed over three wine grape vineyards (GRAPEX sites) and three almond orchards (T-REX sites) for the year 2021, using tower measurements of total ET (as defined in [7]) as well as individual E and T estimates that were calculated using the conditional eddy covariance (CEC) approach as described in [26]). We evaluate the original DisALEXI approach based on the Priestley-Taylor formulation of TSEB (DisALEXI-PT) and the iterative convergence process with ALEXI for irrigated vineyards/orchards in the advective climate characterizing the GRAPEX and T-REX sites. We also evaluate a DisALEXI approach based on the Penman-Monteith formulation (DisALEXI-PM), as first suggested by [38]. Due to the advective nature of the study sites and their characteristically barren inter-rows during the growing season, we hypothesize that shifting to a Penman-Monteith-based approach will improve model partitioned estimates of E and T. When applied operationally, the model output will give growers more specific consumptive water use information, subsequently allowing for improved irrigation and water use efficiencies in these valuable water-limited agricultural systems.
## 2 System Specification
### Overall System Description
Conversely, when the node needs more neighbors, the transmission power is increased slowly, approaching the accurate value for the transmission power little by little and avoiding energy wasting. The system in Figure 1 complies with the MAPE-K [16] architecture, and three modules stand out:  
* Monitoring: This receives as input the current state of the node and calculates the error variables. * Reasoner: This executes the decision-making function. * Actuator: This accomplishes the desired changes in the node. Each of those modules are split into several tasks:  
* _TM1_ : This calculates the node targeted number of neighbors \(ND_{R}\) by adding the WSAN optimal value \(\overline{ND}\) and the change to be applied \(\Delta nd\) estimated by \(TR2\), based on the battery level. Then, it samples the real number of neighbors \(ND\) using the node run-time environment, calculates the difference (\(e_{ND}\)) between the real and the targeted number of neighbors and decides if the \(TR1\) must be triggered. * _TM2_: This samples the node's real battery level \(E\) using the node run-time environment and computes the error \(e_{E}\) as the difference between the critical battery level \(\overline{E_{cr}}\) and the real battery level and then triggers \(TR2\). * _TR1_: This decides the change to be applied to the node communication range by means of its transmission power, so that the real and the targeted number of neighbors of the node become equal or quite similar. The \(TR1\) decision is driven by FDM1. * _TR2_: This decides the change to be applied to the WSAN optimal number of neighbors \(\overline{ND}\), thus influencing _TM1_. The decision-making process is driven by FDM2. * _TA1_: This acts on the communication range by effectively establishing the node transmission power based on _TR1_ output. Figure 2: Fuzzy transfer functions. (**a**) FDM1; (**b**) FDM2.
# Multivariate Regression Analysis for Identifying Key Drivers of Harmful Algal Bloom in Lake Erie
## 2 Materials and Methods
### Coefficients of Determination  
Coefficients of determination (\(R^{2}\)) indicate how well the predictive value explains the measured value. In this work, measured and predicted Chl-a (and TSS) concentrations were taken as the dependent and independent variables, respectively, and the \(R^{2}\) was determined by applying linear regression analysis. The \(R^{2}\) ranges from 0 to 1; the closer the value is to 1, the better the independent variable explains the dependent variable, meaning the higher the prediction accuracy. The formula for \(R^{2}\) is as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}}{\sum_{i=1}^{n}(y_{i}- \overline{y})^{2}} \tag{3}\]  
where \(y_{i}\) is the ith actual value, \(\hat{y}\) is the ith predicted value of the dependent variable, and \(\overline{y}\) is the mean of \(y_{i}\). The adjusted \(R^{2}\), which is defined as the corrected value of \(R^{2}\) for sample size and regression coefficients, is a better parameter than \(R^{2}\) itself. The adjusted \(R^{2}\) is always less than \(R^{2}\). A higher adjusted \(R^{2}\) generally represents a better model but is not always correct and should be used with caution to assess the model. There is no cutoff point of \(R^{2}\) for the appropriate model selection. \(R^{2}\) should be evaluated based on the field data types, data transformations, or subject area decisions [61].
## 2 Mathematical Model
### Objective Function
#### 2.1.4 Required Costs  
The cost of vehicle-to-grid (V2G) power consists of three components: purchased energy, wear, and capital costs. The purchased energy and wear costs for V2G are additional expenses required specifically for V2G, not for driving. Similarly, the capital cost represents the expense of additional equipment necessary for V2G, distinct from the primary function of vehicles, which is transportation. In addition to the cost of V2G power, this section also models the cost of purchased energy for driving purposes [39].  
1. Capital cost of parking lot: \[CF_{cap(i)}=C_{ac}*PC(i)\] (5)  
\(C_{ac}\) is the annualized capital cost for each vehicle. \(PC(i)\) is the capacity of parking lot \((i)\).
2. Cost of purchased energy to charge EVs for driving \[CF_{pu\_driving(i)}=\sum_{d=1}^{t_{u}}\frac{Pr_{off}}{\mu_{conv}}*P_{patch}(i,d)*t(d) \tag{6}\]  
where \(Pr_{off}\) is the market price of electricity at off-peak times, \(\mu_{conv}\) is the efficiency of the inverter, \(P_{patch}(i,d)\) is the required power to charge vehicles from SOC 0 to SOC1 during the duration of \(t(d)\) (which is calculated with Equation (3)), and t(d) it is calculated with Equation (2).  
3. Cost of purchased EV charging energy to charge vehicles for V2G power \[CF_{(pu\_V2G)}(i)=Pr_{pe}*P_{park(i)}*t_{disp}\] (7)  
The value of \(Pr_{pe}\) represents the energy cost purchased and is calculated using the following equation:  
\[Pr_{pe}=\frac{Pr_{off}}{\mu_{conv}}+C_{d} \tag{8}\]  
where \(C_{d}\) is the cost of equipment degradation due to the extra use for V2G, and \(\mu_{conv}\) is the efficiency of the inverter.
# Differential Signalling in Free-Space Optical Communication Systems
## 3 Lossy Channel and Background Noise
### Fog/Smoke Attenuation  
The fog loss is determined based on the visibility (_Vis_) of the channel, which is described by two well-known models of [PERSON] and [PERSON] [28], and is defined as [30]  
\[\textit{Vis}=\frac{17}{\beta_{\lambda}}\bigg{(}\frac{\lambda}{\lambda_{0}} \bigg{)}^{-q}, \tag{10}\]  
where \(\lambda_{0}\) denotes the maximum sensitive wavelength for the human eye, which is normally set to 550 nm (i.e., the green colour). \(\lambda\) is the wavelength of the laser being used and \(\beta_{\lambda}\) denotes the loss coefficient. Based on the [PERSON] model, \(q\) is defined as [31]  
\[q=\left\{\begin{array}{rl}1.6,&\textit{Vis}>50\\ 1.3,&6<\textit{Vis}<50\\ 1.6\times\textit{Vis}+0.34,&1<\textit{Vis}<6\\ \textit{Vis}-0.5,&0.5<\textit{Vis}<0.1\\ 0,&\textit{Vis}<0.1.\end{array}\right. \tag{11}\]  
The relation between the total loss due to the absorption and scattering of light \(\beta_{\lambda}\) and loss due to fog \(h_{\text{fog}}\) is given by the Beer-Lambert law as [31]  
\[h_{fog}=\exp(-\beta_{\lambda}L). \tag{12}\]  
Since, for each individual channel, \(h_{i}\) remains constant; therefore, we have \(\text{Mean}(h_{i})=h_{i}\) and \(\text{Var}(h_{i})=0\). In addition, based on the assumption we made (i.e., \(h_{1}\approx h_{2}\)), the average of DTL from Label (6a) is zero. The total channel coefficient for a DSS based FSO system with geometrical and fog/smoke loss is \(h_{\text{DSS}}=h_{\text{geo}}\times h_{\text{fog}}\). Therefore, using Label (9) and Label (12), the channel coefficient can be obtained for determining the BER expressions, which are summarised in Table 1.
## 3 Analysis of Carrier Phase Recovery Approaches
### Viterbi-Viterbi Carrier Phase Recovery  
As another \(n\)-th power feed-forward carrier phase recovery approach, which is schematically shown in Figure 4, the Viterbi-Viterbi algorithm also calculates the \(n\)-th power of the received symbols to remove the information of the modulated phase. The computed phase data are also summed and averaged over the processing block (with a certain block length). Compared to the block-wise average algorithm, the Viterbi-Viterbi algorithm just treats the extracted phase as the estimated phase for the central symbol in each processing block [31; 32]. The estimated carrier phase in the Viterbi-Viterbi algorithm in the \(n\)-PSK coherent optical transmission systems can be described using the following equation:  
\[\varphi_{VV}\left(k\right)=\frac{1}{n}\text{arg}\left\{\sum_{q=-\left(N_{VV}- 1\right)/2}^{\left(N_{VV}-1\right)/2}x^{n}\left(k+q\right)\right\}, \tag{14}\]  
where \(N_{VV}\) is the block length of the Viterbi-Viterbi algorithm, and should be an odd value of e.g., 1, 3, 5, 7...  
Using the Taylor expansion, the BER floor of the Viterbi-Viterbi carrier phase recovery in the \(n\)-PSK coherent optical communication systems can be assessed analytically, and can be expressed approximately using the following equation:  
\[BER_{floor}^{VV}\approx\frac{1}{\log_{2}n}erfc\left(\frac{\pi}{n\cdot\sqrt{ \frac{N_{VV}^{2}-1}{6N_{VV}}}\cdot\sigma_{T}}\right), \tag{15}\]  
where \(\sigma_{T}^{2}\) is the variance of the total phase noise (or effective phase noise) in the long-haul high-speed \(n\)-PSK optical fiber transmission systems.  
Figure 4: Schematic of Viterbi-Viterbi carrier phase recovery algorithm.  
Figure 3: Schematic of block-wise average carrier phase recovery algorithm.
# Development of Automatic Crack Growth Simulation Program Based on Finite Element Analysis
## 4 Verification of AI-FEM
### Comparison of Natural Crack Growth with AFEA Method including Cracking Transition
#### 4.2.1 Cracking Transition Method  
If the inner surface crack continues to grow in the thickness direction, it is converted into a through-wall crack, and a method for simulating the crack growth at the time of transition and after is needed for accurate soundness evaluation. In the MRP-216 [19] report, crack transition point is defined as when the deepest point of the crack reaches 93% of the pipe thickness, and in the Emc\({}^{2}\) report [14], the crack transition point is defined as the ligament is less than 5-10% of the pipe thickness. In this study, it was defined as when the deepest point of the crack reaches 95% of the pipe thickness.  
For the external crack length after cracking transition, [PERSON] et al. [23] applied the criterion of UK R6, and the relation is as follows Equation (7):  
\[c_{2}=\min\left(t,\ \frac{c_{1}}{4}\right) \tag{7}\]  
where \(t\) is the pipe thickness, \(c_{1}\) is the length of the inner surface crack when the surface crack transitions into a through-wall crack, and \(c_{2}\) is the length of the outer surface crack  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Initial** & **ASME (1.5 yr)** & **AI-FEM (1.5 yr)** & **Difference** \\ \hline \multirow{2}{*}{Case 1} & \(a\) [mm] & 25.15 & 30.48 & 30.23 & 0.82\% \\  & \(l\) [mm] & 50.55 & 62.74 & 60.45 & 3.64\% \\ \hline \multirow{2}{*}{Case 2} & \(a\) [mm] & 25.15 & 33.78 & 33.53 & 0.74\% \\  & \(l\) [mm] & 101.09 & 113.28 & 112.27 & 0.89\% \\ \hline \multirow{2}{*}{Case 3} & \(a\) [mm] & 25.15 & 35.81 & 35.31 & 1.40\% \\  & \(l\) [mm] & 151.38 & 162.31 & 161.54 & 0.47\% \\ \hline \multirow{2}{*}{Case 4} & \(a\) [mm] & 42.16 & 53.34 & 52.58 & 1.42\% \\  & \(l\) [mm] & 84.07 & 105.41 & 102.87 & 2.41\% \\ \hline \multirow{2}{*}{Case 5} & \(a\) [mm] & 42.16 & 61.21 & 60.20 & 1.65\% \\  & \(l\) [mm] & 168.40 & 191.77 & 189.23 & 1.32\% \\ \hline \multirow{2}{*}{Case 6} & \(a\) [mm] & 42.164 & 68.33 & 66.29 & 2.99\% \\  & \(l\) [mm] & 252.48 & 275.08 & 271.02 & 1.48\% \\ \hline \multirow{2}{*}{Case 7} & \(a\) [mm] & 50.55 & 67.31 & 65.53 & 2.64\% \\  & \(l\) [mm] & 101.09 & 128.27 & 123.19 & 3.96\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of SCC growth results.  
after the surface crack transitions into a through-wall crack. In this study, the cracking transition was simulated by applying the criteria of the crack length of R6.
## 4 Discussion
### Limitations  
The current WCMM algorithm assumes manual wheelchair ambulation. For data collected in this study, additional video recordings were available and used to exclude any portion of data in which any form of power assist was used. However, the current WCMM algorithms cannot automatically distinguish between manual ambulation and any form of power support. This will lead to an overestimation of the actual number of pushes and distance covered. To distinguish between manual wheelchair propulsion and being pushed [33], or using any form of power assist, more complex algorithms are required, which are still rare.  
Only one day of WCMM was collected (varying from 2 to 9 h of measurements, normalized to 8 h) due to limitations in sensor recording capacity. The results were normalized to an 8 h period to enable comparison. However, this approach might skew the results of that participant towards his or her activity level of that specific measurement period, when activity had not been constant throughout the day. For a proper representation of movement behavior, a minimum of 2 to 5 consecutive days of continuous monitoring is recommended, with longer durations providing more accurate and reliable patterns [57, 58]. Data collection should include weekdays and weekends to gain a full overview. Such is possible with newer types of IMUs, with lower power consumption and larger battery capacity.  
The data presented in this manuscript are based on 19 participants, selected and recruited for having an active lifestyle. Furthermore, participants with shoulder problems limiting the execution of daily activities were excluded. Therefore, the results are most likely not representative for the general MWU-SCI population, in which the prevalence of shoulder problems is high and for which a much larger sample would be needed, further stratified over age categories, sex, lesion severity, and time since injury. However, with the WCMM methodology described, and the resulting data presented here, the authors aim to encourage researchers and collaborative clinicians to adapt their procedures and not rely on population statistics but monitor individual wheelchair mobility behavior.  
WCMMs only address manual wheelchair ambulation, neither pressure relief nor transfers or other shoulder loading activities of daily life. For such, additional methodologies should be, and are being, developed, to increase the available information, e.g., the number and duration of weight relief lifts or transfers [59], by adding a pressure mat to the measurement equipment. Also, propulsion patterns, as described by [PERSON] 2002 (semi-circular, single loop, double loop, arching) [60], are clinically relevant variables that should be measured in real-life settings. These require, however, an additional sensor on the wrist or forearm and dedicated algorithms for detection. Some attempts have been made to develop algorithms to identify those propulsion patterns from IMU data; however, these are neither published nor available to the scientific community, yet.
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 3 Transmission Line Monitoring Using WSN in Smart Grids  
In this section, we describe a transmission line monitoring scheme, proposing an example architecture of a WSN deployed in miscellaneous places of considered transmission line. Moreover, we develop corresponding scenarios, in which we examine the performance, security, the transmission time and delays in defined conditions, providing concise representation of utilized routing algorithms and packet flows. Through a set of simulations, we analyze gathered results to identify the optimal routing scheme in the existing WSN, in order to minimize the transmission time delay. Using gatheredresults, we try to answer the question, which type of a WSN architecture, along with routing algorithms and security mechanisms, can be used for measuring and monitoring power system phenomena, and is capable of acting as an additional measure of protection, control or operational optimization.
# 2D Semantic Segmentation: Recent Developments and Future Directions
## 4 Key Technologies to Improve the Effect of Semantic Segmentation
### Knowledge Distillation
Task-Driven Knowledge Distillation: To enhance the effectiveness of knowledge distillation in semantic segmentation tasks, prior knowledge of the target task can be incorporated into the distillation process. For instance, task-specific loss functions can be designed to guide the smaller model toward learning more effective feature representations for the target task. 3. Weakly Supervised Knowledge Distillation: High-quality annotation of semantic segmentation data is often time-consuming and expensive. To reduce annotation costs, it is worth investigating the application of knowledge distillation in weakly supervised semantic segmentation tasks. By utilizing weakly labeled data (e.g., image-level labels or edge labels), the performance of lightweight models can be effectively improved. Figure 9: Schematic diagram of knowledge distillation. The first step is to train Net-T; the second step is to distill the knowledge of Net-T to Net-S at high temperature T.  
4. Integration of Model Architecture Search and Knowledge Distillation: To select a more suitable lightweight model, knowledge distillation can be combined with model architecture search (NAS). By automatically searching for the optimal lightweight model structure, the performance of the distilled model can be further enhanced. 5. Online Knowledge Distillation: To reduce the computational cost of the distillation process, online knowledge distillation methods can be explored. Online knowledge distillation performs knowledge distillation of smaller models simultaneously with the training of larger models, thus avoiding additional distillation computation overhead. By updating the smaller model's parameters in real time, knowledge can be effectively transferred, thereby accelerating model convergence. 6. Cross-Model Knowledge Distillation: In practical applications, it may be necessary to transfer knowledge from multiple large-scale models into a single lightweight model. Investigating cross-model knowledge distillation methods can efficiently integrate the knowledge from multiple large-scale models, further improving the performance of the lightweight model. 7. Explainable Knowledge Distillation: Although knowledge distillation can enhance the performance of lightweight models, the distillation process may result in a reduction in model interpretability. To improve model interpretability, it is worth exploring the introduction of explainability constraints into the knowledge distillation process. By constraining the feature representations learned by the lightweight model to have similar explainability to those of the larger model, the model can maintain its performance while exhibiting better interpretability.