esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Using a Spread Spectrum Based on Turbo Equalization
#### RAKE-Based Turbo Equalization Method with Threshold and Weighted Coefficient  
The MRC method in the aforementioned RAKE-based turbo equalization technique overlaps input signals from multiple branches to obtain the optimum performance and synchronizes them before combining. The combining effect is maximized by making the contribution small for values whose correlation value is small--i.e., an extreme fading signal--and making the contribution large for values whose correlation value is large--i.e., when there is no time delay. In the MRC method, small value of correlation become self-noise among the output values in each of the taps. These self-noise values adversely affect the RAKE process. As a result, they can cause performance degradation of the receiver. This problem can be solved by giving the threshold value, which is threshold comparison based on received signal power of each of the taps. Therefore, noise is reduced from the sum of the signals by removing self-noise values which are smaller than the threshold value, by assigning the threshold value to the output value of each of the taps, thereby improving the performance. It is very important how the threshold value decide because this vale impact on performance directly. Before actual underwater experiment, we simulated transceiver model as shown in Figure 1, based on bit error performance in accordance threshold values, we found optimal threshold value of 0.2.  
In addition, the performance of the RAKE receiver can be improved further using the weighted coefficient. Figure 2 shows the RAKE receiver with a weighted coefficient and threshold value. The weighted coefficient plays a role in reducing self-noise at the RAKE receiver by making values whose correlation is large larger and making values whose correlation is small smaller for signals input to multiple branches in the RAKE receiver, and enlarging the combining effect in the MRC phase. The weighted coefficient is applied prior to the threshold process, and the weighted coefficient \(c_{i}(n)\) can be expressed as follows:  
\[c_{i}(n)=z_{i}^{2}(n)/\sum_{i=1}^{L}z_{i}^{2}(n)\;i=1,\;2,\;3,\cdots,L \tag{1}\]\(z_{i}(n)\) refers to an output value from each of the branches and \(L\) refers to a size as much as \(T_{c}\). After calculating the weighted coefficient \(c_{i}(n)\) followed by multiplication with \(z_{i}(n)\), a correlation value output from the branch, the threshold process is carried out to combine the values.
# Towards a Fast, Open-Path Laser Hygrometer for Airborne Eddy Covariance Measurements
## 4 Conclusions and Outlook to the Airborne Application  
The novel concept of an airborne eddy correlation dTDLAS hygrometer with a thin, planar, rotationally symmetric multipass cell in an open-path configuration offers several benefits, such as small gas transit times, no sampling errors and high temporal resolution, particularly in combination with a calibration-free, first-principles data evaluation. Airborne water vapor flux measurements using the EC method would greatly benefit from this proposed instrument. The measurement uncertainty of this laser hygrometer is estimated to be less than 4.5% (k = 1), with the largest individual contributors being the spectral line data (65.7%) and the fitted line area (26.8%). The first step toward an airborne instrument was made by validating our newly designed, compact, 90\({}^{\circ}\)-angled optics setup under static conditions by comparison with an SI-traceable dew point mirror hygrometer. The linear regression between the uncalibrated first-principles dTDLAS and the calibrated DPM showed, over the range from 1300 to 11,500 ppm, an average deviation of the correlation slope from unity of 2.6%. The maximal relative error was 2.2%. The spectroscopic performance was evaluated by analyzing two absorbance profiles at low (1321 ppm) and high (11,542 ppm) amount fractions. Averaging 10 subsequently recorded profiles, the signal-to-local noise ratio is 1306 and 13,430 at 1321 ppm and 11,542 ppm, respectively. The NEC is approximately 1 ppm at 1321 ppm and 11,542 ppm, which can be used as a first estimate of the precision of the instrument. The high precision combined with the good agreement, with a traceable reference without any calibration, indicates that the designed optics setup will be suitable for airborne EC measurements.
## 4 UAV Path Planning with Cooperative Detection Using BLSSPIO
### Simulation
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{Start Position (km)} & End Position (km) \\ \hline \multirow{4}{*}{UAVs States} & UAV\({}_{1}\) & (5, 5, 3) & (85, 85, 3) \\  & UAV\({}_{2}\) & (5, 10, 3) & (85, 90, 3) \\  & UAV\({}_{3}\) & (10, 5, 3) & (90, 85, 3) \\  & UAV\({}_{4}\) & (10, 10, 3) & (90, 90, 3) \\ \hline \multirow{4}{*}{Detection Units} & Position (km) & Radius (km) & Communication Range (km) \\ \hline \multirow{4}{*}{\begin{tabular}{c} Detection \\ Units \\ \end{tabular} } & type I-1 & (45, 25) & 15 & 60 \\  & type I-2 & (60, 54) & 10 & 50 \\  & type I-1 & (40, 70) & 7 & 30 \\  & type II-2 & (72, 42) & 8 & 30 \\  & type II-3 & (25, 45) & 7 & 30 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Mountains \\ Mountains \\ \end{tabular} } & \multicolumn{2}{c}{Position (km) \((8^{\prime}_{e},R^{\prime}_{e})(\text{km})\)} & Height (m) \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Mountains \\ Mountains \\ \end{tabular} } & Mountain\({}_{1}\) & (67, 80) & (8, 5) & 60 \\  & Mountain\({}_{2}\) & (85, 70) & (8, 5) & 50 \\  & \(S_{\text{min}}\) & 1 & \(L_{\text{max}}\) & 300 \\  & \(V_{\text{max}}\) & 400 km/h & \(V_{\text{min}}\) & 300 km/h \\  & \(R_{\text{min}}\) & 1.2274 km & \(T_{\text{max}}\) & 1 h \\  & \(T_{\text{step}}\) & 0.0066 h & \(T_{\text{start}}\) & 0 h \\  & \(T_{\text{end}}\) & 0.9383 rad \\ \multirow{4}{*}{Constraints} & \(T_{\text{end}}\) & 0.33 h & \(\frac{\text{max}}{V_{\text{max}}}\) & (\(-233.90\), 233.90) km/h \\  & \(T_{\text{start}}\) & 342.85 km/h & \(\frac{\text{max}}{V_{\text{max}}}\) & (\(-233.90\), 233.90) km/h \\  & \(d_{\text{aft/c}}\) & 1 km & \(r_{\text{startmin}}\) & 15 km \\  & \(N\) & 50 & & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Other \\ Parameters \\ \end{tabular} } & \(r_{1}\) & 1.01 & \(r_{2}\) & \(1.25\times 10^{-18}\) \\  & \(\mu^{\prime}\) & 2.4637 & \(\sigma\) & 0.05 \\ \cline{1-1}  & \(p_{\text{lin}}^{\prime}\) & 0.8 & \(p_{\text{lin}}\) & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of multi-UAV path planning. Figure 6: Multi-UAV cooperative path planning. are excessively dispersed, an increase in length cost is inevitable. It is also challenging to satisfy the coordination constraints if the communication distances between UAVs are too far. Therefore, path crossovers are essential to some extent. To assess the performance of the proposed BLSSPIO, comparison tests are also conducted on the PIO, BLSSPIO, and CFPSO. As shown in Figure 8, the BLSSPIO outperforms the others in both convergence speed and optimal solutions. For UAV\({}_{1}\)-UAV\({}_{4}\), the cost values of the BLSSPIO are 123.88, 119.88, 122.88, 121.88, respectively. The conclusion is consistent with the previous analysis mentioned above. In conclusion, the BLSSPIO effectively addresses the cooperative path-planning
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
###### Abstract  
A mobile ad hoc network (MANET) is a group of nodes constituting a network of mobile nodes without predefined and pre-established architecture where mobile nodes can communicate without any dedicated access points or base stations. In MANETs, a node may act as a host as well as a router. Nodes in the network can send and receive packets through intermediate nodes. However, the existence of malicious and selfish nodes in MANETs severely degrades network performance. The identification of such nodes in the network and their isolation from the network is a challenging problem. Therefore, in this paper, a simple reputation-based scheme is proposed which uses the consumption and contribution information for selfish node detection and cooperation enforcement. Nodes failing to cooperate are detached from the network to save resources of other nodes with good reputation. The simulation results show that our proposed scheme outperforms the benchmark scheme in terms of NRL (normalized routing load), PDF (packet delivery fraction), and packet drop in the presence of malicious and selfish attacks. Furthermore, our scheme identifies the selfish nodes quickly and accurately as compared to the benchmark scheme.  
mobile ad hoc networks; dynamic topology; multipath routing; secure routing; reputation ratio; trust management; selfish node; malicious node +
Footnote †: journal: Electronics  
0022
# Responses of Water Use Efficiency to Drought in Southwest China
## 2 Materials and Methods  
### Study Area  
Southwest China (97.5-110.2\({}^{\circ}\)E, 21.1-34.3\({}^{\circ}\)N) is mainly composed of Sichuan, Chongqing, Yunnan, and Guizhou provinces, and the total land area is 1,138,700 km\({}^{2}\). The natural divisions mainly consist of the Sichuan Basin, Western Sichuan Plateau, Yunnan-Guizhou Plateau, and the west of Liang-Guang Hills. The main vegetation types of Southwest China are forests, shrublands, grasslands, and croplands (mainly including rice, corn, rape, flue-cured tobacco), which account for 18.87%, 16.83%, 22.25%, and 25.16% of the land area, respectively. Croplands are mainly distributed in the Sichuan Basin and Zhong-Shan hilly area of Guizhou province; the high-elevation areas, such as the western SichuanPlateau, are mainly covered by grasslands; most of southern Sichuan and Yunnan are dominated by forests (Figure 1a).  
Southwest China is characterized by a large elevation gradient: The elevation of the Sichuan Basin and its surrounding areas as well as eastern Guizhou is less than 1000 m; the elevation of southern Sichuan and Northwest Yunnan is between 1000 and 2000 m; and the elevation of the western Sichuan Plateau is above 3500 m (Figure 1b). Air temperature and precipitation exhibit large variations across the region. The annual mean temperature is higher than 10 \({}^{\circ}\)C in the eastern region and is lower than 0 \({}^{\circ}\)C in the western Sichuan Plateau (Figure 1c). The annual precipitation is relatively high (\(>\)1000 mm) in the eastern and southern regions and much lower in the central and northern regions (Figure 1d).  
Figure 1: The land cover, elevation, and climate of Southwest China: (**a**) land cover types; (**b**) elevation; (**c**) mean annual temperature (MAT); (**d**) mean annual precipitation (MAP).
# MAC Address Anonymization for Crowd Counting
## 2 Results
### Requirement 4: A Collision Rate of Less Than \(10^{-9}\) for \(10^{7}\) MAC Addresses
#### 2.4.3 Proving Requirement 4 Is Satisfied  
We have \(m=2^{64}\simeq 1.84\times 10^{19}\), which means that we truncate SHA-256 hashes to 64 bits. This corresponds to a load factor \(\alpha=10^{7}(1.84)^{-1}10^{-19}\simeq 10^{-12}\) for \(n=10^{7}\) MAC addresses. Figure 4 then shows that the collision rate expectation is approximately equal to \(10^{-12.5}\). Note that, for \(\alpha\) sufficiently low (e.g., \(\alpha\leq 10^{-3}\)), the approximation becomes (4), which explains why the level sets in Figure 4 appear to be linear slopes.  
We point out that approximation errors are negligible for our choice of parameters. Our load factor \(\alpha\simeq 10^{-12}\) implies (for any \(K\geq 2\)) \(|R_{K-1}(\alpha)|\leq 10^{-24}\). Moreover, as already pointed out, \(m\geq 2^{64}\implies|\delta(\alpha,n)|\leq 5\times 10^{-20}\).  
The conclusion is that our estimate of the collision rate expectation is approximately equal to \(10^{-12.5}\), with an error upper bounded by \(5\times 10^{-20}+10^{-24}\simeq 5\times 10^{-20}\), so that Requirement 4 is met.
# Sentiment Analysis Based Requirement Evolution Prediction
## 4 Evaluation and Experimental Results
### Experimental Results
The results of both the joint model and HASM show that the discovered feature-sentiment hierarchy can identify the sentiments for specific features and the associated opinions. We can see that the joint hierarchical model outperformed unsupervised HASM without any hand-engineered features. This may be mainly because of the distinct characteristics of sentiment classification. The LSTM-based recurrent neural network architecture was able to consider intra-sentence relations and provided valuable clues for the sentiment prediction task, while the HASM model, each feature or sentiment polarity as a distribution of words. This result confirms that the joint model was capable of analyzing sentiment for certain features at different granularities. These automatically-identified features with their sentiment orientation are important for understanding how users evaluate the product, which might contain valuable information for the feature requirement evolution and maintenance of the product. To provide the experimental results as an example of the hierarchical product feature requirements presented in the user review, Table 1 is an example of the three-level hierarchical requirement features with sentiment orientation by combining RNN with HLDA. Figure 4: The feature-dependent sentiment analysis comparison under the joint model and ASUM. The result presented different granularity information for the hierarchical organization of features and sentiments. We can see that the produced hierarchical structure showed certain features and corresponding sentiments at various granularities. The hierarchical nodes showed the requirement features at various granularities with different feature-dependent sentiments. The sentiment word changed its polarity according to the specific feature. This hierarchical structure of feature-sentiment shows that general features became specific features of the product as the depth increased. The hierarchical features were relevant in the sense that they covered important features at different granularities of the product applications. The result confirms that the identified product requirement features were organized into a hierarchical structure from general to specific. Requirement feature-dependent sentiment polarities were obtained mainly because of RNN-based sentence-level sentiment classification being fed into the hierarchical topic model. These evaluations and experimental results verify the effectiveness of the hierarchical feature requirements evolution prediction. The qualitative results show that the features automatically identified from review text described the overall requirement change of the products. As a result, we conclude that the combination of the supervised deep recurrent neural network and unsupervised hierarchical topic model is an effective approach to requirement evolution identification from users' review text. The above experimental results and analysis show that our proposed method is efficient, feasible, and practical.
## 6 Conclusions  
In conclusion, this paper presented an innovative automated vision-based system for detecting falls and issuing instant alerts upon detection. By utilizing a fine-tuned human segmentation model and image fusion technique as pre-processing and classifying sets of live footage with a 3D multi-stream CNN model (4S-3 DCNN), the system can effectively detect when a monitored human experiences a sequence of falling followed by fallen. By applying human segmentation pre-processing, the system can isolate the human appearance in 32 input frames and use three-level image fusion to create four fused images that capture movement features across the consecutive frames. The 4S-3 DCNN architecture has four branches, each corresponding to one of the pre-processed images. Each branch is responsible for extracting and learning features from a different set of consecutive spatial and temporal information. The model classifies the 32 frames into one of four classes, including Standing, Falling, Fallen, or Others. If there is a sequence reporting Falling followed by Fallen, the system alerts of a human fall situation. The system's effectiveness was assessed using the publicly available Le2i dataset, achieving impressive accuracy, sensitivity, specificity, and precision values of 99.44%, 99.12%, 99.12%, and 99.59%, respectively. Therefore, this proposed system has the potential to be a valuable tool for detecting human falls, preventing fall injury complications, and reducing healthcare and productivity loss costs.  
Despite its effectiveness in detecting human falls, the proposed method has some limitations. Firstly, it can only detect falls in scenes that have one person without any localization of the person within the video. Additionally, the experiments conducted to evaluate the method's performance were only conducted on the Le2i fall detection dataset, which may not represent real-world scenarios accurately. In future research, we aim to explore the performance of this method on various publicly available datasets, such as MCFD and URFD, to enhance its generalizability and effectiveness in real-world settings. Additionally, we will consider integrating comparisons with wearable sensor-based methods to provide a broader perspective on the problem.  
Conceptualization, methodology, software, investigation, resources, formal analysis, and validation, T.A., K.B. and G.M.; data curation, T.A. and K.B.; writing--original draft preparation, T.A.; writing--review and editing, supervision, project administration, and funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
The work was funded by Researchers Supporting Project number (RSP2023R34), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors extend their appreciation to Researchers Supporting Project number (RSP2023R34), King Saud University, Riyadh, Saudi Arabia.
## 4 Results and Discussion
### Conceptual Framework and Validation
#### Hypothesis 2  
The hypothesis that the lack of continuous involvement and capabilities positively contributes to the lack of collaboration and model integration was supported. Collaboration is the precondition to BIM implementation because the major stakeholders need to communicate and exchange information with others to complete their scopes of work. The early and active involvement of the downstream stakeholders is critical to building trust and sharing knowledge with others, which facilitates the project-wide collaboration needed for multidisciplinary model integration [53]. Thus, the lack of continuous stakeholder participation (H12) prevents the team from working collaboratively (H27). On the other hand, the collaboration between the design team and the construction team will not intrinsically lead to a blending of disciplines [16]. In the Singapore context, even in the construction phase, there was generally insufficient collaboration between the design consultants and the contractors. The communication between the designers and site managers was weak. Under the current contractual framework of Singapore, the relationships tend to be adversarial. The constant updating and management of broadly accessible digital information would potentially subject the designers and the contractors to increased liabilities as the project proceeds [26]. Therefore, the lack of proper assignment of responsibilities and allocation of risks (H32) would point to the need for collective risk management and model management (H28, H33, and H34). In addition, plenty of resources upfront are also needed to create and fix the model early (H36). Thus, the lack of capital resources (H31 and H42) creates a difficulty in the model integration (H43, H45, and H46). Moreover, the staff may unconsciously evaluate whether their knowledge, skills, and experience are good enough to enable them to be involved in the BIM-based work practices. If they regard themselves as less competent and experienced individuals in working with BIM (H07 and H14) or have not yet learnt about similar success stories, they would not actively use BIM (H09). Because of human nature, the negative mindset and passive behavior may in turn keep their inertia to continuously learn about relevant processes (H35). This would result in fragmented or discipline-specific BIM implementation, rather than project-wide collaboration and multidisciplinary design modeling and integration. In the post-survey interviews, the experts also emphasized the importance of building a high level of capabilities in the project team, as experienced and skilled personnel that can lead BIM modeling teams are still lacking in the current market in Singapore.
## 4 Conclusions  
Some adverse effects were found after the exposure to FOS (_D. rerio_: hatching, behavior, and biochemical responses; _E. cryptic_: reproduction and growth). Answering to the main question of the current study, FOS may not be considered safe for _D. rerio_ and _E. cryptic_ for concentrations higher than the PECs described for soil (e.g., 1.067 mg/kg) and for surface water (e.g., 0.06496 mg/L). For a better evaluation of the risk, the obtained results showed the importance of assessing the short- and long(er)-term effects of different pesticides and their metabolites (as is the case of phosphonic acid) to various non-target organisms. Additionally, a multi-endpoint approach, with standard (described by the guidelines) and extra endpoints, is highly recommended for further studies. Multigenerational studies might also be required to address the potential endocrine-disrupting properties of FOS and phosphonic acid in non-target organisms (this is a data gap).  
Conceptualization: A.B., J.S. and V.L.M.; methodology: A.B., J.S. and V.L.M.; formal analysis: [PERSON], [PERSON] and [PERSON]; investigation: [PERSON], [PERSON] and [PERSON]; resources: [PERSON] and [PERSON]; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON], [PERSON], [PERSON] and [PERSON]; project administration: [PERSON]; funding acquisition: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the project UNRAvEL (POCI-01-0145-FEDER-029035) financed by FEDER, through COMPETE 2020--POCI, PT 2020, and by national funds (OE), through FCT/MCTES national funds (PIDDAC). FCT/MEC (Fundacao para a Ciencia e Tecnologia/ Ministerio da Ciencia e Tecnologia e Ensino Superior) did the financial support to CESAM (Centro de Estudos do Ambiente e do Mar) (UIDP/50017/2020+UIDB/50017/2020) via national funds. [PERSON] and [PERSON] have a fellowship and a contract researcher from the project (POCI-01-0145-FEDER-029035), respectively. [PERSON] is funded by national funds (OE), through FCT, in the scope of the framework contract foreseen in the numbers 4, 5, and 6 of the article 23, of the Decree-Law 57/2016, of 29 August, changed by Law 57/2017 of 19 July.  
The experiments were in accordance with the current laws of the country in which they were performed. Zebrafish Facility complies with the Portuguese law (Portaria 1005/02 and Portaria 1131/97), which follows the European Guideline 86/609/EC, and with the Council Regulation No. 2005/01 EC. It also follows the FELASA (Federation of European Laboratory Animal Science Associations) guidelines and recommendations concerning animal welfare and the design and conduct of research projects in which animals are used. Animal housing, welfare, and experimentation were under constant monitoring from the veterinary service.  
Not applicable.  
The data are available on request from the corresponding author.  
The authors declare no conflict of interest.