esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 2 Related Work
### Deep Learning Models  
Long short-term memory (LSTM), BiLSTM, GRU, and CNN have been widely used in various natural language processing tasks, but each has its advantages and disadvantages. CNN [15] uses convolutional layers and max-pooling or max-timeout pooling layers to extract higher-level features, but easily ignores contextual semantic features. LSTM [16] can capture long-term dependencies between word sequences, but cannot take into account local features. The GRU [17] model can solve the problem of long distance dependence to a certain extent, but if the input data is too much, GRU will also cause the problem of long distance forgetting due to gradient disappearance. The BiLSTM [18] model can capture bidirectional dependencies and capture context features more accurately. [PERSON] [19] proposed a CNN-LSTM to improve text sentiment analysis, which takes account of both contextual and local features. [PERSON] [20] proposed a combined GRU-CNN model, which proved that the combined model had better classification effect than a single model. Withthe rise of the attention mechanism, many scholars have added the attention mechanism to the LSTM to focus on important features to make up for the shortcomings of LSTM in feature extraction. [PERSON] [21] proposed an attention-based LSTM neural network that integrates social context knowledge and textual information through modeling. [PERSON] and [PERSON] [22] and [PERSON] [23] among others have also proposed a sentiment classification method based on attention and bi-directional long short-term memory (BiLSTM), and the effectiveness of the method has been demonstrated through experiments. Research on text classification using deep learning has mainly focused on how to extract features by better using neural networks, without considering that different words in a text contribute differently to the semantics and that different features have different effects on the classification model.  
Therefore, this paper focuses on studying the comments under TikTok short videos to explore where consumers' concerns lie when watching short videos, enriching the study of comment data on the TikTok platform. A feature fusion method based on the combination of the LDA topic model and Word2 Vec word vector is proposed in feature extraction. The method incorporates the topic features of the text based on the word vector, extends the feature representation of the comment information, and solves the problem of inconspicuous sparse feature representation of the short text to some extent. The fused features are input into a BiLSTM improved by self-attention, which reduces the dependence on external parameters and allows the model to focus on the text's features. Combining it with the BiLSTM facilitates better extraction of more important features for classification. After comparative experiments, the superiority of the method for topic classification was proven.
## 5 Experimental Results and Discussion
### Transmission Performance with Single-Arm Driven Mode
When the ROP was larger than \(-1\) dBm, the error floor could be observed, mainly due to the quantization noise of DAC. At the 7% and 20% HD-FEC thresholds, there were 0.7 dB and 0.6 dB penalties for PAM-6 and PAM-8 signals. Such a penalty can be attributed to the common mode noise elimination with differential driven mode.
# Software Architectures for Adaptive Mobile Learning Systems: A Systematic Literature Review
## 4 Third Phase: Review Results
### What Requirements Must Be Considered for the Development of an AMLS? (RQ1)
* Obtaining, managing, and using domain information is another crucial requirement involving the hierarchical representation of learning resources such as exams, courses, exercises, and examples. This requirement also involves establishing the difficulty levels of the learning topics addressed and the educational content formats available. Fulfilling this requirement also involves performing various tasks on domain information, such as processing, modeling, storage, distribution, reasoning, and delivery [36]. * Usability. The ISO/IEC 25000 standard [37] defines it as the ability of a product to be used by certain users to achieve specific objectives with effectiveness, efficiency, and satisfaction within a specific context. * Generation of student history is required to achieve persistent storage of context, student, domain information, and information about the types of adaptations performed for the learner. * Generation of the student profile is a requirement that aims to characterize and profile students based on information such as learning style, objectives, and level of studies. * Technological resource management consists of registering, updating, deleting, sorting, and classifying information about the characteristics of the user's access devices, such as storage size, battery levels, available presentation formats, and connections. The objective of this requirement is to use the information about the devices' technological resources to customize the educational resources. * Educational resources management consists of registering, updating, deleting, ordering, and classifying all educational content, including videos, courses, documents, and images. * Heterogeneity is a requirement that arises from the diversity and differences in an AMLS regarding information sources, types of information, device hardware, and mobile operating systems, among other aspects. * Robustness against disconnections involves providing mechanisms that ensure the operation of applications in the event of frequent disconnections [38]. * Lightness is a requirement that consists of achieving efficient use of the processing and storage resources of mobile devices during the execution of the system [38]. * Extensibility consists of generating an architecture that is open to new ways of accessing sensors and functionalities [38]. * Modularity focuses on separating responsibilities into single-purpose components [38]. * Ease of testing and maintainability. Consistency in components should facilitate the development of unit tests and maintainability [38]. What Software Models or Architectures and Their Main Characteristics Have Been Proposed to Develop AMLS? (RQ2)  
This section describes the software architectures proposed in the selected primary studies in this SLR based on the following aspects: stakeholders, main services provided, architectural styles, views, or components, and technologies used to implement the proposed AMLS.
## 2 Materials and Methods
### Sampling and Data Acquisition  
#### 2.5.1 Acquisition of Data by GPR  
The GPR data were acquired using a GSSI SIR 3000 GPR system (Salem, NH, USA), with 200 MHz and 400 MHz antennas. The GPR sections were acquired within a time window of 200 ns. The survey was georeferenced with a handheld GPS with Navigation Sensors (GPSMAP 64 SX), Garmin Ltd. (Olathe, Kansas, U.S.A.). The acquisition system of the electromagnetic signals was bi-static, with a standard offset between the transmitter and receiver antennas.  
The GPR was calibrated in an area adjacent to each fish-farming site, where a hole was excavated and a metal bar was inserted to a depth of approximately 50 cm to obtain a test profile and determine the propagation velocity of the electromagnetic waves using the hyperbole overlap method.  
The GPR data were collected at this site (Area 1, Figure 3a) in March 2017. Four profiles were surveyed: AB (240 m in length, with readings being taken at intervals of 10 m), BC (60 m in length, 5 m intervals), CD (40 m in length, 5 m intervals), and EF (45 m in length, 5 m intervals).  
The privately-owned fish farm (Area 2, Figure 4) was also surveyed using GPR in March 2017. Two profiles were surveyed: AB (400 m in length and readings taken at intervals of 5 m) and CD (105 m in length, 5 m intervals). The terrain is flat.  
Figure 3: Area 1 (Braganca, Para), on which an aquaculture research center (CEANPA) is under construction, showing the geophysical profiles (**a**) and the location of the ponds (**b**) (modified from [56]).
# Clustering Analysis for Classifying Student Academic Performance in Higher Education
## 5 Discussion  
The k-means algorithm produces the best clustering compared to the BIRCH and DBSCAN algorithms. This is due to k-means algorithms that easily cluster high-dimensional numerical data. Implementing k-means also helped label groups of students and successfully selected influential features in this student dataset. In general, the k-means algorithm is reliable and consistently produces consistent clusters after several runs of tests.  
The BIRCH algorithm, the second-best algorithm for Model B, has produced low classification quality compared to k-means. After the analysis was conducted, it was found that the characteristics of the students were not well separated. This weakness may be related to the lack of this algorithm, which is sensitive to the arrangement of records in the data set and prefers spherical clusters.  
Moreover, the evaluation results show that DBSCAN is the lowest and weakest algorithm in clustering student datasets. The clustering analysis results also showed that the students could not be separated well, and there were no prominent performance characteristics for each group of students. The DBSCAN algorithm is a density-based clustering technique that assumes that clusters are high-density areas in a single space and are separated by low-density areas. So, this algorithm can identify clusters in a dataset by simply looking at the local density of data points. In this case, the DBSCAN algorithm cannot cluster well because the student dataset used is both high-dimensional and not spatial in nature, with noise causing the complexity to be high [40]. The principal component analysis method (PCA) approach successfully reduced the dataset's dimensions but caused data interpretation difficulties.
# Faster and Better Nested Dissection Orders for Customizable Contraction Hierarchies
## 3 Results
### Order Experiments
#### 3.4.2 Preprocessing Time  
Previously, CCH performance came at the cost of high preprocessing time. We compute better CCH orders than FlowCutter in a much shorter time.  
KaHiP 0.61 and KaHiP 1.00 are by far the slowest. KaHiP 2.11 is faster than F100 but slower than F20. All InertialFlowCutter variants are faster than F20. IFC8 and F3 have similar running times. Metis is the fastest by a large margin, and Inertial Flow is the second fastest.  
The two old KaHiP versions are slow for different reasons. As already mentioned, K0.61 computes at least 10 cuts, as opposed to K1.00 and K2.11. K1.00 is slow because the running time for top-level cuts with \(\epsilon\geq 0.2\) increases unexpectedly, according to [14].  
Using 16 cores and IFC8, we compute a CCH order of Europe in just 242 s, with 2258 s sequential running time on the Skylake machine. This corresponds to a speedup of 9.3 over the sequential version. See Table 3. Note that, due to using eight cutters, at most eight threads work on a single separator. Therefore, in particular for the top-level separator, at most 8 of the 16 cores are used. The top-level separator alone needs about 50 s using eight cores. Due to unfortunate scheduling and unbalanced separators, it happens also at later stages that a single separator needs to be computed before any further tasks can be created. Using eight cores, we get a much better speedup of 6.8 for Europe; up to four cores, we see an almost perfect speedup for all but the smallest road network. This is because some cutters need less running time than others. Thus, there is actually less potential for parallelism than the number of cutters suggests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Graph**} & \multicolumn{5}{c}{**Cores**} \\ \cline{2-7}  & & **1** & **2** & **4** & **8** & **16** \\ \hline \multirow{2}{*}{Col} & Time [s] & 11.6 & 6.1 & 3.3 & 2.1 & 1.7 \\  & Speedup & 1.0 & 1.9 & 3.5 & 5.5 & 6.8 \\ \hline \multirow{2}{*}{Cal} & Time [s] & 71.5 & 36.7 & 19.2 & 11.3 & 7.2 \\  & Speedup & 1.0 & 1.9 & 3.7 & 6.3 & 9.9 \\ \hline \multirow{2}{*}{Eur} & Time [s] & 2257.8 & 1160.0 & 600.7 & 334.2 & 241.8 \\  & Speedup & 1.0 & 1.9 & 3.8 & 6.8 & 9.3 \\ \hline \multirow{2}{*}{USA} & Time [s] & 1869.5 & 947.9 & 497.0 & 275.5 & 173.2 \\  & Speedup & 1.0 & 2.0 & 3.8 & 6.8 & 10.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Running times in seconds of IFC8, using up to 16 cores of the Skylake CPU.
# A Joining Procedure and Synchronization for TSCH-RPL Wireless Sensor Networks
## 2 Overview on the IEEE 802.15.4 TSCH and RPL  
Regarding WSN, there are different standards that allow for the construction of this type of ad-hoc mesh network. However, the IEEE 802.15.4 standard has managed to position itself as the de facto standard for this type of network [2], having provided inspiration for different industrial systems, such as WirelessHART [3], ISA100.11a [4], WISA [5], and ZigBee [6]. In the same way, the RPL protocol has been proposed as a standard solution for the routing layer to build paths towards a root node (convergecast traffic). There is a great variety of publications that are related to this routing protocol, and provide different improvements and modifications. These solutions are mainly based on obtaining different routes according to the criteria that are defined in the objective functions (OF) used by this protocol. The main aspects of the aforementioned protocols are summarized below.
###### Abstract  
The utilisation of reclaimed asphalt pavement (RAP) as a suitable substitute for natural aggregate and binder offers an energy-saving and cost-effective approach to enhance the performance of asphalt mix. Realiasing the potential use of RAP as a promising recycling technique, many countries are seeking to recycle RAP as part of the global effort to address the rising challenge of climate change and contribute to a sustainable environment. This study aimed to develop an integrated approach to determine the amount of RAP to be used in an asphaltic concrete wearing course with 14 mm nominal maximum aggregate size (ACW14). The RAP was incorporated with two waste materials comprising waste engine oil (WEO) as a rejuvenator and Crumb Rubber (CRM) as a binder modifier. A total of five different mixes, which include R0 (control mix), R30, R50, R70, and R100 (replacement of 30%, 50%, 70%, and 100% of RAP aggregates in the mix, respectively) were evaluated. The Marshall parameters, resilient modulus (M\({}_{\text{R}}\)), indirect tensile fatigue, moisture susceptibility, and mass loss (ML) tests were conducted to investigate the performance of each mix. Finally, an arbitrary scale was developed to optimise the RAP content. The results showed that the Marshall parameters, moisture susceptibility, and ML values of the RAP mixes met the criteria outlined in the standard. According to the M\({}_{\text{R}}\) performance, the R50, R70, and R100 mixes were more resilient than the R0. In terms of fatigue resistance, the R30, R50, and R70 mixes showed better performance than the R0. Overall, the collective performance of all RAP mixes was above the R0 and it increased with the increment of RAP content. Therefore, it was possible to design ACW14 mixes with up to 100% RAP in combination with WEO and CRM.  
reclaimed asphalt pavement; ACW14 mix; hot mix asphalt; waste engine oil; crumb rubber modifier +
Footnote †: journal: Applied Sciences  
Accepted: 17 May 2021  
Published: 4 June 2021  
Published: **Note**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Access-Control Systems  
Access-control systems ensure that only compliant vehicles gain entry, enabling authorities to dynamically adjust restrictions based on real-time traffic, pollution levels, and policy goals. Without robust access control, LEZs risk inefficiency, enforcement challenges, and public resistance.
## 3 Results and Discussion
### Experimental Results
#### 3.3.3 Case C  
In this case, \(\lambda=1.3\), \(\sigma_{m}=0.04\) m, \(m_{m}=0.25\) kg. Three experiments were performed with \(\beta=0.6,\ 0.8,\ 1\), respectively. The tip residual accelerations are shown in Figure 14.  
In this case, the situations when \(\beta=0.6\) and \(\beta=0.8\) are almost the same, but when \(\beta=1\), the residual vibration amplitude increased evidently.  
Experimental results illustrate the square torques are efficient to compensate sinusoidal disturbance, but the effects vary with different conditions. According to the results of experiments, for all the three cases, the compensation effects are almost the same when \(\beta=0.6\) and \(\beta=0.8\). Compared with that, situations when \(\beta=1\) are worse. The amplitudes of residual vibration during compensation are listed in Table 2.  
Figure 14: Measured accelerations of the flexible beam tip in Case C.  
Figure 13: Measured accelerations of the flexible beam tip in Case B.  
Recall the added special simulation results which corresponded to the experiments. Figure 15 presents the results in the amplitude ratio-domain. The point marked 'ex' means the experimental data. The experimental results were largely in line with the simulation results. Simulation results show that the maximum amplitude of residual vibration always has a minimum point for each frequency ratio. Despite that the number of experiments is limited, the effectiveness and veracity of the proposed criterion are proved. Both experiments and simulations have verified the veracity of our study. However, it should be noted that all the experiment results are from the SGSRT, and the limitation of single axis rotation can be avoided. This limitation will be solved in future studies.
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 1 Introduction
At the biometric enrolment stage (which should be carried out in a secure way whenever an individual is associated with a sensor), non-sensitive Salted Data are generated and stored in the sensor. Ideally, these biometric Salted Data do not leak information about the biometric template as the Helper Data associated with the secret key do not leak information about the secret key. At the biometric verification stage (which is carried out whenever an individual should be recognized at the sensor node), the biometric Salted Data stored are employed to compare the template biometric features with the query biometric features obtained from the individual. In summary, the contributions of this paper are the following:  
1. The proposal of sensor nodes authenticated by their physical identity and the physical presence of their users. 2. A low-cost solution that uses the SRAM PUF of the sensor node to prove the physical identity of the node, to obfuscate secret keys, and to obtain the nonces of the proposed communication protocol. 3. The proposal of a lightweight fingerprint recognition solution that proves the physical presence of the user and that protects biometric data with the PUF response at the sensor node. 4. Experimental results showing that the proposed solutions can be implemented in low-cost hardware platforms. To the best of our knowledge, this is the first work which combines PUFs and biometrics to increase security at sensor nodes. The paper is structured as follows: Section 2 summarizes several application scenarios of the proposed sensors. Section 3 presents a low-cost solution to use the SRAM of the sensor nodes to provide not only physical identities of devices but also obfuscation of sensitive information and nonces. Section 4 describes the fingerprint feature named QFingerMap16 (QFM), how it can be extracted using sensor nodes with low-cost hardware, and how it can be protected at sensor nodes so that biometric data are always protected whenever sent and stored. Section 5 describes a communication protocol between the proposed sensor nodes and the base station. Results in terms of authentication, implementation, security and privacy are presented and discussed in Section 6. Finally, conclusions are given in Section 7.
# Automated Assessment Tool for 3D Computer-Aided Design Models
## 3 Proposed Approach
### Implementation and Validation
#### 3.3.1 The Developed Tool  
The developed tool is called the CAD MAA Tool as an abbreviation of Computer-Aided Design Model Automatized Assessment Tool. It is mainly composed of two interfaces, as shown in Figure 4. The first interface appears when the user launches the created add-in and loads the reference model as well as the model under evaluation (Figure 4a). After choosing the CBrep (the coefficient of the Brep evaluation), the CG (the coefficient of the general evaluation), the CFm (the coefficient of the feature manager evaluation), and the CC (the coefficient of the constraints evaluation) coefficients, and pressing the \"Calculate\" button of the CAD MAA Tool results interface (Figure 4b), the results interface will be displayed.  
Figure 4: Developed tool’s structure.  
The latter interface clearly shows the student's final mark and sub-marks and a brief evaluation (good level, quite good level,...). This main interface also offers the user the option to reload the model (after making corrections).  
The created tool offers the possibility to calculate a global mark, but it also enables the user to calculate sub-marks such as a general evaluation mark, a feature evaluation mark, a geometry evaluation mark, or even a constraint evaluation (Figure 5). The final score is simply a linear combination of the subscores with different coefficients depending on the teacher's strategy and the objectives of the lesson.  
This general evaluation makes an evaluation of the model possible without the need to enter to the geometric and topological details. It globally compares the two models (Figure 5) in terms of the following:  
* Center of mass (X-coordinate, Y-coordinate, Z-coordinate);
* Volume;
* Surface area;
* Mass;  
Figure 5: Evaluation details interface.  
* Density (includes material comparison);
* Moment of inertia (Lxx, Lyy, Lzz, Lxy, Lxz, Lyz).  
However, feature-based evaluation directly compares the feature manager of the two models and verifies whether specific features are created properly or not.