esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies
## 4 Experimental Results
Therefore, we investigated the extent to which VADER can correctly predict sentiments associated with user reviews. The effectiveness of VADER relies on human judgement. We asked two people, both of whom had travel experience and had no relationship with this research, to be human coders. One was responsible for annotating user reviews related to KKday, and the other was responsible for annotating those related to Klook. Because fine-grained sentiment polarity is a bit too challenging for human coders, we asked the human coders to annotate sentiments as positive, negative, and neutral. Manual annotations were used as ground truth to measure the validity of VADER. Table 3 shows the annotation statistics of VADER and the human coders. The accuracy, which is the ratio of the number of correct predictions to the total number of reviews, was \((200+15+21)/297=0.7946\). The performance measured in terms of precision, recall, and F1-Score is shown in Table 4. It was observed that VADER performed better in the classification of positive and negative reviews compared with that of neutral reviews. This is presumably due to the relatively narrow numerical interval of VADER scores defined as neutral. Another possible reason is that human coders tend to annotate a sentiment as neutral when positive or negative sentiments are less obvious in the review. It can be seen from Table 3 that 54 reviews were annotated by human-coders as neutral, which was twice as many as those annotated by VADER. To validate the features extracted by our method, we also asked the two human-coders to select up to ten features from each review. In a total of 296 reviews, the total number of features selected by our method is 2960, while the total number of features selected by human-coders is 2353. For a same review, if the feature selected by our method interacts with the feature set selected by the human-coder, then it is considered as matching. Among the 2960 features, there are 1718 matching features. Therefore, the recall, precision, and F1-score metrics are 0.7301, 0.5804, and 0.6467, respectively. Compared to the feature set selected by computer, the feature set selected by human-coders is smaller, which leads to high false positive rate. As a result, the precision is lower than recall. If the set of manually selected features can be expanded, the precision would be improved. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strengths:** & **Threats:** & \\ tour guide & high speed & \\ observation deck & food court & \\ pocket wifi & front desk & \\ credit card & hot spring & \\ qr code & mrt station & \\ day tour & rock formation & \\ souvenir shop & & \\ rail bike & & \\ cable car & & \\ \hline
**Weaknesses:** & **Opportunities:** & \\ sun moon lake & None & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Annotation results. Figure 4: SWOT matrix for KKday.
# Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle
## 6 Conclusions  
In this study, a hierarchical model predictive control was developed for torque-coupled HHVs in order to further improve fuel economy in the presence of the relatively low energy density of the hydraulic accumulator and frequent load changes, the randomness of the driving conditions, and the uncertainty of the engine dynamics. The proposed control is comprised of a SMPC-EMS in the upper layer and an MPC tracking controller for the engine in the lower layer. The SMPC-EMS is designed using a Markov prediction model and scenario-based enumeration approach, and the MPC tracking controller is combined with a load observer. Simulation verification and comparisons were performed for two driving conditions. Meanwhile, the experimental validation carried out on the test platform was also presented. The main findings of this work can be summarized as follows:  
1. The EMS of the upper layer has a direct influence on the improvement of fuel economy. The optimization-based EMS has better fuel economy than the RB-EMS, which was proved by an average increase of 5.4% and 7.3% for the SDP and the SMPC, respectively, in the simulation results, and an increase of 4.0% and 5.9% for the SDP and the SMPC, respectively, in the experimental results.
2. In the optimization-based EMS, both close to globally optimal and online implementable EMS have the advantage of further improvement in fuel economy. In the proposed SMPC-EMS, the Markov prediction model and online regulation of MPC ensured the adaptability to driving conditions and the accuracy of the torque demand distribution, which results in a further increase of fuel economy than the offline optimization-based SDP.
3. The closed-loop controller of the lower layer has an indelible contribution to fuel economy, and a well-designed controller ensures the tracking performance of the distribution torque from the EMS of the upper layer, and therefore further improvements the fuel economy. This was evidenced by an increase of 2.3% for the RB+MPC as compared with the RB+PID, and an increase of 2.2% for the SMPC+MPC as compared with the SMPC+PID.
4. From the experimental results, we found that, in practice, the optimization calculation amount of the optimization-based EMS could influence the control performance. Therefore, in future work, we should find ways to further reduce optimization calculation and increase the calculation speed of the control system in the design of EMS.  
[PERSON] wrote the original draft, designed control method, drew the figures and carried out the simulations; [PERSON] was responsible for supervising and guiding this research and reviewed and revised the article draft. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (no. 61573304 and no. 61973265) and the Natural Science Foundation of Hebei Province (grant no. F2017203210).  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Effect of Surfactants on the Properties of Matrices
#### 3.2.2 Mechanical Properties of Hardened Matrix Pastes  
The effect of 2-methyl 2,4 pentanediol and polyethylene glycol on compressive strength, flexural strength and elastic modulus of 80K20 MK is presented in Figure 8. Matrices 100K, 80K20 MK, 80K20 MKm and 80K20 MKp were cured at 80 \({}^{\circ}\)C for 24 h with additional post-curing at room temperature for 3 days. Matrix 80K20 MK resulted in a compressive strength of 30 MPa, somewhat better than the pure Koranel with the same activator. The addition of 2-methyl 2,4 pentanediol had no effect on the compressive strength; meanwhile, polyethylene glycol contributed to an increase of 35 MPa. The flexural strength for 80K20 MK was 0.8 MPa, comparable to the one for pure Koranel. The addition of 2-methyl 2,4 pentanediol tripled the flexural strength to 2.5 MPa and that of polyethylene glycol helped even to reach 4 MPa. This positive effect is also noticeable in the elastic modulus, for which 80K20 MK reaches 3 GPa, and 80K20 MKm and 80K20 MKp reach, respectively, 5.3 and 6.3 GPa in bending, whereas for axial, it reaches 2.5, 4.6 and 6.2. This large effect of surfactants is likely due to their influence on reducing microcrack formation [23], thus posing these surfactants forward as valuable additions for producing high strength lightweight panels. The fact that the compressive strength increases only slightly is because defects are closed upon  
Figure 7: Rheology of matrices K (**a**), 80K20 MKm (**b**) and 80K20 MKp (**c**), all indicated with open dots in comparison with matrix 80K20 MK, indicated with filled dots.  
compression, while for flexion, a crack means concentration of the force on the edges. Thus, the structure will become stronger if less cracks are formed.  
Compared to some of the literature data [17], a compressive strength of 35 MPa is rather low. The most likely reason is the lower OH concentration or higher modulus of the chosen activating solution, which was beneficial for the lower reaction rate but also leads to a higher fraction of undissolved slag in the hardened alkali-activated slag. The flexural strength of concrete is usually a factor of 8-10 lower than the compressive strength [36]. The flexural strength for the samples without surfactants is thus rather low, but the use of surfactants increases it to an average value.
## 2 Spatial Similarity Analysis and Sampling Method of Robot Errors in Configuration Space  
### Robot Kinematics Model Construction  
To investigate the distribution of industrial robot positioning errors, the base coordinate frame {B} and the tool coordinate frame {T} are firstly defined. As shown in Figure 1, the base coordinate frame of a six-axis robot is fixed to the robot base, with its Z-axis coinciding with the A1 axis of the robot, and its X-axis being perpendicular to both the A1 and A2 axes; and the tool coordinate frame is fixed to the robot flange, with its Z-axis coinciding with the A6 axis of the robot, and its X-axis being perpendicular to both the A5 and A6 axes. To determine the space status \(T_{\text{BT}}\) of the tool coordinate frame in the base coordinate frame for any given joint angles \(\mathbf{Q}=[\theta_{1},\,\theta_{2},\,\theta_{3},\,\theta_{4},\,\theta_{5},\, \theta_{6}]\), a kinematic model based on the Product of Exponentials (POE) formula is established. In this model, each rotational joint of the robot can be considered as a helical motion with a fixed axis and pitch, as illustrated in Figure 2, where \(\mathbf{s}\) denotes the unit vector along the helix axis, \(\mathbf{r}\) is the perpendicular vector from the {B} origin to \(\mathbf{s}\), and \(h\) is the intercept of the helical motion. Let \(\hat{\theta}\) represent the angular velocity of the helical motion and \(-s\hat{\theta}\times\mathbf{r}\) and \(hs\hat{\theta}\) represent the linear velocities of the origin of the coordinate frame in the radial and axial directions of the helix, respectively. The helical motion can be described by the motion screw \(\mathbf{V}\):  
\[\mathbf{V}=\begin{bmatrix}\mathbf{\omega}\\ \mathbf{v}\end{bmatrix}=\begin{bmatrix}\mathbf{s}\dot{\theta}\\ -\mathbf{s}\dot{\theta}\times\mathbf{r}+\mathbf{hs}\dot{\theta}\end{bmatrix},\mathbf{V}\in \mathbb{R}^{6} \tag{1}\]  
In analyzing the theoretical motion screws of the rotational joints on the robot, it is known that \(\|\mathbf{\omega}\|\
eq 0\) and \(\mathbf{h}=0\). Therefore, the motion screw \(\mathbf{V}\) can be normalized and simplified as  
\[\mathbf{\zeta}=\mathbf{V}/\|\mathbf{\omega}\|=\begin{bmatrix}\mathbf{s}\\ \mathbf{v}\end{bmatrix}=\begin{bmatrix}\mathbf{s}\\ \mathbf{r}\times\mathbf{s}\end{bmatrix},\mathbf{V}\in\mathbb{R}^{6} \tag{2}\]  
After obtaining the motion screws of all joints based on the robot geometric structure, a theoretical kinematic model of the robot can be established using the POE formula [3]:  
\[\mathbf{T}_{\text{BT}}=F(\mathbf{Q})=e^{\xi_{1}\theta_{1}}e^{\xi_{2}\theta_{2}}e^{\xi_ {3}\theta_{2}}e^{\xi_{4}\theta_{4}}e^{\xi_{5}\theta_{5}}e^{\xi_{6}\theta_{6}} \mathbf{M} \tag{3}\]  
where \(\xi_{i}\) represents the normalized motion screw of joint \(i\), \(\theta_{i}\) is the rotation angle of joint \(i\), and \(M\) is the space status \(\mathbf{T}_{\text{BT}}\) when all joint angles are set to 0.
## 3 Multiple-Access Reservation Receiver Initiation MAC (MAR-RiMAC)
### Robustness to Noise  
As previously explained, the CSS approach is highly robust to noise, i.e., CSSs can be successfully detected even under very low SINRs (\(-6\) dB). The ASC approach is also resilient to noise, as it only relies on a simple energy detection mechanism that only requires distinction between \"Busy\" and \"Idle\" minislots, i.e., to determine whether the received signal is above a predefined threshold. Note that similar detection is utilized by many protocols that rely on a carrier sense mechanism that needs to determine whether the received signal is above the noise floor (\"Busy\") or not (\"Idle\"). It is still possible to have minislot magnetections, which can be split into two types: false positive, in which the sink labels the minislot as \"Busy\" even though no device was transmitting in the minislot, or false negative, in which the sink labels a minislot as \"Idle\" even though at least one device has transmitted in this minislot. The former error is more common due to noise spikes or unexpected interference. Resiliency to such errors can be attained by extending the sequences and, in particular, adding sufficient redundant bits (which result in additional minislots) to ensure that the actual signaling set is discovered despite the few erroneous minislots. The longer the sequences, the less susceptible to noise, thus trading overhead for robustness. An analysis of the number of redundant bits that are required to render the ASC insusceptible to noise can be found in [58].
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
## 5 Key Encapsulation and Recovery Method Using CRP
This protocol incorporates a step-by-step approach that enables the receiver to recover the correct key by iteratively analyzing the responses, identifying matches, and addressing collisions and failures to detect. By the conclusion of the search process, the receiver will possess the necessary information to determine the positions that correspond to successful matches and those requiring further investigation by flipping one bit at a time. A collision occurs when two randomly selected responses, ra and rb (each consisting of \(P\) bits), share at least \(P\)-\(T\) common bits. The assumption made in this protocol is that all responses follow a binomial distribution, where the probability (\(q\)) of obtaining a state \"0\" is equal to the probability of obtaining a state \"1\" (\(q\) = 0.5). To ensure the protocol's effectiveness, it is necessary to ensure that the probability of collision \(\Psi(P,T,q)\) between the responses of the orderly subset and randomly chosen responses remains sufficiently low [33]. A model based on binomial distribution has been developed that focuses on three key variables within the algorithm: the rate of injected noise in the subset of responses, the length of the responses (\(P\)), and the threshold (\(T\)) in terms of the number of bits. The number \(t\) represents the number of erratic bits below the threshold \(T\). This model seeks to find the optimal values for these variables, striking a balance that minimizes collision probabilities while maintaining the protocol's efficiency and reliability. The formula is expressed as  
\[\Psi(P,T,q)=\sum_{t=0}^{T}[\binom{P}{t}q^{t}\cdot(1-q)^{P-t}] \tag{1}\]
# Application of the Random Decrement Technique in Damage Detection under Moving Load
## 3 Laboratory Models of Simply Supported Beam and Arch Cable Bridge  
### Experimental Study of the Simply Supported Beam  
The accuracy of the proposed method was verified using two laboratory models, namely, a simply supported beam and an arch cable bridge. This part illustrates the simply supported beam model constructed in the laboratory of civil and structural health monitoring, Zhejiang University, Hangzhou, China. The laboratory model consists of a 6-m long I-shaped beam. In the cross-section, the beam's flanges measure 0.072 m \(\times\) 0.005 m, while the web measures 0.112 m \(\times\) 0.005 m. The beam is made of structural steel with Young's modulus of 200 GPa, a Poisson ratio of 0.3, and a density of 7850 kg/m\({}^{3}\). In this model, a bogie was used to simulate the moving load. When the bogie passes through the beam, the acceleration data were recorded at 9 stations with a fixed distance of 0.6 m simultaneously [18]. Figure 4 shows the positions of accelerometers attached to the simply supported beam in the laboratory. The beams, bogie, and few of the accelerometers are shown in Figure 5. A typical crack, manually created on the beam, and its dimensions are shown in Figure 6. In this figure, the delta represents the ratio of the height of crack to the height of the beam.  
Figure 4: Locations of accelerometers to record acceleration data.  
Figure 3: The concept of calculating the cross-RD function.  
The accelerometers were attached to the bottom of the beam. Due to use of the flanges as a lane for bogie, the bogie could simply run over the beam. Different velocities (0.3, 0.4, and 0.5 m/s) were considered to examine the effect of velocity variation on damage detection accuracy. The acceleration data were recorded with a sampling frequency of 500 Hz in this model. Table 2 shows damage scenarios considered in the simply supported beam model.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 3 Our Merging Approach: Ring Reunion Algorithm  
Next, we present our merging algorithm for ring-based peer-to-peer overlays, named Ring Reunion Algorithm. It aims on the one hand on reliable merging success in the presence of several parallel peer-to-peer overlays. On the other hand, the merging overhead in terms of bandwidth consumption and traffic should relate only to the number of overlay constructs (Section 4.1) in the network and not to the number of nodes. As constructs, we define patterns that form if nodes are interconnected by their successor pointers. We distinguish between three different constructs: full circles, hanger-ons, and chains (see Figure 4)--indicating whether the nodes are connected to a ring, a chain that is attached to other constructs, or a chain that is freestanding. Thus, any overlay construct periodically should initiate a merging attempt with further overlay constructs, regardless of its size.  
For the presentation of our approach, we first describe in Section 3.1 how a single node can identify potential other nodes in other overlays. Once a potential node is identified, we discuss in Section 3.2 how the nodes in an overlay construct coordinate to limit the number of merging attempts per overlay construct. With a potential contact at hand, in Section 3.3, we present our merging protocol and point out in Section 3.4 how the merging process terminates. In Section 3.5, we extend our Ring Reunion Algorithm to initiate coordinated merge processes in parallel. Our approach, termed in the evaluation Reunion2, Reunion3, and Reunion4 using \(2^{2}\), \(2^{3}\), and \(2^{4}\) parallel instances, improves the merging performance and robustness. Finally, in Section 3.6 we discuss how our Ring Reunion Algorithm handles the parallel merging of multiple overlay rings.
# Asynchronous Flipped Grant-Free SCMA for Satellite-Based Internet of Things Communication Networks
## 5 Simulation Results  
This section provides some extensive simulation results to evaluate the performance of the proposed multiple-access scheme AF-SCMA. Packets are generated by infinite MTDs following Poisson distribution and they are transmitted asynchronously. As a baseline, each packet contains 500 information bits which are encoded by 3 GPP Turbo encoder with coding rate \(r=1/3\) jointly with SCMA encoder. For SCMA decoder, we adopt the common but efficient MPA to decode the packet. Sending extra flipped packets simultaneously will reduce the received power of each packet. The performance of AF-SCMA is investigated under different desired received energy per bit to noise power spectral ratio \(\left[E_{b}/N_{0}\right]_{D}\). However, due to the effect of pass loss and shadow fading in the satellite return link, the power control mechanism cannot be perfect in practice. Thus, the power levels of the received packets are assumed to follow a lognormal distribution with mean \(\mu=0\) and standard deviation \(\sigma\) in dB. AWGN is added for each packet and they are summed together before decoding. Please note that the performance of AF-SCMA cannot be further improved by sending more replicas such as the methods mentioned in [6,8], since more replicas will destroy the construction of the transmitted packets. Therefore, we only evaluate the performance of AF-SCMA with 2 replicas (one original version and one flipped version). For AF-SCMA scheme, each packet is divided into 10 chunks (i.e., \(N_{chunk}=10\)), and the length of detection sliding window \(W\) is assumed to be 10 times of the packet length. Moreover, the maximum decoding iteration \(N_{iter}^{max}\) is set to be 10.
# Quantum Game Application to Recovery Problem in Mobile Database
## 3 Quantum Game-Based Recovery Model  
### Mobile System Architecture  
In a typical MDS design, a small database fragment is created from the main database on the MH. This design is meant to handle the accessibility limitations alleviated by MHs and mobile satellite services (MSS). If the MH is present in the cell serviced by the MSS, it may interact directly with another MH in the vicinity. The MH may freely move between cells, which each include a base station (BS) and a large number of MHs. Additionally, the BSs configured the stations to act as a wireless gateway, allowing them to communicate with the MHs and send data via the wireless network. Wireless communication is possible between the MHs and the BS, but not directly with the database server [11; 31]. Figure 2 depicts the mobile system's architecture.
## 3 Proposed Algorithm
### Consideration of Computational Complexity  
Although the proposed method is reasonably superior to the existing methods in terms of detection capability via migrating MF-DFM and residual phase errors, the improved performance comes at the cost of computational complexity. Specifically, the increased complexity comes from several aspects: (1) the range search; (2) the velocity search in MF-DFB; and (3) the phase estimation by the ME-PSO algorithm. Fortunately, when the parameters of the radar system are given and some prior information (such as location) of the target to be detected can be obtained, the searching area in the parameter space can be greatly reduced [32]. Specifically, the location information can be obtained from the previous tracking records or high-SNR results. The location information is not necessarily accurate before detection since the range search in the proposed MF-RFT can refine the location based on the coarse location information and reduce the scope of the range search, which also results in a decreased computational load. On the contrary, the scope of the range search should be extended to the maximum detection range of the target if the location information of the target is unavailable [45]. In addition, many operations, such as MF-DFB, can be replaced by the fast Fourier transform (FFT) algorithm if the number of Doppler dimensions is an integer power of two. Moreover, the proposed ME-PSO algorithm for phase estimation can significantly alleviate the computational burden with acceptable performance. Therefore, the proposed approach is promising for real-world implementation, and its running time will be evaluated in the next section.