esa-sceva/satcom-chunk-collection huggingface dataset:
# A C-BiLSTM Approach to Classify Construction Accident Reports
## 3 C-BiLSTM-Based Classification Framework
### One Dimension Convolutional Layer  
The local features of a text can be extracted via CNN, which refers to a kind of feedforward neural network whose model structure mainly includes an input layer, convolutional layer, pooling layer, fully connected layer, and output layer. In C-BiLSTM Model, a single convolutional layer is used to reduce data dimensions and extract serial information. Its structure is shown in Figure 3. A max-over-pooling or dynamic k-max pooling is generally used to choose the most important or k-most important features after the convolution. However, the input to BiLSTM must be a serialized structure, and pooling will destroy the sequence organization of the text. Therefore, data after convolution operation will not undergo pooling operation anymore.  
Take \(X\in R^{L\times d}\), the text representation after word embedding, as input and the word vector of each word in the text is \(x_{i}\in R^{d}\) where L is the max length of the text and d is the dimension of word vector (the dimensionality in this study is 300). Convolution is mainly used for feature extraction. The features of input text can be extracted by sliding on input sequence via filter \(m\in R^{b\times d}\) (k is the length of filter). In every position i in the sentence, there is a window vector \(\text{w}_{\text{i}}\) that contains k continuous word vectors \((x_{i},\ x_{i+1},\ldots,x_{i+k-1})\). Eigenvalue \(\text{c}_{\text{i}}\) is obtained by convoluting filter m and window vector \(\text{w}_{\text{i}}\) and its calculation process is shown in Equation (4).  
\[c_{i}=f(m\cdot w_{i}+b) \tag{4}\]  
where \(b\) is bias term, the value of which can be adjusted in the training process; \(f(.)\) represents the nonlinear activation function, rectified linear units (ReLU). ReLU has better performance than other activation functions in terms of iterations required for reducing network convergence. By convoluting every window vector in the text, the feature sequence \(C\ =\ (c_{1},c_{2},\ldots,c_{L-k+1})\) can be obtained for the  
Figure 3: The architecture of the convolution operation.  
text. 128 filters in the same size are used in C-BiLSTM to acquire several feature sequences. Therefore, data will become a new feature representation O after the convolution layer, as shown below.  
\[O=(C_{1};C_{2};\dots;C_{128}\ ) \tag{5}\]  
To be specific, semicolon stands for column vector connection and new features representation will be fed into BiLSTM as input.
## 4 Discussion
Extrinsic Disturbances Affecting Changes in the Synergistic Relationship between the URB's Hydrological Processes and EQ  
In the context of the URB warming and humidifying, EQ was dominated by a non-significant increase from 2001 to 2019. Even under supportive conditions for vegetation growth, EQ degradation still occurred in the southern and northwestern parts of the downstream region of the URB. Moreover, the results of this study showed that the correlation between the URB's hydrological process factors and EQ was relatively high until 2012 and decreases afterward. This suggests that climatic and hydrological conditions are not the only forces affecting improved EQ in this inland river basin and that external drivers may be behind it as well. The rapid economic development and continuous population increase led to urban expansion and increased industrial and mineral land use, and road construction generates ecological problems such as increased water consumption and the destruction of grasslands [42]. This study collected data about population and socioeconomic indicators (primary, secondary, and tertiary industries) from the statistical annals of the URB, during 2001-2019, in an attempt to explore the link between the hydrological process factors and EQ (Figure 12) and found that the population and socioeconomic indicators in the URB showed an increasing trend. In particular, the secondary sector surged in 2010 and reached its peak in 2012. This might have been the main reason for the slight decrease in EQ and increase in the correlation with the URB's hydrological process factors after 2012. On the one hand, the construction of hydraulic reservoirs and industrial mines has contributed to the socioeconomic development of the region; on the other hand, the construction of factories and reservoirs has cut off the main streams of the river basin and occupied a large area of grassland, increasing the conflict between man and nature [15]. To achieve sustainable ecological development, the Inner Mongolia Autonomous Region has taken corresponding ecological restoration measures, such as the Beijing-Tianjin Sand Source Control Project, the Natural Forest Protection Project, and the policy of Returning Grazing Land to Grassland Project, mainly by reducing livestock and resting grazing [43; 44]. As seen in Figures 12b and 13, the trends of gradual reduction of total livestock, reduction of arable land area, and subsequent increase of grassland, as well as forest land area, from 2001 to 2019 confirm the effective implementation of this series of measures. This has improved the carrying capacity of grasslands to some extent, by reducing the consumption of water resources and the burden on grasslands [45]. Ecological protection measures taken by the government have accelerated EQ improvement, as well as soil and water conservation, in the URB. Figure 12: Economic indicators (**a**) and population (**b**) of the URB.
## 6 Fuzzy Based Adaptive Resource Allocation
### FRB Enhanced Class-based Policy-Driven Resource Control
Similarly, the Class 2 and 3 input variables expressed by min\(\{\mathrm{b_{2}}/\mathrm{B_{T}},\,\mathrm{B_{2}}/\mathrm{B_{T}}\}\) and min\(\{\mathrm{b_{3}}/\mathrm{B_{T}},\,\mathrm{B_{3}}/\mathrm{B_{T}}\}\) are _fuzzified_ with identical membership functions and parameters as Class 1 with universe of discourse given by [0, \(\mathrm{B_{2}}/\mathrm{B_{T}}\)] and [0, \(\mathrm{B_{3}}/\mathrm{B_{T}}\)] respectively. There are three output variables from the FRB, each indicating the current level of allocated bandwidth per class. The variables are the _Class1 Res_, _Class2 Res_, and _Class3 Res_ which denote the level of resource allocation per class. The variables have identical fuzzy sets as follows:1. _Light Allocation (L4),_ modeled by a Gaussian membership function with parameters mean \(\beta=0\), and variance \(\sigma=0.17\). 2. _Medium Allocation (MA),_ modeled by a Gaussian membership function with parameters mean \(\beta=0.5\), and variance \(\sigma=0.13\). 3. _High Allocation (HA),_ also modeled by a Gaussian membership function with parameters mean \(\beta=1\), and variance \(\sigma=0.17\). The Gaussian membership function is given by:  
\[f(x)=\exp\left[  
Note that the FRB input membership functions are \"scaled\" and \"shaped\" by the policy allocations of B\({}_{1}\), B\({}_{2}\), and B\({}_{3}\) (_i.e._, the FRB system is dynamically adjusted with policy re-configuration). This design loosely couples the high level resource allocation polices and the fuzzy rule-base system implemented in the RB, so that either one can be independently modified. With three inputs variables each having three membership functions, our current FRB system implementation within CNQF prototype has 27 rules. The Z\({}^{\text{th}}\) rule is expressed as follows:  
\(R^{\text{'}}\): If Class 1 load is \(F_{1}^{z}\) and Class 2 load is \(F_{2}^{z}\) and Class3 load is \(F_{3}^{z}\) THEN Class1 Res, Class2 Res, Class3 Res = \(y_{1}\), \(y_{2}\), \(y_{3}\); where \(F_{1}\),\(F_{2}\),\(F_{3}=\{\)_LL, ML, HL\(\}\), \(y_{1}\), \(y_{2}\), \(y_{3}=\{\)_LA, MA, HA\(\}\)_ and Z = 1,..., 27. Figure 7:1 Sample output Resource allocation surface map for Class 1 with varying Class 2 and Class 1 network loads. Figure 7:2 Sample output Resource allocation surface map for Class 1 with varying Class 2 and Class 3 network loads.
# FORCE--Landsat + Sentinel-2 Analysis Ready Data and Beyond
## 3 Processing Capability
### Higher Level: Highly Analysis Ready Data
#### 3.3.5 Data Fusion  
FORCE ImproPhe (Improving the spatial resolution of land surface Phenology) increases the spatial resolution of coarse continuous fields (example: Figure 12). It was originally developed to increase the spatial resolution of coarse resolution MODIS phenometrics, using Landsat ARD as multi-temporal prediction targets [25]. The fusion intensively uses the information from the local pixel neighborhood at both resolutions, wherein sparser medium resolution data are used to disentangle the land surface phenology by employing textural and spectral homogeneity metrics. ImproPhe is useful (i) in areas or times when Landsat/Sentinel-2 data are not dense enough to derive land surface phenology directly, and (ii) in areas where inter-annual climate variation prevents the strategy of  
Figure 11: Land surface phenology-based trend and change analysis for Crete, Greece. The change, aftereffect, trend (CAT) transformation shows both long-term (30+ years) gradual, and abrupt changes. The CAT transform was applied to the annual value of base-level phenometric time series, which was itself derived by inferring land surface phenology metrics from dense time series of green vegetation abundance, derived from spectral mixture analysis (SMA) of Landsat ARD.  
pooling multiple years to increase data density. In general, ImproPhe can be applied to any coarse continuous field, provided a link to spectral-temporal land surface processes exists.  
FORCE Level 2 ImproPhe (L2 IMP) is capable of improving the spatial resolution of lower-resolution ARD using higher-resolution ARD, e.g., refining Landsat images with Sentinel-2 targets (example: Figure 13). Although this function produces Level 2 data (Figure 2), the general higher-level concept (3.3.1) also applies. The higher-resolution ARD are condensed to seasonal windows, and the ImproPhe code is applied to each lower-resolution ARD dataset. The refined dataset is appended to the original dataset as a separate product; thus two surface reflectance versions are available for each date. The higher-level FORCE modules can digest this data structure, and the user can choose to use the original BOA or the refined product.  
Figure 12: Land surface phenology metrics at coarse resolution (MODIS-derived, 500 m) and with improved spatial resolution at 30 m for an image subset in Brandenburg, Germany. Depicted are (rate of maximum rise, integral of green season, and value of early minimum in RGB). Using the FORCE ImproPhe module, the spatial resolution was enhanced using multi-temporal Landsat and Sentinel-2 A/B prediction targets.
## 5 Conclusions  
The finite element software ANSYS was used to simulate a unidirectional horizontal loading test of an infill wall constrained by BLC-C composite walls on both sides. The finite element model was validated through experimentation and the main conclusions are as follows.  
1. Within the elastic story drift limit, the experiment process was divided into an elastic stage and an elastoplastic stage. In the elastic stage, the BLC-C composite wall and the infill wall acted as the lateral force-resistant members. In the elastoplastic stage, the BLC-C composite wall became the main lateral force-resistant member.
2. The load-displacement curves and crack distribution images obtained from the finite element simulation were similar to the test results. Therefore, the finite element model proposed in this study could be used to accurately simulate an actual deformation process.
3. A decrease in the width of the BLC-C composite wall or an increase in the width of the infill wall delayed the appearance of interface cracks.
4. A large opening delayed the appearance of interface cracks.
5. An enhancement in the strength grade of the bricks led to the earlier appearance of interface cracks.
6. When the maximum normal contact stress rose, the interface cracks occurred later. Therefore, in order to delay the appearance of interface cracks, it is recommended that the tensile bond strength of the mortar and the steel plate in the connection structure of the interface are reinforced.  
The out-of-plane behavior of the specimens was also of importance. However, a lateral support was used in this test to prevent the out-of-plane behavior of the specimen. Hence, this paper did not address the out-of-plane behavior of the specimen. Our research group aims to research this further in the future.  
Conceptualization, P.C. and L.Z.; methodology, L.Z.; software, P.C.; validation, X.Q.; formal analysis, P.C.; resources, S.R.; data curation, G.L.; writing--original draft preparation, P.C.; writing--review and editing, P.C.; supervision, K.Q.; project administration, X.Q. All authors have read and agreed to the published version of the manuscript.  
Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.
## 2 Materials and Methods
### Measurement of Spatiotemporal Gait Parameters  
Spatiotemporal gait parameters were assessed using the Optogait(r) optical system (Microgate Corporation, Bolzano, Italy) and its accompanying software (version 1.9.7.0, Microgate Corporation, Bolzano, Italy). The Optogait(r) optical system consists of transmitting and receiving bars positioned parallel to each other at a distance of 1 m. As participants moved between the transmitting and receiving bars, the system detected interruptions in the optical signal and automatically calculated spatiotemporal gait parameters based on the presence of a foot within the recording area. Previous studies have confirmed the high validity of the Optogait(r) optical system for assessing spatiotemporal gait parameters in older adults [13]. To minimize the impact of training, all participants wore comfortable shoes during the gait ability test, which was repeated three times in random order. The following gait parameters were measured: gait speed, gait speed variability, stride length, stride length variability, double-limb stance, and double-limb stance variability.
## 4 Numerical Examples
For passive TMD, the optimum results of the cases with st_max = 3 and 4 were found as the same since the maximum g\({}_{1}\) value is 2.0755 which is not at the limit for both cases. This situation means that the stroke limitation for passive TMD is not a restriction if the st_max value is bigger than 2.0755. As given in the optimum results, the optimum mass of TMD and ATMD are at the maximum limit. It is a known issue and the proposed algorithm shows the efficiency by finding the mass at the upper bound. It is seen that optimum periods are decreasing, and optimum damping ratios are increasing by the reduction of stroke capacity value, st_max. The controller parameters show very different values with respect to cases of st_max, because all of these gain variables of PID controller formulation can use different sets of values to generate similar control signals in active control. When the design constraint (g\({}_{1}\)) values are inspected, it can be seen that passive TMD has a maximum value of 2.0755 instead of reaching the limit of st_max. ATMD needs more stroke capacity than TMD, and the maximum value is 3.2983 for the case with st_max = 4. It is understood that the maximum stroke needed for TMD is that value. The critical excitation considered for the objective function value is the BOL090 component of the Duzce (Turkey) record of the earthquake. Under this excitation, the maximum displacement of the upper story is 0.41 m for the uncontrolled structure. By using a TMD with a maximum mass of 5% of the superstructure, it is reduced to 0.2802 m, while it is possible to reduce it down to 0.1897 m. This situation can also be clearly seen from the time history plots of the critical excitation (Figure 5). When the plots given for all st_max are inspected, it is clearly seen that TMD and ATMD are also effective in obtaining a quick steady state response, since vibrations are damped quickly. This is resulting from the optimum high damping values of ATMD cases. The optimum ATMD for all cases of st_max is also effective on all 44 excitations. The maximum top story responses are given for all excitations in Table 4 and the results are also plotted in Figure 6. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & \multicolumn{2}{c}{**TMD**} & \multicolumn{2}{c}{**ATMD**} \\ \hline st\_max & 2 & 3 and 4 & 2 & 3 & 4 \\ \hline m\({}_{\text{d}}\)(t) & 180 & 180 & 180 & 180 & 180 \\ T\({}_{\text{atmd}}\)(s) & 0.9418 & 0.9434 & 0.8947 & 0.9941 & 1.0797 \\ \(\xi_{\text{d}}\)(\%) & 5.64 & 4.69 & 28.77 & 11.86 & 10.15 \\ K\({}_{\text{P}}\)(Ns/m) & - & - & \(-\)10000 & \(-\)1157.6 & 8762.9 \\ T\({}_{\text{d}}\)(s) & - & - & 67.39 & 599.42 & \(-\)79.12 \\ T\({}_{\text{i}}\)(s) & - & - & 8616 & \(-\)7687.9 & \(-\)2748.1 \\ f(x) (m) & 0.2820 & 0.2803 & 0.2469 & 0.1963 & 0.1898 \\ g\({}_{1}\) & 1.9999 & 2.0755 & 1.9999 & 2.9989 & 3.2983 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The optimum results. Figure 5: Time history displacement of the top story of the structure under the critical record. (**a**) st_max = 2; (**b**) st_max = 3; (**c**) st_max = 4.
## 4 Numerical Simulation of Coal Pillar Width of Gob-Side Entry Driving in Inclined Coal Seams  
### Establishment of the Numerical Model  
According to the mining relationship and the roof and floor strata conditions of the 4602 working face, a numerical calculation model that is 470 m long, 200 m wide, and 200 m high is established (Figure 6). In order to simulate the overburden load, the upper boundary stress of the model is set as 7.5 MPa. The displacement on the left, right, and bottom boundaries is fixed to 0. The lateral pressure coefficient is set as 1.2, and the coal and rock masses are calculated by the Mohr-Coulumb model. The horizontal distance between the #1 coal seam residual pillars and the #2 coal seam is 31 m; therefore, the above calculated stress concentration influence range of residual pillars is 11 m and the range of the internal stress field is 8 m. The reasonable pillar width can be further determined by simulating the stress distribution characteristics of the roadway surrounding the rock with pillar widths of 4 m, 6 m, 8 m, 10 m, 12 m, and 14 m, respectively. The calculation process is as follows: model establishment \(\rightarrow\) original rock stress equilibrium \(\rightarrow\) excavation of the #1 coal \(\rightarrow\) excavation of the 4502 and 4702 working faces in the #2 coal seam \(\rightarrow\) excavation of the 4602 head roadway \(\rightarrow\) calculation result analysis. The mechanical parameters of the coal and rock mass in numerical simulation are obtained by conversion on the basis of laboratory test data. Table 1 gives the mechanical parameters of the coal rock mass.  
Figure 6: Three-dimensional numerical model.
## 4 Experimental Results
### Performance Metrics Summary and Statistical Significance  
Table 6 consolidates the performance metrics across all evaluated models, including accuracy, robustness under distortions, video-level aggregation improvements, calibration quality, and human-in-the-loop error reductions. The results highlight the trade-offs between speed, accuracy, and resource requirements, offering practical guidelines for model selection.  
Statistical significance tests were conducted to ensure that the observed differences in performance are meaningful. Table 7 presents \(p\)-values for paired \(t\)-tests across model accuracy metrics. All comparisons show \(p\)-values below 0.01, confirming statistically significant differences.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 4 Classifiers
### Pnn  
PNN [14] belongs to the family of neural networks. It is sensitive in scenarios when one input feature has a higher value than the other one. The input variables should have a similar scale, otherwise they should be normalized before use. PNN is an efficient algorithm for smaller datasets and classification problems. The Probability Distributed Function (PDF) used in PNN is calculated by the Parzen Window (PW), which is based on Kernel Density Estimation (KDE). In PNN, layers are organized in the multilayered FeedForward Network (FFN) at four levels, which are as follows:  
* Input layer: The first layer is the initiating layer of PNN. It has multiple neurons where each neuron represents a predictor variable. There are \(N\) number of groups, and \(N-1\) neurons are used as categorical variables. Then, the range is standardized by dividing the subtracted median by the interquartile range. These values are used as an input for the next layer of neurons, which is a pattern layer.
* Pattern layer: This is the second layer of PNN. Each case contains a single neuron in the training dataset. The target value and predictor variables are stored together in a case. Then, a private neuron calculates the Euclidean Distance (ED) from the center point of all neurons. This process is repeated for each and every case. After that, ED is used with the radial basis kernel function along with the sigma values.
* Summation layer: The output of the pattern layer is a pattern neuron that is used as an input for the summation layer. In PNN, each category of target variables has one pattern neuron. After that, every hidden neuron stores the actual target category of each training case; these weighted values are streamed in the pattern neurons that are in compliance with the hidden neuron's category. These values are added in pattern neurons for the determined class.
* Output layer: The weighted votes are compared in the output layer. The selection criteria for determining which vote should be used for predicting the target category are based on the comparison of weighted votes for each target class that is calculated in the second layer.