esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Width of Crack  
To determine the maximum crack width, the crack width development in the specimens was measured during the loading process (according to the load classification). The final measurement is shown in Figure 5, where the crack width is the maximum crack width of the specimen at a certain load level. Referring to the code \"Code for Design of Concrete Structures\" [27] (GB 50010-2010) 0.2 mm was taken as the crack width threshold (dashed line in the figure). From Figure 5A-D, it can be seen that the maximum crack width of each specimen increases linearly with the increase in load, and the crack width is less than 0.1 mm when cracking, and the load is 50-100 kN when reaching the threshold value of crack width (0.2 mm) in a normal use limit state. The above indicates that the variation parameters (sea sand replacement rate, longitudinal reinforcement rate, type of mixing water and duration of service) did not have significant effects on the crack width development of the specimens under the tidal environment of 270 d duration. The reason for this may be that the reinforcement of the specimen beams was still protected by the concrete protective layer under the 270 d tidal environment (wet and dry cycles) and was less affected by chloride ion corrosion. The bond between the reinforcement and concrete interface was not damaged, so the crack width development of the specimens tended to be consistent.  
Figure 4: Final failure pattern and fracture distribution.
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 2 Related Work
### AOI Extraction
They presented an overall framework consisting of data, spatio-temporal and semantic layers. From raw data they first built AOI boundaries and then extract its semantics, aiming to provide an appropriate characterization. They used several heuristics within the process and showed that their approach may be applied in various cities with minor tuning. Similarly, [PERSON] et al. [26] aimed to characterize urban spaces and model them to neighborhoods. They proposed a novel algorithm which used Foursquare user checkins and semantic information so as to extract neighborhood boundaries and appropriately characterize them based on the venues they contain, the temporal distribution of checkins within them and the categories of visitors (i.e., tourists or local residents). They recommended the extracted neighborhoods to Twitter users. Likewise, [PERSON] et al. [27] also used Foursquare data, cross-checked with Twitter so as to extract their geo-location. They proposed a spectral clustering algorithm aiming to map a city's neighborhoods and describe their dynamics based on the social flows of people which in the context of this work were referred to as \"livehoods\". They identified three dispersion patterns between these livehoods and typical neighborhoods, namely areas in transition, different demographics within the same area and strong borders. Finally, [PERSON] et al. [28] proposed a fuzzy algorithm that aimed to delineate neighborhoods of urban areas, using volunteered geographic information gathered by collected and annotated POIs from volunteers carrying a GPS device. However, volunteers collected the dataset having in mind the fulfilment of the research goal which is opposed to spontaneously-generated datasets as e.g., in the context of a social network, which of course led to excellent results.
## 2 Methodology
The success of any regularization method is dependent on the correct regularization strength for every iteration and characteristics of the regularization operator. Conventionally Laplacian first derivative operator (LFDO) is used as a stabilizer (\(\mathbf{L}\) in Equation (2)), in the case of a profile retrieval problem, it provides additional information and is a better approximation compared to regularization using the identity matrix [15]. Using a LFDO, this is done by forcing values of the update of adjacent points within a profile to be close. Moreover, it is difficult to develop a mathematical derivation for a nonlinear problem (i.e., RT equation) and RTLS derivation is also based on linear problem. Thus, the \(\mathbf{I}\) matrix is empirically replaced by the \(\mathbf{L}\) matrix in Equation (3) to compensate the effect of nonlinearity. The final form of RTLS at the \(i\)th iteration as:  
\[\mathbf{x}_{i+1}=\mathbf{x}_{i}+\Big{(}\mathbf{K}^{T}\mathbf{K}-\mathbf{g}( \mathbf{x})\mathbf{L}^{T}\mathbf{L}+\boldsymbol{\alpha}^{2}\mathbf{L}^{T} \mathbf{L}\Big{)}^{-1}\mathbf{K}^{T}\Delta\mathbf{y}_{\delta} \tag{4}\]  
It is worth mentioning that the RTLS does not belong to either stochastic or Tikhonov or empirical regularization methods. It is derived from the understanding of quadratic eigenvalue analysis of matrix inversion, which is equivalent to the minimization of the Rayleigh-Quotient equation [33]. For each measurement instance, the optimal regularization strength (\(\boldsymbol{\alpha}\)) is calculated at all iterations to block the nonlinear error injection into the retrieved space, as well as to restrict propagation of other errors as described earlier for all measurement instances. The regularization strength (\(\boldsymbol{\alpha}\)) of the RTLS method is data driven, and is calculated from the residual vector as:  
\[\mathbf{W}=\mathbf{L}^{-T}\Big{(}\mathbf{K}^{T}\mathbf{K}-\mathbf{g}(\mathbf{ x})\mathbf{L}\Big{)}\mathbf{L}^{-1} \tag{5}\]  
The lowest singular value of the matrix \(\mathbf{W}\) has been shown to provide the optimal regularization strength [33]. Since the RTLS method is a dimensionless formulation and keeps uniformity of two separate classes of variables of the present problem, we consider a logarithmic scale for both state space parameters and measurements to produce a unit-less Jacobian without changing the functional property of the problem, i.e.,  
\[\mathbf{K}=\frac{\partial(\log\mathbf{y})}{\partial(\log\mathbf{x})}=\frac{ \mathbf{x}}{\mathbf{y}}\ \frac{\partial\mathbf{y}}{\partial\mathbf{x}} \tag{6}\]
## 3 Future Work
### Prosumer Communities
#### 3.1.1 Prosumer Definitions, Objectives, and Motivations
Field trials are equally critical to evaluate the effectiveness of proposed techniques and methodologies. For example, field trials could utilize MATCH, a real-time algorithm based on the theory of Lyapunov optimization, to assess its functions in the daily operations of the smart grid [23]. Furthermore, as future work, the proposed framework and methodologies in managing PCGs could be tested using a larger dataset in the real world to evaluate system performance in real time [4; 7; 8]. In addition, energy management strategies could take into account the technology aspect. For example, in the future, a prosumer energy management system with central storage and fair priority allocation could be further examined for improved peak load management and system reliability [24].
## 3 Results and Discussion
### Photocatalytic Studies  
#### 3.7.1 The Effect of Catalyst Dosage  
The photocatalytic activity of ZnO NPs was estimated by measuring the reduction of paper mill aqueous solution under solar light irradiations. The experiments for the effect of catalyst dosage were carried out three times, each time using the same catalyst (\(n=3\)). The effect of catalyst dosage on the % of COD reduction by ZnO NPs reveals the maximum COD reduction in paper mill effluent studied, as illustrated in Figure 7, by comparison with the majority previous studies, which have looked on the impact of catalyst loading on photocatalytic efficiency [28]. These findings showed that when the number of catalysts loaded rises, the photodegradation rate increases until it reaches an optimum dose (500 mg/L as shown in Figure 7). The reason for this may be because increasing the catalyst dosage increases the total surface area of the catalyst, the active surface area, and the number of reaction sites. As a result, the quantity of hydroxyl and superoxide radicals rose, making the degradation of organic pollutants easier. Hence, the percentage of degradation was increased. Because of the effects of light scattering and screening, the percentage of photodegradation decreases with greater loadings (>500 mg/L) when the photocatalyst dosage is higher than the optimum level. Furthermore, high catalyst dosage increases agglomeration (particle-particle interaction), limiting photo catalytic efficiency by reducing the amount of catalyst surface area available for light absorption and pollutant adsorption.  
The shape, surface area, and crystallinity of a material are all known to play a role in its photocatalytic activity [29]. The photocatalytic activity of a material can be increased by increasing its surface area and crystallinity. Nonetheless, when the calcination temperature increases, the crystallinity of the material increases while the surface area of the material decreases. As a result, morphology could be a major determinant of the final degree-radiation efficiency. Sphere-shaped ZnO nanoparticles (Figure 4a) had a high removal efficiency, according to the findings. [PERSON] et al. [30] found similar findings.
###### Abstract  
The conventional channel estimation methods based on a preamble for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems in mobile-to-mobile sensor networks are inefficient. By utilizing the intrinsicsparsity of wireless channels, channel estimation is researched as a compressive sensing (CS) problem to improve the estimation performance. In this paper, an AdaptiveRegularized Compressive Sampling Matching Pursuit (ARCoSaMP) algorithm is proposed. Unlike anterior greedy algorithms, the new algorithm can achieve the accuracy of reconstruction by choosing the support set adaptively, and exploiting the regularization process, which realizes the second selecting of atoms in the support set although the sparsity of the channel is unknown. Simulation results show that CS-based methods obtain significant channel estimation performance improvement compared to that of conventional preamble-based methods. The proposed ARCoSaMP algorithm outperforms the conventional sparse adaptive matching pursuit (SAMP) algorithm. ARCoSaMP provides even more interesting results than the mostadvanced greedy compressive sampling matching pursuit (CoSaMP) algorithm without a prior sparse knowledge of the channel.  
FBMC/OQAM; channel estimation; compressive sensing; sparse adaptive; greedy algorithm +
Footnote †: journal: Sensors
# Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification
## 4 Experiments
### Experimental Results
#### 4.4.3 Effects of the Length of Document  
To investigate the impact on performance of attention-based hierarchical neural networks over documents with different number of sentences, we design the experiment according to the number of sentences and then compare the performance of H-LAN and HAN on groups of documents with various sentence numbers. Figure 6 depicts the classification accuracy of both models on IMDB and Yelp2013 datasets, respectively.  
From Figure 6a, we can observe that the accuracy of these two models presents a sharp upward trend when the number of sentences changes from (0, 5] to (15, 20]. As documents with the sentence number from (15, 20] to (25, 30], both models show a stable descending trend on IMDB dataset. The main reason for this result is that, fewer sentences usually contain less valuable information contributing to the orientation of sentiment analysis and documents with a large number of sentences contain more complex correlation between sentences so that it is difficult to learn a high-quality document representation from large-scale unstructured text. In particular, compared with HAN, our H-LAN model has a relatively small improvement when the number of sentences exceeds 20, which potentially demonstrates that H-LAN has a robust receptive field on the text sequences. As shown in Figure 6b, it is interesting to find the similar uptrend until the number of sentences arrives at (5, 10]. However, the trends in the accuracy rate of all discussed models show a significant fluctuation when performing on documents with sentence number from (5, 10] to (20, 25]. The reason is that too long documents contain too many noisy words, so that it can greatly mislead the prediction of fine-grained text sentiment classification. After that, H-LAN and HAN present a steady downward trend in terms  
Figure 5: Classification accuracy of the proposed LAN model on two sentence-level sentiment classification datasets. (**a**) MR; (**b**) SST. When the dimension of sentiment resource words is equal to 0, it means that the lexicon-enhanced attention mechanism is not equipped with the word embedding module.  
of accuracy before the number of sentences reaches (25, 30). Specifically, comparing with H-LAN and HAN, it can be observed that H-LAN improves the accuracy of HAN by a large margin. This strongly proves that our H-LAN model has a powerful receptive ability to alleviate the impact of gradient vanishing [51] caused by the architecture of recurrent neural network. To sum up the above observation, we can conclude that the proposed H-LAN can effectively retain the semantic structure of the raw document to improve the performance of sentiment analysis.
## 3 System Flexibility Digital Representation
### Data Modelling and Data Analysis
#### 3.3.3 AHP-Based System Flexibility Static Evaluation Index  
The digital twin flexibility evaluation of production system design scheme (PSDS) is designed to obtain the flexibility evaluation quantitative index. The flexibility static evaluation index can be obtained through expert scoring and AHP method combined with the analysis of digital twin static functional model. The production system scheme designed for the demand of the enterprise has different characteristic attributes, and the staffs have different production tasks in the production enterprise have different subjective requirements for system flexibility. The expert score and AHP method are combined to obtain the enterprise's weight for the flexibility of product, process, and machine in the system static function model.  
To obtain flexibility weight, the judgment matrix needs to be constructed first. Judgment matrix is mainly considering production enterprise's subjective demand for flexibility in the design phase. Invite enterprise staffs (e.g., process engineering, production manager, etc.) to grade the relative importance of three-dimensional flexibility with their own understanding of the enterprise acquirements on the basis of the score standards in Table 2. The mean value of the scoring results is obtained to obtain the positive reciprocal matrix A.  
\[\text{A}=\left(a_{xy}\right)_{3\times 3}\quad\forall x,y\in[1,3] \tag{24}\]  
where, \(a_{xy}\) represents the importance of a dimension compared with another dimension in the factors affecting the flexibility of the production system. For the judgment matrix, combined with the normalization processing method, the flexibility weight of each dimension is expressed.  
\[W=\left(W_{1},W_{2},W_{3}\right)^{T}=\sum_{y=1}^{3}\overline{W_{xy}}/\sum_{x= 1}^{3}\sum_{y=1}^{3}\overline{W_{xy}} \tag{25}\]  
Figure 5: Structure diagram of dynamic prediction model.  
\[\overline{W}_{xy}=a_{xy}/\sum_{x=1}^{3}a_{xy} \tag{26}\]  
where, \(W_{1},W_{2}\) and \(W_{3}\) are the weights of the influence factors for product dimension flexibility, process dimension flexibility, and machine dimension flexibility respectively. The flexibility static evaluation index \(FL\) of the production line is obtained by using linear weighting:  
\[FL=W_{1},W_{2},W_{3}\times(F_{Pd},F_{Fc},F_{RM})^{T}=W_{1}\times F_{Pd}+W_{2} \times F_{Fc}+W_{3}\times F_{RM} \tag{27}\]
## 3 The FQS Algorithm
### Correctness and Complexity Analysis
#### 3.3.2 Complexity Analysis  
Section 3.1 provided details on computing the expected maximal shift position and showed the time complexity of Algorithm 3 to be in \(O(m+|\Sigma|)\). The other two preprocessing steps compute the shift arrays using the bad character rule, hence the time required for these two steps are also in \(O(m+|\Sigma|)\) according to the QS algorithm.  
In the searching phase, the FQS algorithm integrates a pre-testing stage with the QS algorithm. The time complexity of one pre-test is constant, and the total time complexity is in \(\Theta(n)\). The worst-case time complexity for searching phase in the FQS is \(O(mn)\), and the average time complexity is \(O(n)\). The extra space required by the FQS is in \(O(|\Sigma|)\). The FQS algorithm has the same worst-case and average-case time and space requirements as the QS algorithm. As with the general BM algorithm, the worst case complexity can be improved to \(O(n+m+|\Sigma|)\) using the good suffix heuristic with memorization [2; 19].
## 4 Principle and Operation of the Metamaterials
### Programmable Metasurfaces  
Once a unit cell is designed in the early stages of metamaterial and metasurface research, its function is fixed. For example, an absorber functions at a specific frequency where the input impedance is matched to the free space. However, due to the intrinsic structure of the unit cells, re-design and re-fabrication operations are not allowed if there is a change in the working frequency or even the functionality [82]. Adding \"tuning\" capabilities to the unit cells can alter the properties of metamaterials and metasurfaces. The behavior of their electromagnetic waves can then be modified externally by changing the stimulation. The resonance frequency can be shifted by changing the parameters of the substrate or structural material. For example, the permittivity of the liquid crystal can be modified under different gate voltages [83].  
Furthermore, a computer algorithm can control such tuning. For developing programmable metasurfaces, there are a variety of tuning procedures available in the literature [84]. The solutions are classified as global tuning at the metasurface level such as electric, magnetic, light, and thermal tuning and local tuning at unit cell levels such as switch diodes, continuous tuning varactors, and collective tuning varactors. Such metasurfaces are programmable, and they open up more possibilities for dynamical wave applications without the need for re-fabrication or re-design. In this paper, we have directed our research about software-defined metasurfaces, which have the capacity to tune the characteristics of each RIS unit cell separately.
# PPDD: Egocentric Crack Segmentation in the Port Pavement with Deep Learning-Based Methods
## 4 Experiments
### Experimental Results
#### 4.2.1 Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**(\%)** & **RC** & **LEC** & **CSSC** & **RD** & **CJC** & **AC** & **Background** & **Total** \\ \hline Mask2 Former-r101 & & & & & & & & & \\ PA & 48.71 & 53.9 & 42.5 & 69.33 & 60.33 & 64.39 & 97.46 & 62.37 \\ mIoU & 33.8 & 38.7 & 29.13 & 36.79 & 40.31 & 54.62 & 95.42 & 46.96 \\ \hline Mask2 Former-swin-l & & & & & & & & \\ PA & 48.45 & 56.03 & 40.67 & 64.75 & 62.9 & 65.84 & 97.98 & 62.37 \\ mIoU & 34.36 & 41.82 & 34.92 & 39.89 & 41.76 & 56.19 & 95.90 & 49.26 \\ \hline SegFormer & & & & & & & & \\ PA & 40.84 & 58.64 & 53.92 & 73.10 & 58.17 & 81.96 & 99.10 & 66.53 \\ mIoU & 35.13 & 50.74 & 48.31 & 62.27 & 49.19 & 72.55 & 97.52 & 59.39 \\ \hline DeepLabv3+ & & & & & & & & \\ PA & 39.76 & 48.67 & 36.29 & 46.54 & 46.45 & 68.38 & 98.70 & 54.97 \\ mIoU & 31.95 & 38.14 & 27.05 & 37.86 & 36.93 & 57.18 & 96.19 & 46.47 \\ \hline \hline \end{tabular} Performance metrics are expressed as percentages and rounded to two decimal places, RC: reflection cracking, IEC: longitudinal–edge cracking, CSSC: corrugation, shoving, and slippage cracking, RD: rutting and depression, CJC: construction joint cracking, AC: alligator cracking.  
\end{table} 
Table 2: Quantitative evaluation results for each class and overall performance in semantic segmentation.
## 3 Results and Discussion  
### Proximal Composition of Stickwater  
The proximal chemical composition of stickwater is shown in (Table 1). In the present study, 9% of solids, 0.8% of fat, 54.85% of protein, 53.84% of moisture, and 7.21% of ash (dry basis) were obtained in the treatment without ultrasonic pulses.  
Submitting stickwater to ultrasound pulses modified protein and solid values (74.81 and 6.45, respectively). [PERSON] (1997) reported 8-10% of total solids, 5.6% protein, 0.6% fat, 1.8% ash, and 92% humidity in SW from a flour industry in Ensenada, Baja California. On the other hand, in SW, from a mixture of _Sardinops sagax caerulea_, _Engraulis morstax_, _Cetengraulis mysticetus_, and _Scomber japonicus_, 9.5% total solids, 4.7% protein, 1.7% ash, and 1.8% lipids were obtained [28]. [PERSON] [29] found lower amounts of protein in SW of Monterrey sardine (_Sardinops sagax cerulea_), with 91%, 3%, 5.1%, and 1.4% for moisture, protein, fat, and ash, respectively. According to various authors, protein content may vary depending on capture time, size of the individuals, and treatment after the recovery of the effluent. Additionally, the extraction methodology and the protein-measurement technique (dry or wet basis) can lead to variations in the measurement of proteins [7; 30]. Through centrifugation and acid precipitation with TCA and HCI, other authors separated the insoluble-protein fraction of stickwater from the species above, obtaining its chemical composition on a dry basis. The fraction treated with HCI presented contents of 69.7%, 8.2%, 13.9%, and 4% for protein, ash, fat, and non-protein nitrogen, respectively. In contrast, for the fraction treated with TCA, the reported values were 76.7%, 7.9%, 12.1%, and 3.2% in protein, ash, fat, and non-protein nitrogen, respectively [31].  
The significant variation in the proximal content in this study resulted because the stickwater was subjected to an industrial evaporation process before the sample was obtained, which considerably reduced the amount of moisture. At the same time, components such as protein and ash were concentrated. It should be mentioned that said elimination of water was not enough for the sample to be taken on a dry basis. However, the proximal  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Stickwater** & **Moisture** & **Solids** & **Ashes** & **Protein** & **Fat** \\ \hline Without pulsation & 53.8 \(\pm\) 3.6 & 9.0 \(\pm\) 0.8 & 7.2 \(\pm\) 0.6 & 54.8 \(\pm\) 4.2 & 0.8 \(\pm\) 0.1 \\ Ultrasound-pulsed & 66.0 \(\pm\) 2.3 & 6.4 \(\pm\) 0.3 & 7.2 \(\pm\) 0.6 & 74.8 \(\pm\) 3.8 & 0.2 \(\pm\) 0.0 \\ \hline \end{tabular}
\end{table}
Table 1: Proximal chemical composition of Stickwater.  
composition of SW depends on the species of fish, the time of capture, and physiological conditions [7].