esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Statistical Analysis  
Three-factor analysis of variance was conducted to determine whether the reconstruction error significantly changed with the three factors method, noise, gap, and their interaction. The RMSE was taken as the error metric and the significance level of 0.05 was used. Tukey post hoc analysis was used for desired pairwise comparisons of significant factors. In all four cases, the two methods were found to result in significantly different reconstruction errors (Tukey post hoc test, \(p<0.001\)) with the DMDct method having lower error. The effect of noise on DMDct was insignificant (\(p=0.797\)), whereas the error of csDMD for noisy cases was significantly lower than its error for the noise-free cases (\(p<0.001\)). Both methods resulted in significantly higher errors for the gappy cases (\(p<0.001\)). Figure 6 shows the mean RMSE values of DMDct and csDMD for the four test cases studied with the error bars showing the standard deviations.
## 3 Results and Discussion
### Energy Assessment  
The energy balance associated with the mass balance in 3.5 is reported in the Sankey diagram of Figure 9. Data are expressed as a fraction of the chemical energy of the wet biomass input. The total energy input includes the process heat provided by the electrical heaters and the utilities' electrical power consumption, respectively 7.2% and 10.7% of the chemical energy of the feedstock.  
The heat of pyrolysis was assessed from the energy balance on the pyrolyzer: it corresponded to 1053.5 kJ per kg of raw biomass input (6.3% on an HHV basis), which is aligned with the data available in the literature [49; 67]. Even though bio-oil represents the main product, about 21% and 9.3% of feedstock energy content remain in the char and non-condensable gas, respectively. The energy losses at the pyrolysis reactor were 10% due to thermal losses (1%) and unrecovered products (9%). The latter was considered as unconverted dried biomass trapped in the reactor since it was not practically recovered, as suggested by [PERSON] et al. [49]. In the plant configuration herein discussed, the heat required by the process, the sum of the heat of pyrolysis (6.3%) and water's heat of  
Figure 9: Energy balances (percentages expressed as a fraction of the chemical energy of the wet biomass at the inlet).  
vaporization (1.8%), was externally provided by means of electrical resistors, while in an industrial scenario this would likely be supplied by combustion of co-products and the heat in the off-gases recovered by ORC applications [68].  
Despite 10% of feedstock chemical energy being lost as heat losses and thermal power transferred to cooling fluids, a significant energy concentration effect was achieved by combining fast pyrolysis with fractional condensation: bio- oils collected in the first and second condenser account for the 43% of biomass chemical energy at the inlet, while that retained in the aqueous phase (13.7%) indicates the presence of soluble organics in the water fraction.  
As liquid energy output, the first two fractions (excluding the aqueous phase) accounted for 43% of total energy (with an HHV of 24.4 and 21.7 MJ kg\({}^{-1}\), respectively) while representing 27.2% of the initial mass. Figure 10 displays the mass and energy balances associated with the results presented in Sections 3.5 and 3.7.  
The present layout demonstrated that a consistent amount of energy outputs could be recovered in different bio-oil cuts, favoring further upgrading processes.  
According to [PERSON] et al. [69], a lower hydrogen demand for a potential downstream upgrading process such as hydrotreatment can be achieved using the proposed strategy, increasing the overall life cycle efficiency. These findings, therefore, suggest that the conversion from solid to liquid in the proposed scheme has the potential to achieve a remarkable performance as regards the energy valorization of all products.
## 6 Results and Discussion
### Fatigue Life Assessment
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Joint**} & \multirow{2}{*}{**Type**} & \multicolumn{6}{c}{**Allowance of Lifetime Traffic Volume (\(\times\)10\({}^{6}\))**} \\ \cline{3-10}  & & \multicolumn{4}{c}{**Nominal Stress Approach**} & \multicolumn{4}{c}{**Hot Spot Stress Approach**} \\ \cline{3-10}  & & **JTG** & **EC 1** & **AASHTO** & **OBS 1** & **JTG** & **EC 1** & **AASHTO** & **OBS** \\ \hline \multirow{3}{*}{U2\({}_{\rm L}\)} & CU & \(\infty\) & 1,348,147 & \(\infty\) & 1,268,173 & \(\infty\) & 65,411 & \(\infty\) & 106,523 \\  & TEU & \(\infty\) & 3,248,470 & \(\infty\) & 3,055,767 & \(\infty\) & 157,358 & \(\infty\) & 262,168 \\  & Prolong 2 & - & 141\% & - & 141\% & - & 141\% & - & 146\% \\ \hline \multirow{3}{*}{U7\({}_{\rm L}\)} & CU & 11,421 & 60,150 & 17,006 & 35,351 & 1227 & 6396 & 2002 & 3483 \\  & TEU & 27,521 & 144,937 & 40,977 & 85,754 & 2952 & 15,387 & 4816 & 9016 \\  & Prolong & & 141\% & & 143\% & & 141\% & & 159\% \\ \hline \multirow{3}{*}{U12\({}_{\rm L}\)} & CU & 1108 & 2281 & 1400 & 29762 & 133 & 323 & 189 & 3079 \\  & TEU & 2671 & 5496 & 3373 & 71928 & 320 & 777 & 454 & 7893 \\  & Prolong & & 141\% & & 142\% & & 141\% & & 156\% \\ \hline \multirow{3}{*}{U13\({}_{\rm L}\)} & CU & 214,623 & 1,416,594 & 573,530 & 323,268 & 23,990 & 266,751 & 82,400 & 32,600 \\  & TEU & 517,152 & 3,413,399 & 1,381,968 & 778,942 & 57,712 & 641,712 & 198,227 & 85,035 \\ \cline{1-1}  & Prolong & & 141\% & & 141\% & & 141\% & & 161\% \\ \hline \hline \end{tabular}  
* OBS: Results solved by the observation traffic model. 2 Prolong = (\(N_{\rm TEU}-N_{\rm CU}\))/\(N_{\rm CU}\times 100\%\).  
\end{table} 
Table 4: Fatigue life of the typical rib-to-deck joints. strength of RD joints can be effectively enhanced through the application of TEUs in OSDs, which in turn improves the fatigue life of RD joints under vehicle loads.
## 3 Materials and Methods  
### Sample Collection and Analysis  
A total of 24 groundwater samples were collected from the valley basin, all of which were phreatic water extracted from the Borehole well. The samples were primarily concentrated in two areas. One is the agricultural activity area in the upper reaches of the Beichuan River, and the other is the urban area in the middle and lower reaches. These samples are representative of the status of groundwater under two main land use types in the basin. All sampling wells were pumped for more than 15 min to eliminate the influence of standing water in the wells. Before sampling, in-situ measurements of parameters (pH, etc.) were conducted at all sites. All collected groundwater samples were stored in high-density polyethylene bottles that had been pre-rinsed 3 to 5 times with the target groundwater. The collected samples were kept at a temperature of 4 \({}^{\circ}\)C and delivered to the laboratory for analysis within 48 h.  
The in situ physicochemical parameters (HACH HQ4D, Loveland, CO, USA) were measured using portable meters. The detection of major cations (Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\)) was performed using an inductively coupled plasma mass spectrometer (Agilent 7500 ce ICP-MS, Tokyo, Japan). Ion chromatography (Shimadzu LC-10 ADVP, Kyoto, Japan) was employed for the detection of major anions (SO\({}_{4}\)\({}^{2-}\), Cl\({}^{-}\), NO\({}_{3}\)\({}^{-}\), F\({}^{-}\)) and NH\({}_{4}\)\({}^{+}\). The determination of HCO\({}_{3}\)\({}^{-}\) was carried out using acid-base titration. TDS was measured by the gravimetric analysis method. According to previous work, heavy metals have been reported detected in the water system of the basin [50]. Therefore, the trace elements and heavy metals are also measured in the present research with the aid of inductively coupled plasma spectrometry (Agilent 7500 ce ICP-MS, Tokyo, Japan). To ensure the accuracy of the analytical results, each groundwater sample was subjected to three replicate tests. The standard and blank controls are interspersed throughout the experimental process. The ionic charge balance error percentage of the water sample test results was within \(\pm\)5%, indicating that the test results are reliable.
## 5 Proposed Protocol to Secure E-Commerce Transactions
### Procedures of Proposed Protocol
#### 5.3.3. Decryption Procedure  
It is a reverse process for an encryption process; public and private keys are used for decryption, and received requests are decrypted upon reaching the recipient. Our protocol decrypts the requests as in Algorithm 3, and we extract the keys from steps 1 and 2 up to \(DR_{i}=De(ER_{i})\), where \(De\) represents the decryption process, and the decryption process is completed. In our protocol, we use DLD technology to determine the data leakage in the text after decoding and calculating the probability (\(P_{i}\)) and then compare it with the threshold (\(t\)) to validate/detect the plaintext of the requests on the receiver device. Then, plaintext should conceal (\(C_{i}\)) using \(PW\) and \(Fk_{i}\) through performing an XOR operation with the text \(C_{i}=R_{i}\oplus PW\oplus Fk_{i}\) saved to be archived in datasets. Similar to Algorithm 2, Algorithm 3 stores keys anonymously.  
```
Input: \(K_{u}\) keys with 256-bit, \(PW\) and \(ER_{i}\), \(t\)-threshold Output: \(DR_{i}\)
1: Extracting \(K_{r}\longleftarrow PW\oplus sco\)
2: Extracting \(FK_{i}\longleftarrow FK_{e}\oplus sco\oplus PW\)
3: Using \(FK_{i}\) 256-bit with FER decryption
4: Using \(ER_{i}\longleftarrow En(R_{i}||FK_{i}||salt_{i}||N_{ID}\oplus U_{ID})\)
5: Decrypting \(DR_{i}\longleftarrow De(ER_{i})\)
6:\(P_{i}\longleftarrow DR_{i}/D_{i}\)
7: If \(P_{i}>t\) Declare as Info leakage
8: Else repeat step 6
9: Saving \(R_{i}\) in the dataset
10: Storing \(C_{i}\longleftarrow R_{i}\oplus PW\oplus Fk_{i}\)
11: Storing \(Fk_{e}\longleftarrow Fk_{i}\oplus sco\oplus PW\)
12: Storing connection order on the receiver side
```  
**Algorithm 3** Decryption procedure.
## 3 LPBF Sensing Techniques
### Thermal Techniques
The solidification phase change behavior of the printed alloy observed by DSC can hint toward cracking phenomena [165]. The observed undercooling of the alloy powder using a differential DSC was correlated with specific inoculation variants, such as cracks. At the lowest undercooling scenarios, the fewest number of cracks were observed. [PERSON] et al. [61] utilized DSC to determine the phase transformation temperatures of as-fabricated TiNi LPBF-printed samples. The phase transformation temperature was shown using DSC to be significantly affected by the alloy's proportion of Ti and Ni elements. Figure 28: Example provided by [159] of (**a**) a comparison between the melt pool width of the STWIP coaxial camera and optical imaging and (**b**) the single-track with process defects (highlighted). The Ni element was reported to be disproportionately lost due to the melting process, producing a martensitic transformation temperature difference. [PERSON] et al. [62] similarly investigated the Ni evaporation occurring during the LPBF printing process of NiTi alloys using DSC. The resulting precipitation, dislocations, and internal stresses concerning the phase transformation temperature were also observed. Another study compared DSC measurements with SEM microstructure imagery regarding the variation in the solidification microstructure of AlSi10 Mg powder, such as microstructure inhomogeneities [166]. [PERSON] et al. [113] measured the STR of an experimental high W-content nickel-based superalloy using DSC. It was observed that solidification cracking always occurred in the fragile mushy zone where the liquid and solids coexist. This observation concluded that a decreased solidus temperature leads to the formation of low-melting-point liquid films and increases the tendency for liquid cracking. DSC was used to produce thermograms, pointing to a thermal stability regime and a single sharp endotherm temperature, as shown in Figure 29 [75].
## 3 Results
### Determination of Peptides with LC-HRMS
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Protein (UniProt Database ID)** & **Identified Peptides** & **WPH** & **Epitope Sequence** & **Epitope Location in Protein Chain** & **Immun Epitope Database ID** & **References** \\ \hline  & f122-132 & H1 & & & & & \\  & f123-133 & H1 & & & & & \\  & f123-134 & H1, H2, H3 & & & & & \\  & f122-134 & H1, H2, H3 & & & & & \\  & f122-134 & H2 & EVDDEA & f127-132 & 95369 & [21] \\  & f125-134 & H2, H4 & & & & \\  & f125-135 & H2, H4 & & & & \\  & f124-134 & H2, H3 & & & & \\  & f127-135 & H4 & & & & \\ \hline  & f104-116 & H3 & FCMENS & f105-110 & 95378 & [21] \\ \hline  & f71-76 & H2 & IIAEKT & f71-76 & 95461 & [21] \\ \hline  & f77-82 & H2 & KIPAVF & f77-82 & 95498 & [21,43] \\ \hline  & f95-100 & H1, H2 & & & & \\  & f94-100 & H1, H2 & LDTDYK & f95-100 & 95545 & [21] \\  & f94-102 & H1, H2 & & & & \\  & f94-101 & H2 & & & & \\ \hline  & f149-154 & H1, H2, H3 & & & & \\  & f149-155 & H3 & LSFNPT & f149-154 & 95574 & [21] \\  & f149-159 & H3 & & & & \\ \hline  & f107-118 & H2 & & & & \\  & f104-116 & H3 & & & & \\  & f107-116 & H4 & & & & \\ \hline  & f109-114 & H1, H2 & & & & \\  & f108-114 & H1 & & & & \\  & f109-116 & H1, H3 & & & & \\  & f108-117 & H1, H2 & & & & \\  & f109-117 & H2 & & & & \\  & f107-118 & H2 & & & & \\  & f104-116 & H3 & & & & \\  & f107-116 & H4 & & & & \\ \hline  & f107-118 & H2 & PEGSLA & f113-118 & 95652 & [21] \\ \hline  & f96-102 & H1, H2, H3 & & & & \\  & f96-103 & H1 & TDYKKY & f97-102 & 95898 & [21] \\  & f94-102 & H1, H2 & & & & \\ \hline  & f124-131 & H1, H2 & & & & \\  & f123-131 & H1, H2 & & & & \\  & f122-131 & H1, H2 & & & & \\  & f122-132 & H1 & & & & \\  & f123-133 & H1 & TPEVDD & f125-130 & 95922 & [21] \\  & f122-134 & H1, H2, H3 & & & & \\  & f125-134 & H2, H4 & & & & \\  & f125-135 & H2, H4 & & & & \\  & f124-134 & H2, H3 & & & & \\ \hline  & f15-29 & H2 & VAGTWYSLAMAA & f15-26 & 95946 & [21] \\ \hline  & f26-40 & H3 & ASDISLLDAQSAPLR & f26-40 & 96113 & [44] \\ \hline  & f41-55 & H3 & VYVEELKPTPEGDLE & f41-55 & 97098 & [45] \\ \hline  & f25-35 & H3 & & & & \\  & f24-35 & H3 & & & & \\  & f25-40 & H3,H4 & AASDISLDA & f25-34 & 98677 & [24] \\ \hline  & f25-36 & H4 & & & & \\  & f25-38 & H4 & & & & \\ \hline  & f84-95 & H3 & DALNENKVLV & f85-94 & 98723 & [24] \\ \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)  
In addition to the LC-HRMS and SE-UPLC analyses, the residual \(\beta\)-LG content in the WPHs was quantified in Romer Labs Diagnostic GmbH (Austria), using a commercial AgraQuant(r) enzyme-linked immunosorbent assay (ELISA) test kit, according to the manufacturer's instructions. The amount of residual \(\beta\)-LG content was the lowest in the H1 hydrolysate (0.13 \(\mu\)g/g of WPH), the H2 sample contained a larger amount of \(\beta\)-LG compared to H1 (237 \(\mu\)g/g), and the most nonhydrolyzed samples showed a significantly larger \(\beta\)-LG content compared to the two previous samples (86,922 \(\mu\)g/g in H3 and 68,786 \(\mu\)g/g in H4) (Table 4).
## Approach based on overlapping community  
Overlapping communities means that there are some shared nodes between two different communities on the same network. This kind of situation is common in the real world (for example, a social network user participates in multiple groups at the same time), and some existing community partitioning algorithms are also able to reveal the existence of overlapping communities. Due to the special topological location of such shared nodes, some researchers started to define the bridging of nodes by using overlapping nodes in overlapping communities [1; 2; 10].  
[PERSON] et al. [10] proposed an extended overlapping community detection method and, based on this, they proposed a way to define node bridging. However, in some special cases, this indicator will identify nodes outside the community as bridge nodes. Therefore, the author proposes a method of correcting bridgeness with indicators such as degree centrality, which is called degree-corrected bridgeness.  
For networks that have divided overlapping communities, [PERSON] et al. [1] proposed a random walk-based sorting algorithm for bridge nodes to enforce network immunity strategies. First, extract overlapping nodes according to the community division results; then perform random walk RWOS from random nodes in the network and set the overlapping nodes visited in this process as immune targets until enough targets are collected.  
On the basis of the above results, [PERSON] et al. [2] conducted another similar work and proposed the overlapping neighborhood-based immune strategy, overlap neighborhood. The basic idea of this strategy is that among the neighboring nodes of overlapping nodes, there is a high probability of nodes with high degree, and immunizing these nodes can effectively control the spread of epidemics. Based on this, the process of the algorithm is as follows. First, use the overlapping community division algorithm to find overlapping nodes; then, find the neighbor nodes of all overlapping node and arrange them in descending order of node degree; then, the immune priority sequence of the network can be obtained.  
In general, the method of defining bridge nodes based on overlapping community is simpler and more intuitive, but it relies too much on overlapping community, so that such algorithms can only focus on the overlapping parts of the communities in the network and cannot measure the bridging effect of the vast majority of nodes, which cannot give enough target objects in tasks such as network immunity.
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 1 Introduction
### Related Work  
During the last few years, many research groups have investigated and evaluated the performance of C-V2X, focusing on what the packet reception ratio (PRR), packet collision ratio (PC), and resource allocation algorithms should be. In [16] the authors performed a comprehensive analytical investigation and analyzed the resource allocation and collision probability in Mode 4. [PERSON] et al. in [14] studied the S-SPS method and evaluated the impact of resource pool configuration and the Resource Reservation Interval (RRI) on the system performance in Mode 4. The authors in [15] proposed a \"lookahead\" technique to reduce resource collisions by identifying the next resource location. Different size and periodicity of packets with optimized parameters have been investigated in [21]. The authors of this paper have made some contributions related to this work. The authors proposed a novel algorithm of resource allocation named Estimation and Reservation of Resource Allocation (ERRA) in [22] as an alternative algorithm to the S-SPS scheduling method in C-V2X Mode 4. The authors have also extended this algorithm in [23] to be more practical for vehicles exiting or just entering the awareness range of a vehicle operating in Mode 4. In [24], the authors have proposed a novel technique for resource re-selection assisted by adaptive modulation and collision detection in C-V2X Mode 4. The novel technique is run and tested in different modulation and coding schemes and transmission powers. The authors of [25] introduced a comprehensive analysis related to the interaction between transmission power and the performance of scheduling radio resources in C-V2X Mode 4. Furthermore, they have proposed adaptive transmit power control to achieve a higher quality of service in different traffic scenarios. Based on the predefined positions of vehicles in the network, the network-controlled resource management and frequency reuse distances of vehicles being active in Mode 3 could be improved as shown in [26].  
The fuzzy logic-based algorithm and OpenFlow algorithm have been proposed in [27] as an SDN-based multi-access edge computing for the vehicular networks. In [28], the interference signals and quality of service in a highway scenario have been investigated by a system-level simulator. A distributed estimation mechanism to improve the scalability and robustness of vehicle connectivity under an RSU coverage area has been proposed in [29]. PRR and latency are the terms used in [30] to evaluate the proposed solution to maximize reuse distance scheduling for C-V2X Mode 3.
# Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition
###### Abstract  
Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor data with noise in a smart environment setting. Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity recognition. Deep learning algorithms have achieved promising results on balanced datasets, but their performance on imbalanced datasets without explicit algorithm design cannot be promised. Therefore, we aim to realise an activity recognition system using multi-modal sensors to address the issue of class imbalance in deep learning and improve recognition accuracy. This paper proposes a joint diverse temporal learning framework using Long Short Term Memory and one-dimensional Convolutional Neural Network models to improve human activity recognition, especially for less represented activities. We extensively evaluate the proposed method for Activities of Daily Living recognition using binary sensors dataset. A comparative study on five smart home datasets demonstrate that our proposed approach outperforms the existing individual temporal models and their hybridization. Furthermore, this is particularly the case for minority classes in addition to reasonable improvement on the majority classes of human activities.  
Activity recognition; Smart home; Imbalanced class; Joint learning; Temporal models +
Footnote †: journal: Applied Sciences
# Physics-Based Vehicle Simulation Using PD Servo
## 6 Experimental Results
### Simulation Using GPS Data  
Using global position system (GPS) data captured from a surveillance camera (a.k.a. dashboard camera), a traffic scene similar to a real situation can be generated without an input trajectory specified by user. Using the Kanade-Lucas-Tomasi (KLT) feature tracker, which is a local search using gradients weighted by an approximation to the second derivative of the image, a set of feature points is detected and tracked from a sequence of camera images [33]. For each frame, the 3D positions of the scene camera are estimated using the structure from motion (SFM) method that estimates 3D structure  
Figure 15: Various crash scenes.  
from the 2D image sequences by tracking feature points [34]. Using a least-squares fitting method, an input trajectory can be estimated from the GPS data extracted from dashboard camera and used to generate traffic scene, as shown in Figure 16. Compared to the real scene captured from the camera, the simulated scene shows convincing movements of vehicles in terms of physical properties such as velocity and direction.