esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Simulation Framework  
We develop a simulation framework at a single intersection to test the effectiveness of the proposed algorithm in this paper. The test intersection has a typical symmetrical four-leg configuration. The length of each approach and departure section is approximately 193.6 m. Each approach includes two incoming lanes with the specific allowable movements, where the left lane only serves left-turn traffic, and the right lane serves through traffic. The north- and southbound approaches are main roads, and the east- and westbound approaches are minor roads. This distinction allows for the consideration of different traffic priorities and behaviors at the intersection. The radius of the cooperative zone is set as 100 m. The simulation duration is 1 h. Other simulation parameter settings can be seen in Table 1.  
To investigate the sensitivity of the proposed algorithm, we consider three cases based on different traffic volumes. Table 2 summarizes the three cases tested in this study. Since our study focuses on environments with high penetration rates of AVs, for each case, four different penetration rates of AVs (i.e., 70%, 80%, 90%, 100%) are considered to evaluate the validity of the proposed algorithm. In addition, the simulation results in a 15 min period are compared to those under the no-control (NC) operation and fixed-time signal control (PSC) strategy.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Notation** & **Value** \\ \hline Common & & \\ \hline Radius of CZ (m) & \(R\) & 100 \\ Lane length (m) & \(L\) & 193.6 \\ Lane width (m) & \(W\) & 3.2 \\ Road speed limit (m/s) & \(V_{lim}\) & 20 \\ Vehicle length (m) & \(l\) & 5 \\ Minimum gap (m) & \(d_{m}\) & 5 \\ \hline AVs & & \\ \hline Desired spacing (m) & \(d_{ij}\) & 10 \\ Maximum speed (m/s) & \(\mathcal{V}_{max}\) & 18 \\ Minimum speed (m/s) & \(\mathcal{V}_{min}\) & 5 \\ Maximum acceleration (m/s\({}^{2}\)) & \(\mathcal{A}_{max}\) & 3 \\ Maximum deceleration (m/s\({}^{2}\)) & \(\mathcal{A}_{min}\) & 3 \\ Prediction horizon (s) & \(N_{p}\) & 5 \\ Time interval (s) & \(\delta\) & 1 \\ Weight & \(\omega_{1}\) & 2 \\ Weight & \(\omega_{2}\) & 1 \\ Weight & \(\omega_{3}\) & 1 \\ Weight & \(\omega_{4}\) & 1 \\ \hline MVs & & \\ \hline Maximum speed (m/s) & \(V_{max}\) & 18 \\ Maximum acceleration (m/s\({}^{2}\)) & \(a_{max}\) & 3 \\ Maximum deceleration (m/s\({}^{2}\)) & \(b_{max}\) & 4 \\ Reaction time of drivers (s) & \(\tau\) & 0.5 \\ Constant & \(\epsilon\) & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings.
## 1 Introduction
### Research Question  
After reviewing the current literature on the research topic, it can be claimed that financial institutions require a more effective system to analyze customers' preferences of usage of services and to determine loan approval based on a set of criteria. Developing this system to become more effective can potentially help businesses work in a more efficient manner. This research aims to identify important preference and approval factors, while taking hybrid factors and other related matters into consideration, in order to develop an effective model for leasing companies and businesses. The research question for this study is \"How can we create effective models for leasing businesses?\". The inputs used to answer this question were preference and approval factors, which were later grouped, allowing us to identify any potential correlation. The next step was to use a neural network to create different types of models. The results from the developed models' other tools were then compared. This research aims to answer other questions too, such as \"What are some customer requirements?\", \"What are customers interested in?\", \"What are some of the criteria that lending companies need to look at before approving a loan?\", \"Is there any relationship between different factors?\", \"What is the relationship between preference and approval factors?\" and \"What external factors are related?\". To answer these questions, this research developed preference, approval and hybrid decision models for lending businesses.  
The objectives of this study are to identify preference factors that affect a customer's decision of choosing a particular financial institution, to determine the important approval factors that providers need to take into consideration while approving loans and to identify any relationship between and among the factors. The study also aims to develop a prefer ence model that customers use when selecting services, to develop an approval model that providers use to approve credit requests and, finally, to develop a hybrid decision model to evaluate and improve outcomes.  
This research can beneficially contribute to the field of finance in multiple mays. Firstly, it will allow us to better understand preference factors that customers use when choosing a financial institution. It will also enable us to explore the approval factors that providers can use when approving a credit request. Furthermore, it will also help us understand the relationship that exists between the aforementioned factors. Another benefit of this research is that the developed models can increase the effectiveness of leasing companies, thereby improving customer satisfaction as well. So, leasing companies can use the developed models as a guideline to make decisions and implement policies.
## 5 Discussion  
### Influencing Factors for Model Generalization Ability  
In this study, we realized the inter-continental transfer of the model based on the fine-tuning method. The results show that with this method, only a very small number of samples are needed to achieve a rice recognition accuracy similar to that of the model trained with a large number of samples, showing the strength of the proposed method. However, although current research shows that the transferred model has a strong spatiotemporal generalization ability, research on the factors influencing this ability is still insufficient. Among the factors affecting the spatiotemporal generalization ability of the model, one is the similarity of features between crops and background features in the source and target domains. When the rice classification features in the target domain are similar to those in the source domain, the accuracy of model transfer tends to be higher (Figures 13 and 14). The result of this study indicates that the different transplanting dates of rice have a significant impact on the generalization ability of the transferred model (Figure 18). Nevertheless, there are still many other factors that affect the similarity and spatiotemporal generalization ability of the model, including sample factors (the number, distribution, and class proportions of samples), model factors (the type and architecture of the model, among others), image factors (acquisition time, quality, type, and processing method of remote sensing images, among others), and other factors (inter-annual/regional differences in rice phenology, rice planting mode, crop structure, and fragmentation of cultivated land). The influences of these factors are complex. Distinguishing the main influencing factors in a targeted and accurate manner, analyzing the influence mechanism of these influencing factors on the spatiotemporal generalization ability of the model, and developing suitable methods to improve the spatiotemporal generalization ability of the model are of great significance.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 9 Meeting the IoT-BCN Challenges: Tools, Techniques and Strategies
### Confidentiality through Encryption  
Encryption is a method of hiding data [38]. For enhanced confidentiality, a robust encryption algorithm such as AES that employs a 128-bit encryption key is mandated [82] for data encryption. Data in the cloud can be encrypted with the credentials of the data owners. Fine-grained data encryption in the cloud can be performed using attribute-basedencryption (ABE) as proposed by [PERSON] et al. [83]. It has been used on different occasions to secure data on the cloud. Studies such as [84; 85; 86] leverage a homomorphic crypto-system to encrypt locations and distance measurements harvested by such IoT. With statistical or machine learning based on IoT data, different privacy-preserving methods such as highlighted in [86; 87; 88] can be employed on IoT data. To generate machine learning models, federated machine learning models as specified by [PERSON] et al. [89] can be employed to avoid data transfer from end devices to the remote machines. Over-the-air communication is encrypted using a secret key exchanged between the nodes.
# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
###### Abstract  
Wireless multimedia sensor networks (WMSNs) are capable of collecting multimedia events, such as traffic accidents and wildlife tracking, as well as scalar data. As a result, WMSNs are receiving a great deal of attention both from industry and academic communities. However, multimedia applications tend to generate high volume network traffic, which results in very high energy consumption. As energy is a prime resource in WMSN, an efficient routing algorithm that effectively deals with the dynamic topology of WMSN but also prolongs the lifetime of WMSN is required. To this end, we propose a routing algorithm that combines dynamic cluster formation, cluster head selection, and multipath routing formation for data communication to reduce energy consumption as well as routing overheads. The proposed algorithm uses a genetic algorithm (GA)-based meta-heuristic optimization to dynamically select the best path based on the cost function with the minimum distance and the least energy dissipation. We carried out an extensive performance analysis of the proposed algorithm and compared it with three other routing protocols. The results of the performance analysis showed that the proposed algorithm outperformed the three other routing protocols.  
WMSN; energy efficiency; GA; multipath; routing; network lifetime +
Footnote †: journal: Sensors
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
## 3 Simulation and Measurement Results
### Results of High-Band Operation  
Figure 10 shows the simulated and measured results of the dual-band PS achieved by varying the \(f_{\rm H}\) band bias voltage from 0 V to 16 V and fixing the bias voltage at 0 V in the \(f_{\rm L}\) band. The PSR of the \(f_{\rm H}\) band was 114.097\({}^{\circ}\)\(\pm\) 6.076\({}^{\circ}\), and the phase at the \(f_{\rm L}\) band was maintained at 0.360\({}^{\circ}\)\(\pm\) 1.035\({}^{\circ}\). The measured ILs were smaller than 1.867 dB and 1.983 dB, and RLs were higher than 22.550 dB and 16.833 dB within the two bands.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Bias Voltage [V]** & **PSR [\({}^{\circ}\)]** & **Phase [\({}^{\circ}\)]** & **PD [\({}^{\circ}\)]** & **Max. IL [dB]** & **Min. RL [dB]** \\ \(f_{\rm L}\)/\(f_{\rm H}\)) & **@\(f_{\rm L}\)** & **@\(f_{\rm H}\)** & **\(f_{\rm L}\)/\(f_{\rm H}\))** & **@\(f_{\rm L}\)/\(f_{\rm H}\))** & **@\(f_{\rm L}\)/\(f_{\rm H}\))** \\ \hline
0 to 16/2.5 & 114.067 & 60 & \(\pm\)8.396/\(\pm\)0.342 & 1.580/1.989 & 19.669/21.077 \\
0 to 16/16 & 114.494 & 114 & \(\pm\)9.465/\(\pm\)0.667 & 1.414/1.419 & 19.695/25.047 \\ \hline \end{tabular}
\end{table}
Table 4: Measured results of the dual-band PS achieved by varying the bias voltage of the low-band while fixing the bias voltage of the high-band.  
Figure 9: Simulated and measured results of a dual-band PS achieved by varying the bias voltage of the low\(-\)band while fixing the bias voltage of the high-band at 0 V: (**a**) PSRs; (**b**) ILs and input/output RLs.  
Table 5 presents the measured results when the bias voltage of the varactor diode at the \(f_{\rm{H}}\) band was varied and fixing the bias voltage was fixed at 2 V and 16 V at the \(f_{\rm{L}}\) band. As seen from Table 5, the \(f_{\rm{H}}\) band achieved a nearly identical PSR while the phase of the \(f_{\rm{L}}\) was constantly maintained. The measured ILs were smaller than 1.9 dB, and RLs were higher than 16.7 dB.
# On Connectivity and Energy Efficiency for Sleeping-Schedule-Based Wireless Sensor Networks
###### Abstract  
Based on the connectivity and energy consumption problems in wireless sensor networks, this paper proposes a kind of new network algorithm called the connectivity and energy efficiency (CEE) algorithm to guarantee the connectivity and connectivity probability, and also to reduce the network energy consumption as much as possible. Under the premise that all sensors can communicate with each other in a specific communication radius, we obtained the relationship among the connectivity, the number of sensor nodes, and the communication radius because of the theory of probability and statistics. The innovation of the paper is to maximize the network connectivity and connectivity probability, by choosing which types of sleeping nodes to wake up. According to the node's residual energy and the relative value of distance, the algorithm reduces the energy consumption of the whole network as much as possible, and wakes up the number of neighbor nodes as little as possible, to improve the service life of the whole network. Simulation results show that this algorithm combines the connectivity and the energy efficiency, provides a useful reference value for the normal operation of the sensors networks.  
wireless sensor networks; connectivity; energy efficiency; power allocation +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Characterization of La-EPRC Particles for Static Adsorption of Phosphorus Removal
#### 3.2.3 Effect of Adsorbent Dosage on Phosphorus Removal  
Figure 13 illustrates that the unit adsorption capacity of the adsorbent gradually decreases with increasing La-EPRC dosage. This is primarily due to the heightened adsorbent dosage resulting in an increase in adsorption sites, while the phosphorus concentration in the solution remains constant, leading to a reduction in phosphorus adsorption per unit mass of La-EPRC particles. Conversely, the adsorption removal rate increases with higher adsorbent dosage. For instance, at a La-EPRC dosage of 3 g, the highest removal rate surpasses 98%, and further dosage increases show minimal impact on the removal rate. This is indicative of near-complete removal of phosphorus from the solution at this point.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature**} & \multicolumn{4}{c}{**PFO Adsorption Kinetics**} & \multicolumn{4}{c}{**PSO Adsorption Kinetics**} \\ \cline{2-9}  &
\begin{tabular}{c} **Initial Phosphorus** \\ **(Chemistry)** \\ \end{tabular} & \multicolumn{1}{c}{\(\mathbf{q_{e}}\)} & \multicolumn{1}{c}{\(\mathbf{K_{1}}\)} & \multicolumn{1}{c}{\(\mathbf{R^{2}}\)} & \multicolumn{1}{c}{\(\mathbf{Initial Phosphorus}\)} & \multicolumn{1}{c}{\(\mathbf{q_{e}}\)} & \multicolumn{1}{c}{\(\mathbf{K_{2}}\)} & \multicolumn{1}{c}{\(\mathbf{R^{2}}\)} \\ \hline \multirow{4}{*}{15 \({}^{\circ}\)C} & 10 & 0.8561 & 0.0882 & 0.9875 & 10 & 1.0389 & 0.09038 & 0.9908 \\  & 15 & 1.3376 & 0.0772 & 0.9939 & 15 & 1.6457 & 0.04879 & 0.9972 \\  & 20 & 1.7768 & 0.0954 & 0.9870 & 20 & 2.1316 & 0.04902 & 0.9945 \\  & 10 & 1.2312 & 0.0662 & 0.9947 & 10 & 1.5382 & 0.00455 & 0.9957 \\  & 15 & 1.8674 & 0.0567 & 0.9957 & 15 & 2.3984 & 0.02247 & 0.9951 \\  & 20 & 2.3163 & 0.0768 & 0.9889 & 20 & 2.8273 & 0.02914 & 0.9972 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Adsorption kinetics fitting results at different temperatures.  
Figure 12: Results of adsorption kinetics fitting at 35 \({}^{\circ}\)C. (**a**) Results of fitting PFO adsorption kinetics. (b) Results of fitting PSO adsorption kinetics.
## 4 Main Results
### Controller Design  
Previously, the convergence of the prediction stage was ensured. Thus, designing the controller based on the estimated variables is possible. Therefore, in what follows, the stability conditions based on the observer for the closed-loop system are stated.  
Theorem 4.3: _Let us consider the system (13) or (17) and the observer scheme presented in Figure 2 with an adequate convergence (Theorems 1 and 2, respectively). Then there is a control \(C(s)\) of type PID such that the closed loop system \(Y(s)/R(s)\) is stable if and only if \(\tau<\frac{1}{a}-\frac{1}{b_{1}}+\sqrt{\frac{1}{a^{2}}+\frac{1}{b_{1}^{2}}}\)._  
Proof.: The adequate convergence of the observer is provided by Theorems 1 and 2: \(\tau<\frac{1}{a}-\frac{1}{b_{1}}+\sqrt{\frac{1}{a^{2}}+\frac{1}{b_{1}^{2}}}\) for systems (13) or (17), respectively. Additionally, by using the separation principle for linear observers, a controller can be designed by using the estimated variable \(\hat{y}(t+\tau)\) as if the variable were taken from the original system. Therefore, a PID controller \(C(s)\) stabilizing the transfer function without delay, \(G(s)\), for both cases (13) or (17) can be found (see Lemma 2). Therefore the most restrictive condition between the convergence and control stages is the convergence stage, from which the conclusion of the theorem follows.
## 4 Data Analysis
### Development of Structural Models
#### 4.3.1 Measurement Model
Figure 5: Normal probability curve of Cronbach alpha. Figure 6: Normal probability curve of composite reliability. Recently, the heterotrait-monotrait ratio of correlations (HTMT) method for assessing discriminant validity has been developed. HTMT is the average correlation between heterotrait and heteromethods compared with the average correlation between monotrait and heteromethods [89]. Table 7 shows the HTMT analysis output based on the requirement that every construct have a value lower than 0.900. Below 0.85 is highly recommended. Table 8 shows Cross Loadings for discriminant validity calculations. According to Cross loadings, a specific item should have greater loadings on its own parent construct than other constructs in the research. If an item loads well onto another construct relative to its own parent construct, discriminant validity difficulties exist. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Constructs**} & **Database** & **Management** & **Operational** & **Privacy** & **Technology** \\  & **Barriers** & **Barriers** & **Barriers** & **Barriers** & **Barriers** \\ \hline Database Barriers & & & & & \\ Management Barriers & 0.290 & & & & \\ Operational Barriers & 0.191 & 0.132 & & & \\ Privacy Barriers & 0.313 & 0.204 & 0.241 & & \\ Technology Barriers & 0.504 & 0.252 & 0.175 & 0.497 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Fornell linker criterion for discriminant validity. Figure 7: Normal probability curve of AVE.
# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
## 3 Proposed Approach
### Image Reconstruction and Autoencoder  
The encoder \(ENC_{S}\) and decoder \(DEC_{S}\) constitute an autoencoder for the source domain \(X^{S}\). The \(ENC_{S}\) maps an input image \(x^{S}\in X^{S}\)to a code in a latent space and based on this code, the \(DEC_{S}\) reconstructs the input image as \(x^{S\to S}\). Similarly, \(ENC_{T}\) and \(DEC_{T}\) constitute an autoencoder for the  
Figure 1: Overview of our model architecture. \(x^{S}\) and \(x^{T}\)are images from the source and target domain. The encoder \(ENC_{S}\) and \(ENC_{T}\) are two sequences of convolution layers (including Resnet blocks [33]) that map images to a code in a higher level latent space, \(DEC_{S}\) and \(DEC_{T}\) are two sequences of de-convolution layers (including Resnet blocks) that generate images from the outputs of the encoder. The Discriminator \(D_{S}\) and \(D_{T}\) determine whether an image is real or synthesized. During training, we share the weights of the two encoders except for the first and the last layer. Similarly, the weights of decoders are also tied except the first and the last layer. \(DEC_{S}\left(ENC_{S}\left(x^{S}\right)\right)\to x^{S\to S}\) and \(DEC_{T}\left(ENC_{T}\left(x^{T}\right)\right)\to x^{T\to T}\) are reconstructed images. \(DEC_{S}\left(ENC_{T}\left(x^{T}\right)\right)\to x^{T\to S}\) and \(DEC_{T}\left(ENC_{S}\left(x^{S}\right)\right)\to x^{S\to T}\) are style-transferred images. \(C\) is the classifier trained by the source images and the style-transferred source images.  
target domain \(X^{T}\). The aim of these two autoencoders is to reconstruct images as similar as possible to their input images in each domain. We use the mean squared error as the loss function to penalize the differences between inputs and outputs:  
\[\mathcal{L}_{REC_{S}}(ENC_{S},DEC_{S}) =\mathbb{E}_{X^{S}}[\frac{1}{k}\|x^{S}-DEC_{S}(ENC_{S}(x^{S}))\|_{2}^ {2}] \tag{1}\]  
\[\mathcal{L}_{REC_{T}}(ENC_{T},DEC_{T}) =\mathbb{E}_{X^{T}}[\frac{1}{k}\|x^{T}-DEC_{T}(ENC_{T}(x^{T}))\|_{2 }^{2}] \tag{2}\]  
where \(k\) is the number of pixels in input \(x^{S}\) and \(\|\cdot\|\) is the squared \(L_{2}\)-norm.
# A Task-Centric Cooperative Sensing Scheme for Mobile Crowdsourcing Systems
## 5 Performance Evaluation
### Effects of Variations of Spatial Correlations  
A key assumption made by the TCPS and IPSA algorithms is that the spatial correlation matrix **B** estimated using training data will remain unchanged during normal operation (testing data). If this assumption is correct, then the approximation error computed using training data should have similar magnitudes as the approximation error computed using testing data.  
To test this theory, we plot approximation error for each day of training and testing period. The day-by-day approximation error is calculated using the formula:  
\[\varepsilon\left(\lambda\right)=\frac{\left|\left|\mathbf{Y}_{K-R}\left( \lambda\right)-\mathbf{B}\mathbf{Y}_{R}\left(\lambda\right)\right|\right|_{F}} {\left|\left|\mathbf{Y}_{K-R}\left(\lambda\right)\right|\right|_{F}} \tag{18}\]  
where \(\mathbf{Y}_{K-R}(\lambda)\), and \(\mathbf{Y}_{R}(\lambda)\) are the sensing measurements of the \(\lambda^{\text{th}}\) day of the idle participants and active participants respectively, and **B** is the spatial correlation matrix estimated based on \(\left|\mathbf{Y}(\lambda)\right|\); \(\lambda=1\), 2, and 3. We use 11-fold cross validation to examine the approximation errors of the 11 days' worth of Intel-Berkeley Lab sensing data.  
In Figure 6a, we set the training loss threshold as 0.025. It yields 10 active participants according to the IPSA algorithm. In Figure 6b, a different loss threshold of 0.015 is used and 15 participants will be active. In Figure 6c, the training loss threshold is set to 0.005 and 20 participants will be active.  
Figure 4: Number of active participants _vs._ loss threshold.  
Figure 5: Number of participated tasks for each participant within 8 days.  
From the results in these figures, it is clear that the testing approximation error on different day is very stable. When more participants are active and the training loss threshold is set lower, the testing approximation error is lower. Thus, the 11-fold cross validation of Intel-Berkeley Lab sensing data proves that for a given approximation error we can adjust approximation error to approach it by selecting the appropriate number of active participants and training loss threshold.  
Figure 6: Testing approximation errors of IPSA. (**a**) 10 active participants (training loss threshold = 0.025); (**b**) 15 active participants (training loss threshold = 0.015); (**c**) 20 active participants (testing loss threshold = 0.005).