esa-sceva/satcom-chunk-collection huggingface dataset:
# Attentional Extractive Summarization +
## 8 Evaluation  
In this section, we show and discuss the results obtained by the systems of the _Attentional Extractive Summarization_ framework (SHA-NN and SHTE) on the CNN/DailyMail and NewsRoom corpora. We also performed comparisons with other extractive and mixed systems. (We considered as mixed systems those that combine extractive and abstractive strategies, either end-to-end or decoupled. )In order to reflect the categorization of the models, we show in the tables the category to which each model belongs. Specifically, these categories are five: _Heuristic_ (simple rules to generate summaries), _Attentional_ (models that fall under our framework for extractive summarization), _Oracle_ (models that require a sentence labeling previously to the training phase), _Reinforcement_ (models that use Reinforcement Learning to optimize ROUGE metrics during training) and _Text generation_ (models that do not use oracles nor reinforcement learning, and are trained for text generation by maximizing the likelihood of each word in the reference summary, given the document and all the previous words in that reference summary). The evaluation of the systems' performance has been carried out by using three variants of the ROUGE measure [17]. Concretely, Rouge-N with unigrams and bigrams (R-1 and R-2) and Rouge-L (R-L) were used. It should be noted that the ROUGE measure is based on ngrams overlapping. Therefore it is adequate when reference and generated summaries are extractive; however, it is no longer as suitable when the reference or the generated summaries are abstractive. In Table 2, the results of our systems and other state-of-the-art systems for the CNN/DailyMail corpus are shown (ECS and PGen are the acronyms for ExConSumm and Pointer-Generator respectively). Our systems obtain similar results to those of Pointer-Gen+Cov [31], CopyCat [32], and SummaRunner [7]. The obtained results are worse in comparison, despite our systems sharing with it the same backbone architecture (Transformer Encoders). This is possibly due to BertSumEXT starts from a very powerful contextualized pre-trained language model [27]. Additionally, it is interesting to observe that the results obtained by our systems are better than those obtained by some Reinforcement Learning based systems such as DQN [13] and similar to Refresh [10]. Therefore, our extractive summarization framework could be used as an alternative to Reinforcement Learning approaches and oracle-based systems. Tables 3 and 4 show the results, in terms of ROUGE, on the NewsRoom corpus. Specifically, Table 3 shows the results on the full test set and Table 4 shows the results on each one of the three test subsets defined by [PERSON] et al. [23]. Each subset makes reference to the extractiveness degree of their summaries, measured in terms of the density metric proposed in [23]. There are 3 different subsets: NR-Ext (subset whose reference summaries have a high density of words that appear in the articles), NR-Mix (subset with a medium density), and NR-Abs (subset whose reference summaries have a low density and, then, it can be considered as abstractive).
## 3 Uniaxial Compression Test and Particle Flow Numerical Simulation  
### Uniaxial Compression Test  
The standard specimens and polyurea-coated specimens were subjected to uniaxial compression loading tests using a universal testing machine; the strain was measured using an extensometer (see Figure 2a). During the uniaxial compression test, displacement-controlled loading was applied at a loading rate of 0.1 mm\(\cdot\)min\({}^{-1}\). The unloading phase began when the strain of the specimen reached 4%, with an unloading rate of 100 N\(\cdot\)s\({}^{-1}\). Figures 2 and 3 illustrate the uniaxial compression test processes for the standard and polyurea-coated specimens, respectively.  
Figure 1: Schematic of the sample preparation process. (**a**) Macroscopic and microscopic morphology of the coal sample (the white dashed lines in the inset represent the boundaries of the coal block’s laminated joints); (**b**) A coal specimen after cutting and polishing; (**c**) Schematic of the polyurea spraying process; (**d**) Coal specimen after polyurea spraying.  
Figure 2: Uniaxial compression test of the standard coal sample. (**a**) Uniaxial compression test of the standard sample; (**b**) Surface cracks after failure of the standard sample; (**c**) Crack distribution inside the standard sample.  
As shown in Figure 2b, after uniaxial compression failure, a primary crack appeared in the middle of the standard specimen. Considering the stress-strain curve of the standard specimen under uniaxial loading (Figure 4), we characterize the failure of the standard specimen as brittle failure, with the maximum stress at failure, \(\sigma_{\rm c}\), being 25 MPa. During the compression process, the coal specimen first undergoes a compaction phase where porosity gradually decreases and the modulus increases [26; 27; 28]. The fitting results show that the elastic modulus of the coal specimen was 2.2 GPa and the Poisson's ratio was 0.25. By using the Mohr-Coulomb criterion to fit the experimental data, we obtained a value of 0.457 for the friction coefficient \(\mu_{0}\) in the elastic stage, and the internal friction angle was 24.593\({}^{\circ}\).  
As shown in Figure 2c, in addition to longitudinal cracks, the coal sample also exhibits a large number of transverse cracks (marked with red dashed lines in Figure 2c). Figure 3b shows the condition where the polyurea-coated specimen was compressed to its limit using a universal testing machine. At this point, the coal specimen was completely pulverized (as shown in Figure 3c). It can be observed that the polyurea coating, which is applied around the coal sample, behaves as a hyperelastic material. Despite the complete pulverization of the coal specimen, the polyurea coating remained intact and crack-free on the surface.  
Figure 4: Stress–strain curve of the standard coal sample under uniaxial loading.  
Figure 3: Uniaxial compression test of the polyurea-coated coal sample. (**a**) Uniaxial compression test; (**b**) Removal of the extensometer and compressing the polyurea-coated coal sample to complete fragmentation; (**c**) Flaky appearance of the polyurea-coated coal sample after fragmentation.
## 2 System Model
### Performance Metrics
#### 2.4.2 Perceptual Evaluation of Speech Quality (PESQ)  
PESQ [23], standardized as ITU-T P.862, provides an objective estimation of speech quality that closely correlates with human perception. Unlike SDR, which evaluates the fidelity of raw signals, PESQ models human auditory characteristics, including frequency masking, time-domain warping, and asymmetric sensitivity to distortions. PESQ scores range from \(-0.5\) to \(4.5\), where higher values indicate better perceptual quality. Since PESQ considers both time-aligned distortions and perceptual loudness variations, it is particularly suited for assessing speech transmitted over lossy channels.
## 3 Results and Discussion
### Rock Integrity Decay Laws Based on Uniaxial Compressive Strength
#### 3.4.1 Decay Index \(\lambda\) under the Different Times of Freeze-thaw Cycles
The change of porosity and the change in the longitudinal wave velocity by a single freeze-thaw cycle are expressed as follows:  
\[\Delta n =n_{N}-n_{0} \tag{7}\] \[\Delta v =v_{N}-v_{0} \tag{8}\]  
For convenience of application, the relationship between \(\Delta n\) and the decay index \(\lambda\) and that between \(\Delta v\) and \(\lambda\) are investigated and illustrated in Figures 5 and 6. It can be seen from the above fitting that there is a good fit in the relationship between decay index \(\lambda\) and the porosity increment as well as in the velocity increment of the longitudinal wave (R\({}^{2}\) > 0.95), in contrast, \(\lambda\) and \(\Delta v\) have a better-fitting relationship (R\({}^{2}\) > 0.98). \[\lambda =-0.0011\Delta n+0.0053\ \ \mathrm{R}^{2}=0.956 \tag{9}\] \[\lambda =2\times 10^{-6}\Delta v+0.0051\ \ \mathrm{R}^{2}=0.985 \tag{10}\]  
Substituting Equation (10) into the decay equation, respectively, using UCS to express the rock integrity, we obtain the following:  
\[\text{UCS}_{\text{N}}=114.8\text{e}^{-0.0041\text{N}} \tag{6}\]0As for fine sandstone, the simple and easily measured variation quantity of velocity of the longitudinal wave can be substituted into Equation (11), and the UCS after certain freeze-thaw cycles can be obtained.
## 3 Wavefront Sensorless AO-RL Environment
### Observation Space  
We utilize the power of the wavefront that propagates through the focal plane to form the observation of the state of the environment. The focal plane is shown on the left in Figure 4. The white circle within the figure indicates the entrance of the single-mode fiber.  
The observable states of the system directly and efficiently relate to the light coupled into the fiber through a reward function calculation, as explained in Section 3.4. We choose to rely on this, rather than using the full Markovian states, which would require access to information about the satellite's angle and atmospheric conditions. In other words, observations rely on the information directly measured about the state of the environment after the wavefront has been propagated through the focal plane and discretized into a sub  
Figure 3: Illustration of a deformable mirror surface and incident beam.  
aperture array of \(n\times n\) pixels, whereas a Markovian state requires complete information about the system to fully describe it, which would introduce high levels of uncertainty since some of the quantities are not directly measurable or measured. The trade-off is the loss of full observability due to the compression of the state for cost and latency. Nonetheless, RL has tools to handle partial observability [53] and our agents can learn an effective policy utilizing the photodetector represented in Figure 4 (right), with each red square representing a pixel of the photodetector.  
In our RL environment, we can choose the dimension of the observation space, which is \(n^{2}\). One might increase the value of \(n\) to increase the dimension of the observation space in order to decrease the partial observability.  
We discretize the focal plane into a sub-aperture array consisting of 2 \(\times\) 2 pixels under quasi-static atmospheric conditions. This setup can be implemented using a fast and relatively cost-effective quadrant photodetector, as illustrated on the right in Figure 4. The use of a low-pixel count detector mitigates the need for slower and more expensive read-out circuits used in infrared cameras, allowing for more light per pixel and reducing noise, which, in turn, improves the speed of the RL algorithm training. However, a 2 \(\times\) 2 sub-aperture array may not suffice for non-static atmospheric conditions due to low-partial observability and, in such cases, the dimension of the observation space can be increased by increasing the value of \(n\).
## 4 Wavelet Transform
### Wavelet Transform Method
#### Discrete Wavelet Transform
\[\phi_{j,k}(t) =\sqrt{2j}\phi\left(2^{j}t-k\right), \tag{29}\] \[w_{j,k}(t) =\sqrt{2j}w\left(2^{j}t-k\right), \tag{30}\]  
The coefficients \(cA_{i}\) and \(cD_{i}\), representing the corresponding coefficients under the wavelet and scaling function bases, respectively, vary with the transformation level, denoted by \(i\). The relationship between the DWT and IDWT coefficients is expressed by the following equation and illustrated by the process shown in Figures 4 and 5, respectively. _DWT (Analysis)_  
The first frequency range of decomposition:  
\[cA_{1}(k) =\sum_{n}g(n-2k)(n-2k)X(n), \tag{31}\] \[cD_{1}(k) =\sum_{n}h(n-2k)(n-2k)X(n), \tag{32}\]  
The second frequency range of decomposition:  
\[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}{\sum_{k}}f(k)\psi^{*}\bigg{(}\frac{k-n}{2 ^{m}}\bigg{)}, \tag{26}\]0 \[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}{\sum_{k}}f(k)\psi^{*}\bigg{(}\frac{k-n}{2 ^{m}}\bigg{)}, \tag{26}\]1  
The \(n\) frequency range of decomposition:  
\[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}{\sum_{k}}f(k)\psi^{*}\bigg{(}\frac{k-n}{2 ^{m}}\bigg{)}, \tag{26}\]2 \[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}{\sum_{k}}f(k)\psi^{*}\bigg{(}\frac{k-n}{2 ^{m}}\bigg{)}, \tag{26}\]3  
_IDWT (Synthesis)_  
The first frequency range of reconstruction:  
\[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}{\sum_{k}}f(k)\psi^{*}\bigg{(}\frac{k-n}{2 ^{m}}\bigg{)}, \tag{26}\]4  
The second frequency range of reconstruction:  
\[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}{\sum_{k}}f(k)\psi^{*}\bigg{(}\frac{k-n}{2 ^{m}}\bigg{)}, \tag{26}\]5  
The \(n\) frequency range of reconstruction:  
\[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}{\sum_{k}}f(k)\psi^{*}\bigg{(}\frac{k-n}{2 ^{m}}\bigg{)}, \tag{26}\]6  
In wavelet analysis, Daubechies wavelets, a class of orthogonal wavelets with compact support, are commonly used and are denoted by db\({}_{\text{N}}\), where N is an integer from 1 to 10. The filter bank consists of a high-pass filter and a low-pass filter. As the number of filter banks increases, the analysis accuracy improves, but the computational cost also rises. Based on the digital signal and frequency operation range of the motor voltage processed in this paper, and considering the computational load and the need for IDWT, this study adopts db45 as the mother-wavelet function. Since the discrete wavelet transform is not effective in directly comparing error variation trends, the energy percentage for each frequency band is computed using the energy percentage Equation (40) to facilitate comparison [7]. \[DWT_{f}(m,n)=\frac{1}{\sqrt{2^{m}}}{\sum_{k}}f(k)\psi^{*}\bigg{(}\frac{k-n}{2 ^{m}}\bigg{)}, \tag{26}\]7
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 5 Proof-of-Concept Evaluation
### Framework Prototype and Validation Example
#### 5.1.1 BESS Model  
The used battery model follows the mechanisms as described in [43]. Thereby, the model is represented by a voltage source (\(E_{bat}\)) in series with a resistance (\(R_{bat}\)) as indicated in Equation (1). The voltage of the battery (\(V_{bat}\)) depends nonlinearly on the SoC of the battery (\(SoC_{bat}\)), and this is derived from the value of \(E_{bat}\) given by Equation (2). The identification of the parameters \(A\) and \(B\) are  
Figure 8: CEMS structure, systems and sub-systems  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Query** & **Description** & **DL Query** \\ \hline \(Q_{I}\) & What is the Enable parameter of a specific HLUC named \(blue\)? & \(Ena\sqcap hasVariable^{-}.HLUC\{blue\}\) \\ \hline \(Q_{II}\) & What is the Priority parameter of a specific HLUC named \(blue\)? & \(Priority\sqcap hasVariable^{-}.HLUC\{blue\}\) \\ \hline \(Q_{III}\) & What are the main functionalities to be provided by an application & \(HLUC\sqcap hasHLUC^{-}.(Application\sqcap EMS)\) \\  & of type EMS? & \\ \hline \(Q_{IV}\) & What are the sub-functionalities provided by a HLUC? & \(PUC\sqcap hasPUC^{-}.(HLUC)\) \\ \hline \(Q_{V}\) & What are the parameters of a specific controller named \(cont\)? & \(ParamCon\sqcap hasVariable^{-}.PUC\{cont\}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Querying the EMS-\(ABox\).  
detailed in [43]. The \(SoC_{bat}\) is estimated by integrating the battery's current (\(I_{bat}\)) on an interval time as shown in Equation (3), where \(Q\) represents the battery's total capacity and \(SoC(0)\) the initial SoC:  
\[V_{bat}=E_{bat}-R_{bat}I_{bat}, \tag{1}\]  
\[E_{bat}=E_{0}-k(\frac{1-SoC_{bat}}{SoC_{bat}})Q+Ae^{-B(1-SoC_{bat})Q}, \tag{2}\]  
\[SoC_{bat}=SoC(0)+\int\frac{I_{bat}}{Q}dt. \tag{3}\]
# Interface Direct Shear Tests on JEZ-1 Mars Regolith Simulant
###### Abstract  
The mechanical behaviors of Martian regolith-structure interfaces are of great significance for the design of rover, development of excavation tools, and construction of infrastructure in Mars exploration. This paper presents an experimental investigation on the properties of a Martian regolith simulant (Jez-1) through one-dimensional oedometer test, direct shear test, and interface direct shear tests between JEZ-1 and steel plates with different roughness. Oedometer result reveals that the compression and swelling indexes of the JEE-1 are quite low, thus it is a less compressible and lower swelling soil. The cohesion and adhesion of JEE-1 are lower than 5 kPa. The values of the internal friction angle range from 39.7\({}^{\circ}\) to 40.6\({}^{\circ}\), and the interface friction angles are 16.7\({}^{\circ}\) to 36.2\({}^{\circ}\) for the smooth and rough interface. Furthermore, the direct shear and interface direct shear results indicate that the interface friction angles are lower than the internal friction angles of JEE-1 and increase close to the internal friction angles with increasing interface roughness.  
Mars Jezero Crater Delta simulant (JEE-1); compressibility; direct shear test; soil-structure interface; friction angle +
Footnote †: journal: Applied Sciences
# Energy-Efficient Wireless Sensor Networks for Precision Agriculture: A Review
## 3 Agriculture-Based Energy-Efficient Schemes in Literature
### Agriculture-Based Power Reduction Techniques
#### 3.1.1 Sleep/Wake Strategy
[75] proposed a WSN for monitoring horticultural yields, which are disseminated among small plotsscattered at a communication distance of 10 km. The air temperature and soil moisture are collected during a 10-week period. The authors adopted the MAC protocol and star topology to conserve the power consumption of the sensor nodes that used the IEEE 802.15.4 standard based on the CC2420 transceiver. The lifetimes of the soil moisture and air temperature sensor nodes are 1024 days and 222 days, respectively, utilizing a battery that delivers 2700 mAh. The _topology protocol_ scheme can be used in agricultural monitoring WSNs to modify the network topology according to the application requirements, thereby reducing the number of active nodes in a network. Some nodes in the network can enter sleep mode to prolong the network lifetime when they are not sensing the same area of interest. [PERSON] and [PERSON] [77] implemented Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) protocols over a different topology of ZigBee WSN (i.e., cluster topology, random topology, star topology with gateway based on two cases; inside and outside) in simulation environments by using the OPNET software (Riverbed, San Francisco, CA, USA). Twenty soil moisture sensor nodes and one gateway node were distributed in a farmland with a dimension of 100 \(\times\) 100 m\({}^{2}\). Each node sends information about the soil moisture to the gateway node through the ZigBee wireless protocol. The gateway node passes the information to the controlling system to control the irrigation valve system. Their study showed that the star topology using DSR protocol reduces the power consumption of the WSN because it has less routing overhead compared with AODV protocol. Table 2 shows the comparison of power reduction technique based on _sleep/wake_ schemes for previous research in agricultural applications. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Power Reduction Scheme**} & \multirow{2}{*}{ \begin{tabular}{c} **Reference** \\ **Example** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Wireless** \\ **Suraging/Shatter** \\ **Lifetime** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Power** \\ **Dainetterfield** \\ **Size** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Connication** \\ **Quantification** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Sensors/Actualtions** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **Application** \\ **Limitations** \\ \end{tabular}
## 2 Materials and Methods
### Experimental Setup
Unfortunately, screws in dental implants are very small and hardly accessible, so the option of installing strain gauges was discarded. As a well-established alternative in dental implant literature, untightening torque after a number of external load cycles can be measured, since torque is proportional to preload according to Equation (3) [9; 28; 29; 31; 32; 33]. Thus, 30 tightening and untightening operations were performed before testing (without applying external load cycles between both operations) in one of each preload cases in order to set the mean value of the loosening torque \(T_{Li}\) referred to as initial untightening torque from now on and its standard deviation \(\sigma\), inherent to the tightening operation [2]. Additionally, few tests were conducted to evaluate the load loss due to creep-relaxation with time; although this phenomenon is not expected to happen with metals at room temperature. Then, the cyclic load was applied to the tested dental implant. Preliminary results indicate that, for the particular implant tested in this work, when self-loosening occurs, 1000 cycles are enough to promote a significant loosening. Thus, in each experimental test, the final untightening torque \(T_{Lf}\) was measured after applying 1000 load cycles, and compared with \(T_{Li}\). Self-loosening was considered to take place if \(T_{Lf}<T_{Li}-2\sigma\). The \(2\sigma\) is used to account for the normal distribution [2] noting that the probability of fulfilling such criterion without occurring self-loosening is only 2.1%. This procedure was repeated in the 30 tests for each of the three initial tightening torques. Furthermore, in order to obtain experimentally the critical external load value that causes self-loosening \(F_{exp}\) and compare it with the theoretical one \(F_{crit}\), the staircase method was used [39]. This procedure is widely used in fatigue testing to obtain the value of the fatigue limit. Accordingly, an initial arbitrary external load \(F\) is applied in the first test; if the screw self-loosens, the external load is decreased by 5N; if it does not, the external load is increased by 5N.
# Application of the Theory of Planned Behavior in Autonomous Vehicle-Pedestrian Interaction
## 5 Data Analysis and Results  
In order to test the hypotheses of the study, SPSS 26 and SmartPLS 3 were applied. SPSS performs parametric and non-parametric statistical techniques in the context of univariate, bivariate, and multivariate analysis. SmartPLS is one of the commonly used software by researchers for investigating hypotheses using SEM analysis using the Ordinary Least Square estimation techniques [55].  
Figure 2: TPB-based AV–Pedestrian Interaction Model.  
Firstly, descriptive statistics (mean, standard deviation, skewness, and kurtosis) were used for demographic variables and scales. Secondly, the authors developed the scale PBQF first time; thus, exploratory factor analysis (EFA) was applied to define the factor. Exploratory Factor Analysis (EFA) is a type of multivariate statistical correlation analysis that can be used to test the validity of variables [56]. This EFA technique is used to figure out how many variables are underneath a single general variable. CFA (confirmatory factor analysis) was applied by the partial least squares structural equation modeling (PLS-SEM) method to test the model of the study. For data analysis, PLS-SEM was used to test the measures and validate the model, as well as to examine the correlations between constructs in the proposed research model [57].  
Table 3 presents descriptive statistics for all the variables under consideration. In order to evaluate the symmetry of the distribution and identify missing values, a descriptive analysis was performed. All of the components' mean scores were in accordance with the normative data. Based on skewness and kurtosis results, both univariate and multivariate normality were investigated. The findings of analyzing the items and dimensions of place attachment indicated no significant deviations from normality.  
Figure 3: Flow Chart for the Survey Process.