esa-sceva/satcom-chunk-collection huggingface dataset:
# Kinematics and Performance Analysis of the 2T1R/1T2R Reconfigurable Decoupled Parallel Mechanism
## 6 Conclusions  
The three-degree-of-freedom reconfigurable parallel mechanism proposed in this article has decoupling characteristics in both 1T2R and 2T1R motion modes, and the parallel mechanism with decoupling characteristics can achieve the motion of the mechanism through simple control, which has the advantages of decoupling and easy control. Parallel mechanisms with 1T2R and 2T1R motion modes can be applied in fields such as mobile robots, parallel machine tools, and military robots. This provides researchers in this  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structural Parameter** & \(l_{11}\) **(mm)** & \(H_{11}\) **(mm)** & \(l_{12}\) **(mm)** \\ \hline Before optimization & 200 & 55 & 200 \\ After optimization & 375 & 30 & 45 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Optimization results of structural parameters.  
Figure 12: Performance maps of the mechanism under two motion modes: (**a**) workspace (2T1R); (**b**) LTI (2T1R) \(\alpha\) = 0; (**c**) workspace (1T2R); (**d**) LTI (1T2R) \(\alpha\) = 0; and (**e**) LTI (1T2R) \(\beta\) = 0.  
field with a way to study reconfigurable decoupled parallel mechanisms, enriching the configuration of decoupled parallel mechanisms and reconfigurable parallel mechanisms. The specific conclusions of this article are as follows:  
1. A reconfigurable three-degree-of-freedom parallel mechanism is proposed, which can be converted in both 1T2R and 2T1R motion modes by means of a metamorphic kinematic pair;
2. In 2T1R motion mode, there is complete decoupling between the input and output variables of the mechanism's motion; in 1T2R motion mode, there is partial decoupling between the input and output variables of the mechanism's motion;
3. Taking the motion/force transmission performance indexes of the mechanism in the two motion modes of 1T2R and 2T1R as the objective function, an optimization design method based on a performance map was used to optimize the main parameters of the mechanism. The performance indexes of the mechanism were significantly improved compared to those before optimization.  
Conceptualization, J.L.; methodology, J.L. and Y.W.; software, J.L. and L.Z.; validation, J.L., R.L., and Y.W.; formal analysis, J.L.; investigation, Y.W.; resources, R.L.; data curation, F.N.; writing--original draft preparation, J.L.; writing--review and editing, J.L.; visualization, J.L.; supervision, R.L.; funding acquisition, J.L., R.L., and F.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi, grant number 2023L563, the Key Research and Development Program of Shanxi Province of China, grant number 202202150401018, and the Opening Foundation of Shanxi Key Laboratory of Advanced Manufacturing Technology, grant number XJZZ202105.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 7 Conclusions  
The technique of _in-situ_ metallocenic polymerization of propylene onto a GF surface resulted in a promising route to increase the adhesion between PP matrix and GF reinforcement since an enhanced compatibility between them can be obtained. The main conclusions of this work are:  
* PP can be copolymerized onto GFs by using metallocene catalyst.
* PP chains grow from the glass surface and enhance the adhesion by entanglements with PP matrix in polymer composites.
* The amount of PP polymerized onto the fiber and the interfacial adhesion seems to have optimal conditions regarding either the PP coverage or the interfacial shear strength measured.
* The interfacial shear strength of PP/GF composites, measured by the fragmentation test, is considerably improved (up to a factor of 2.1) with respect to the case of untreated fiber-PP composites.
* The radial matrix crack developed during fragmentation tests indicates a good matrix fiber adhesion for all _in situ_ polymerized fibers.
* The polymer remains attached to the fiber surface after cryogenic fracture for all the samples subjected to polymerization.
* Uniaxial composites PP-copolymerized GF presents considerably higher ductility and toughness without detriment of strength, with respect to the composites prepared with untreated fibers. These improved properties are assigned only to the increased adhesion caused by molecular entanglements between the PP-grafted GF and PP matrix.
* The MAO pretreatment does not change the PP/GF mechanical properties, since both the adhesion and fiber properties remain unchanged.
* This treatment strongly modifies the composite fracture behavior. The interface fiber debonding is the main mechanism for untreated fiber composites, while PP disentanglement accounts for the breakage of polymerized fiber composites.  
The _in-situ_ polymerization method is clearly a very good approach to improve the compatibility between GFs and PP. The adhesion enhancement as well as the mechanical properties improvements were demonstrated with the evidences shown above. It also seems to be a promissory method to obtain PP prepregs to increase the performance of PP/GF composites by using mat reinforcements.
# Improvement of ANFIS Model for Prediction of Compressive Strength of Manufactured Sand Concrete
## 1 Introduction
[25; 26] have proposed a comparative study between Adaptive Neuro Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Support Vector Machine (SVM) in predicting the compressive strength of geo-polymer concrete based on mixture inputs. Similarly, [PERSON] et al. [27] has optimized the mix design of silica fume concrete in order to obtain desired compressive strength using Biogeography-based programming. In another study, [PERSON] et al. [28] has shown that the ANN model provided more accurate results than traditional regression in predicting compressive strength of lightweight concrete under heat treatment. [PERSON] et al. [29] has used ANN and ANFIS for investigating the compressive strength of brick-mortar masonry. In combining AI methods with global optimization techniques, [PERSON] et al. [30] have implemented whale algorithm in order to better optimize the weight of a ANN model when predicting compressive strength of concrete. In another attempt, [PERSON] et al. [31] have also predicted compressive strength of silica fume concrete based on a hybrid model involving ANN and multi-objective grey wolves technique. Based on these mentioned studies, it can be stated that the AI based methods could be able to analyze the nonlinear relationship between ingredients and compressive strength of various types of concrete for better prediction and assessment [32; 33; 34; 35; 36]. In this study, the main objective is to improve the performance of individual ANFIS model, which is one of the effective AI models, by using two optimized techniques namely Principal Component Analysis (PCA) and Teaching-Learning-Based Optimization (TLBO) for better prediction of the compressive strength of MSC. Out of these optimized techniques, PCA technique was applied for reducing the noise in the input space whereas TLBO was employed to increase the prediction performance of single ANFIS model in searching the optimal weights of input parameters. A number of 289 compression tests of MSC were gathered from the available literature, including inputs parameters such as mixture proportions, cement's compressive strength, water content, as well as manufacturedsand's characteristics. The target response of the present study is the long-term compressive strength of MSC, ranging from 3 to 388 days of curing age. Correlation Coefficient (R), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were employed to validate and test the effectiveness of the proposed model, as well as to compare with other AI models such as individual ANFIS and ANN.
# Spectrum Modeling of Out-of-Band Intermodulation for Dual-Band RF Amplifiers in OFDM Modulation
## 2 Spectrum Model
### The Power Spectrum Density of Amplified Signals
\(P_{y_{out-of-hmd}}(f)\) then equals to half of the summation of those two shifted spectrum items as  
\[\begin{array}{l}P_{y_{out-of-hmd}}(f)=\frac{1}{2}(P_{y_{3}}(f-(2f_{c_{1}}-f_{ c_{2}}))+P_{y_{4}}(f-(2f_{c_{2}}-f_{c_{1}})))\\ \left\{ \begin{array}{l}\frac{1}{2B_{2}}P_{o1}^{2}P_{o2}10^{-\frac{-1B_{3}}{5} }\\ +\frac{1}{4B_{1}^{2}B_{2}}P_{o1}^{2}P_{o2}10^{\frac{-1B_{3}}{5}}\\ \cdot\left\{-\left[f-\left(2f_{c_{1}}-f_{c_{2}}\right)\right]^{2}+4B_{1}B_{2}- B_{2}^{2}\right\},\\ \frac{1}{4B_{1}^{2}B_{2}}P_{o1}^{2}P_{o2}10^{\frac{-1B_{3}}{5}}\\ \cdot\left[4B_{1}B_{2}-2B_{2}\left|f-\left(2f_{c_{1}}-f_{c_{2}}\right)\right| \right],\\ \frac{1}{8B_{1}^{2}B_{2}}P_{o1}^{2}P_{o2}10^{\frac{-1B_{3}}{5}}\\ \frac{1}{8B_{1}^{2}B_{2}}P_{o1}^{2}P_{o2}10^{\frac{-1B_{3}}{5}}\\ \cdot\left\{-\left[f-\left(2f_{c_{2}}-f_{c_{1}}\right)\right]^{2}-B_{1}^{2}+4B _{1}B_{2}\right\},\\ \frac{1}{2B_{1}}P_{o1}P_{o2}^{2}10^{\frac{-1B_{3}}{5}}\\ +\frac{1}{8B_{1}^{2}B_{2}}P_{o1}P_{o2}^{2}10^{\frac{-1B_{3}}{5}}\\ \cdot\left\{-\left[f-\left(2f_{c_{2}}-f_{c_{1}}\right)\right]^{2}-B_{1}^{2}+4B _{1}B_{2}\right\},\\ \frac{1}{2B_{1}}P_{o1}P_{o2}^{2}10^{\frac{-1B_{3}}{5}}\\ +\frac{1}{8B_{1}B_{2}^{2}}P_{o1}P_{o2}^{2}10^{\frac{-1B_{3}}{5}}\\ \cdot\left\{-\left[f-\left(2f_{c_{2}}-f_{c_{1}}\right)\right]^{2}\right.\left. 2B_{2}-B_{1}<\left|f-\left(2f_{c_{2}}-f_{c_{1}}\right)\right|\leq B_{1}\right. \\ +\left.\left(2B_{1}-4B_{2}\right)\left|f-\left(2f_{c_{2}}-f_{c_{1}}\right) \right|\right.\\ -B_{1}^{2}+4B_{2}^{2}+4B_{1}B_{2}\},\\ \frac{1}{8B_{1}B_{2}^{2}}P_{o1}P_{o2}^{2}10^{\frac{-1B_{3}}{5}}\\ \cdot\left[B_{1}+2B_{2}-\left|f-\left(2f_{c_{2}}-f_{c_{1}}\right)\right|\right] ^{2},\\ 0\end{array}  \tag{26}\]  
By comparing ([14], Equation (54)) and (26), it can be seen that the in-band spectra will not be interfered by the out-of-band distortions. In Figure 2, A numerical case is shown with \(f_{c_{1}}=864\) MHz, \(f_{c_{2}}=886\) MHz, \(B_{1}=B_{2}=7\) MHz, \(IP_{3}=26.1\) dBm, \(P_{o1}=-2\) dBm, \(P_{o2}=1\) dBm. The blue dash waveform shows \(P_{y}(f)\) without CM and out-of-band intermodulation, and the red solid waveform shows \(P_{y}(f)\). The difference between the two lines indicates that the impact of out-of-band intermodulation is clear. Also, from Figure 2, we can see that \(P_{y_{out-of-hmd}}(f)\) has a clear interference if there are other signals centered around 842 MHz or 908 MHz in those channels. As a special case, if either \(\tilde{s}_{1}(t)\) or \(\tilde{s}_{2}(t)\) is zero, \(P_{y}(f)\) will be completely deduced to the single-band signal spectrum as in ([11], Equation (25)). Subsequently, the CM and out-of-band IM due to dual bands in Figure 2 will all disappear. Of course, only one single band in blue will remain.
## Data Used in this Study
### Meteorological Station Data  
We used monthly mean maximum temperature (from 1982-2013) and monthly precipitation data (from 2007 to 2013), which were provided by the Australian Bureau of Meteorology, to represent drought-related environmental stress. Monthly mean maximum temperature was calculated as the average of all available daily maxima for the month. The highest observed temperature for the 24 hours before 09:00 local time was recorded as the daily maximum air temperature for the previous day. Monthly precipitation was calculated by summing all available daily rainfall records within the month, which include all forms of precipitation that reach the ground, such as rain, drizzle, hail, and snow. To represent the overall status of the meteorology in Victoria, we used data from 21 temperature stations and 32 precipitation stations. Detailed information of these stations is illustrated in Figure 2  
Figure 1: Land cover map of the studied area (Victoria, Australia). The slant line area enclosed by the blue box is crop land. The crossed line area enclosed by the red box is evergreen broadleaf forest land.  
and Tables A1 and A2 in Appendix A. The measured data were quality controlled using quality flags and were relatively uninterrupted.
## 4 Inland and Marine MLH Detections Using Ground-Based CDWL
### Comparison of MLH Diurnal Variations over the Inland and Marine Sites  
To analyze the MLH diurnal variation, the MLH retrieval results are classified into two conditions of daytime and nighttime [25]. Here, the two conditions are distinguished by the gap between the MLH retrieval results based on aerosol and turbulence. The condition changes from nighttime into daytime when the gap is less than a specified value [11]. The daytime turns into the nighttime again when the gap is larger than the value. A specified value of 500 m is used in Inner Mongolia as the MLH maximum is 3-4 km.  
Figure 3: (**a**) Horizontal wind speed, (**b**) horizontal wind direction, (**c**) vertical wind speed, (**d**) Ig(TKEDR) and its MLH retrieval result measured by ground-based CDWL during 27–28 September 2019 in Inner Mongolia. (**e**-**h**) is 11–12 May 2019 in Hefei. (**i**-**l**) is 10–11 November 2018 in Sansha. Horizontal wind direction is defined as 0\({}^{\circ}\) for northerly wind, rotating clockwise. Negative vertical wind speed denotes rising wind. Pink circle and black diamond denote CDWL-derived MLH\({}_{\text{TKEDR}}\) and radiosonde-derived MLH\({}_{\text{RH}}\), respectively.  
Figure 4 shows a case comparison of the MLH retrieval results at noon and midnight. The TKEDR profiles at noon and midnight on the same day are drawn as a red line and black dash-dotted line, respectively. The red circle and the black circle denote the corresponding MLH retrieval results based on the constant TKEDR threshold. The differences between the MLH retrieval results at noon and midnight in Inner Mongolia, Hefei, and Sansha are 2.83, 1.51, and 0.15 km, respectively. It highlights that the MLH diurnal variation at the marine site (Sansha) is relatively small in this case.  
To further compare the MLH diurnal variations over the inland and marine sites, the statistical distributions of MLH retrieval results during ten clear days are performed. As shown in Figure 5, the red line and the black dash-dotted line denote the Gaussian fits of the distributions of MLH retrieval results in daytime and nighttime, respectively. \(\triangle\)MLH denotes the difference between the two Gaussian centers. The \(\triangle\)MLH in Sansha is 0.1 km, which is much smaller than the \(\triangle\)MLH of 2.5 km in Inner Mongolia and the \(\triangle\)MLH of 1.0 km in Hefei. The results indicate that the MLH diurnal variation at the marine site (Sansha) is smaller regardless of MLH maximum during this observation.
# Automatic Identification of Landslides Based on Deep Learning
## 3 Materials and Methods  
### Data Source  
Remote sensing datasets of landslide are difficult to obtain, we used the open source Bijie landslide dataset [24]. The Bijie landslide dataset is the first open remote sensing landslide dataset with careful triple inspection; the data set was proposed by scholars such as [PERSON] et al. (2020), and the classification research of landslides was carried out on it. Its study area is located in Bijie City, Guizhou Province, China, with about 26,853 square km and an altitude ranging from 457 m to 2900 m. The soil on the slopes caused by the perennial rainfall is soft and prone to landslides, and it is one of the most prone areas in China.  
The remote sensing images in the Bijie landslide dataset were captured by the TripleSat satellite, and the RGB images have a resolution of 0.8 m. Seven hundred and seventy landslide images and two thousand and three other types of images were intercepted from the captured remote sensing images. The dataset consists of satellite optical images and label files. In the process of making the dataset, two methods were adopted to interpret the landslide images to ensure the reliability of the database: One is the visual interpretation by geologists through optical remote sensing images; the other is based on residents' reports and field surveys. Throughout the work, the shapes of landslide samples were drawn with the help of ArcGIS.
## 5 Secure End-to-End Encryption for Iot Communication Scheme
### Registration and Key Generation  
The registration process for the secure end-to-end MQTT protocol is the initial phase to register devices that communicate in the MQTT protocol. This phase is controlled by a factory key (K\({}_{F}\)) and a subscription key (K\({}_{S}\)). The factory key in the protocol is used for communication between dealers or third parties with IoT devices in a secret sharing scheme to generate group key seeds. Meanwhile, the subscription key functions as the master key in generating the group key in combination with the group key seed\({}_{G}\).  
The registration phase of the secure end-to-end MQTT protocol is designed based on the secret sharing protocol developed by [PERSON] et al. [19]. This protocol was chosen because it supports multi-user communication using light operations because it uses XOR operations. This scheme also has advantages in several key management schemes. Implementation of [PERSON]'s secret sharing scheme can be intended for several uses, including one time keys, groups of valid keys, and dealer key management.  
Figure 2: Secure End-to-End Design on MQTT Protocol.  
The pre-registration phase aims to encrypt the channel before the registration as shown in the following process:  
1. Set-up environment: 1. Dealers store information on salt values, factory key hashes, and device subscription keys. 2. Each device has a factory key and a subscription key and can access salt information. 3. The dealer and each device have the functions, \(\Omega\) and \(\omega\), which satisfy [PERSON]'s secret sharing rule [19].
2. Encrypted Channel Creation Process 1. The dealer creates a one time user key for the IoT device and himself. One time user keys (OTUK) of IoT devices can be generated from the hash value of the factory key and device salt stored at the dealer. 2. Dealers generate communication keys and encrypted channels from dealer OTUK and OTUK IoT devices.
3. Process on IoT Device 1. The IoT device creates a device OTUK from the factory key and salt provided by the dealer. 2. IoT devices can communicate securely with encrypted channels created by dealers. 3. Through this encrypted channel, the device can exchange information or key seeds securely with the dealer.  
The proposed registration scheme for Galantucci's secret sharing is as a secure end-to-end MQTT registration scheme as shown in Figure 3.  
At this registration stage, each IoT device group key (\(K_{G}\)) is the same to communicate in the MQTT protocol.
# Routing Algorithm Based on Trajectory Prediction in Opportunistic Networks
## 4 Performance Evaluation
### Results and Discussion
#### 4.2.2 Delivery Ratio  
This part mainly analyzes the performance of data delivery ratio of different algorithms. The change law of data delivery ratio with simulation time length is introduced. Additionally, the effect of the number of nodes on the data delivery ratio is studied.  
In the same simulation scenario mode, the delivery ratio performance of different algorithms is studied. For the simulation results of each algorithm, we can find that the following Figure 3 mainly has five signs (max, the third quartile, median, the first quartile and min). As shown in Figure 3, in some special cases, two or more signs have very similar values. For example, the algorithm RATP proposed in this paper, the third quartile and median values are roughly around 0.01, which is very close. As can be seen from the Figure 3, although CHBPR has a high delivery ratio, it only considers the density of nodes and does not consider the distribution relationship between nodes, resulting in poor connectivity, making the data unable to be more efficient and rapid transport to the destination node, delivery ratio decline. Compared with other algorithms, the RATP algorithm has a higher delivery ratio because the algorithm not only analyzes the different location probabilities, but also analyzes the positional relationship among the node at the same time, the better the connectivity of selected routes, the delivery ratio is higher.  
Figure 4 shows the data delivery ratio of the RATP algorithm, CHBPR algorithm, GPCR algorithm and Greedy algorithm under different simulation times. It can be seen from the figure that as the simulation time increases, the data delivery ratio of the four algorithms also increases. The experimental results show that the RATP algorithm has a low data delivery ratio when the simulation time is between 1.5 h and 2.5 h. This is because a large amount of data is dropped, causing congestion in the network. However, RATP still has a higher data delivery ratio than other algorithms.  
Figure 5 shows the data delivery ratio of the RATP algorithm, CHBPR algorithm, GPCR algorithm and Greedy algorithm under different node numbers. As can be seen from Figure 5, as the number of nodes increases, the RATP algorithm has a higher data delivery ratio than other algorithms. The simulation results show that when the number of nodes is within a certain range, the data delivery ratio increases with the number of nodes. As the number of nodes increases, the chances of encountering between nodes increases, network connectivity increases, and information transmission efficiency increases. However, when the number of nodes exceeds a certain range, the number of neighbor nodes increases due to the increase of the node density, thereby increasing the probability of the selected next-hop node, and finally the data delivery ratio is lowered.  
Figure 3: Delivery Ratio.
# Road-Side Unit Anomaly Detection
## 2 State of the Art
### Clustering Algorithms
#### 2.3.1 Hierarchical-Based Algorithms  
The hierarchical clustering algorithm is based on algorithms denoted as **linkage**. The linkage is an algorithm which focuses on merging or splitting within a cluster. Some examples of this algorithm are as follows:  
* Single linkage: handles the two closest points in the cluster;
* Complete linkage that handles the two farthest points;
* Average linkage that uses an average fictive point to represent the cluster;
* Centroid linkage that uses the most representative point of the clusters.  
Slink [25] and Clink [26] are two algorithms that optimize the basic principle of agglomerative clustering using, respectively, the single and the complete linkage also denoted Agnes (agglomerative nesting) and Diana (divisive analysis) algorithms.  
BIRCH (balanced iterative reducing and clustering using hierarchies) [27] is used to find clusters in large datasets using less memory and run-time. It uses the concept of clustering feature (CF), which is a kind of summary of a cluster and CF-Tree. It is known as an algorithm that builds a cluster with only one scan of the dataset but it can improve the result with more small scans.