esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Fidelity of NS-3 Simulations of Wireless Multipath TCP Connections
## 6 Results
### Downlink MPTCP Throughput  
The downlink experiments are performed by allowing the wireless nodes to download a 20 MB file from a single download server simultaneously over the LTE and Wi-Fi networks. Figure 9 depicts the downlink throughput performance of MPTCP in Setup 1. The results reveal that there is generally a match between the hardware and ns-3 simulator results, except for wVegas, where the simulator gives around 25% larger throughput compared with the testbed.  
Figure 8: Setup 2 Uplink MPTCP Throughput for 75 MB flows.  
Figure 7: Setup 2 Uplink MPTCP Throughput for 20 MB flows.  
Figure 10 reveals that the MPTCP downlink throughput of the testbed in Setup 2 is greater than the simulator (by around 10-15%) for all algorithms except wVegas, where there is a close match. However, both the simulator and the testbed show the same throughput performance trend among the studied congestion control algorithms.  
In summary, the simulator throughput results match the testbed results when no bottleneck exists in the network configuration for all the studied algorithms except with wVegas, where the simulator results are higher than its testbed counterpart. On the contrary, wVegas throughput simulation results match the testbed ones when a bottleneck link exists, whereas the simulator slightly underestimates the throughput results of the rest of the algorithms.
# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 4 Experiments
### Sensitivity Analysis of Hyperparameters (RQ3)  
In this part, we make a very thorough analysis of the influence of hyperparameters on the proposed method in the e-government dataset. Specifically, we investigate the impact hyperparameters of the learning rate and batch size. As can be seen from Table 5, the learning rate affects the performance of our model. This suggests that a lower learning rate might achieve a better result. Specifically, Recall@5, Precision@5, and F1@5 achieve the best results when the learning rate is 0.001. Next, we discuss the impact of batch size on NMCF. It can be seen from Table 6 that a lower batch size might achieve a better NDCG@5. Recall@5, precision@5, and F1@5 achieve the best result when the batch size is 1024.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Recall@5**} & \multicolumn{3}{c}{**Precision@5**} & \multicolumn{3}{c}{**NDCG@5**} \\ \cline{2-11}
**MF** & **UCF** & **SVD** & **TBPR** & **MF** & **UCF** & **SVD** & **TBPR** & **MF** & **UCF** & **SVD** & **TBPR** \\ \hline
**231.49** & 473.28 & 167.70 & 326.00 & 22.61 & 199.91 & 75.98 & 3.90 & 129.88 & 444.69 & 146.34 & 60.75 \\
**<0.0005** & \textless{}0.0005 & \textless{}0.0005 & \textless{}0.0005 & \textless{}0.0005 & \textless{}0.0005 & \textless{}0.0005 & \textless{}0.0005 & \textless{}0.0005 & \textless{}0.0005 & \textless{}0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 4: \(T\)-test for paired comparisons.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Recall@5** & **Precision@5** & **F1@5** & **NDCG@5** \\ \hline UCF & 0.36 & 0.06 & 0.10 & 0.22 \\ Improve & 0.41 & 0.09 & 0.16 & 0.37 \\ SVD & 0.26 & 0.04 & 0.08 & 0.15 \\ Improve & 0.51 & 0.11 & 0.18 & 0.44 \\ TBPR & 0.59 & 0.12 & 0.20 & 0.45 \\ Improve & 0.18 & 0.03 & 0.06 & 0.14 \\ MF & 0.66 & 0.13 & 0.22 & 0.46 \\ Improve & 0.11 & 0.02 & 0.06 & 0.13 \\ NMCF & 0.77 & 0.15 & 0.26 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of top-5 evaluation metrics.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**LR** & **Recall@5** & **Precision@5** & **F1@5** & **NDCG@5** \\ \hline
0.001 & 0.7768 & 0.1554 & 0.2589 & 0.5888 \\
0.003 & 0.7731 & 0.1546 & 0.2577 & 0.59370 \\
0.005 & 0.7616 & 0.1523 & 0.2539 & 0.5792 \\
0.01 & 0.7482 & 0.14965 & 0.2494 & 0.5536 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of learning rate on the performance of NMCF.  
Figure 5: Comparison results (from top-1 to top-5) of different methods. (**a**–**d**) the results of NMCF compared with the baseline methods in terms of recall, precision, F1 score, and NDCG.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Batch Size** & **Recall@5** & **Precision@5** & **F1@5** & **NDCG@5** \\ \hline
128 & 0.7756 & 0.1551 & 0.2585 & 0.6019 \\
256 & 0.7729 & 0.1546 & 0.2576 & 0.5922 \\
512 & 0.7763 & 0.1553 & 0.2588 & 0.5965 \\
1024 & 0.7768 & 0.1554 & 0.2589 & 0.5888 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Impact of batch size on the performance of NMCF.
## 4 Results and Discussion
### Detector-Level Experiment Results
#### 4.2.2 Filter Transmittance Temperature Experiment Results  
Based on the detector-level experiment bench, the filter transmissivity test was carried out to correct the influence of filter temperature characteristics in G\({}_{\text{det}}\)(T) measurement. In this experiment, the PST was fixed to \(-\)65 \({}^{\circ}\)C by the temperature controller to mitigate dark current and noise issues as much as possible. The set temperature of the circulating water cooling (T\({}_{\text{cwc}}\)) was varied in the range of \(-\)25 \({}^{\circ}\)C\(<\) T\({}_{\text{cwc}}<\) 20 \({}^{\circ}\)C at a step size of 5 \({}^{\circ}\)C, thus indirectly controlling the filter and heat sink temperature. F\({}_{\text{fil}}\)(t) was obtained by conducting cubic spline fitting on the normalized results after light source radiance correction. The F\({}_{\text{fil}}\)(t) is shown in Figure 13; the transmittance change in the filter is inversely proportional to its temperature, and the result indicates that the filter transmittance changes by approximately 1.20% as t decreases from 20 \({}^{\circ}\)C to \(-\)15 \({}^{\circ}\)C. The temperature differences between the filter and circulating water cooler setting in experiments were influenced by the ambient laboratory temperature.  
Figure 12: Results of the detector-level bench stability experiment. The response current fluctuation was approximately 0.19% during the experiment, and the temperature fluctuations of the filter, heat sink, and heat exchanger were less than 0.02 \({}^{\circ}\)C.  
Figure 13: Experimental results of F\({}_{\text{fil}}\)(t). The transmittance change exhibited by the filter was approximately 1.20% as t decreased from 20 \({}^{\circ}\)C to \(-\)15 \({}^{\circ}\)C.
Considering the discussion provided above, in this article, we aim to propose a network structure of adaptive neural fuzzy models to identify the inverse dynamics in the course control of underactuated ships, taking the underactuated ships as the research objects and addressing the uncertainty problem caused by the modeling parameters in the ship's course control with the time-varying sailing speed. The trained adaptive neural fuzzy model is also used as an inverse controller in parallel with a fractional-order PI\({}^{\lambda}\)D\({}^{\mu}\) controllers for ships' course control. In order to illustrate the effectiveness of the proposed design, comparative simulations are conducted to verify the advantages of the proposed design. Above all, the main contributions can be summarized as follows:  
1. A novel identification method based on adaptive neural fuzzy model (ANFM) was proposed to approximate the inverse dynamic characteristics of an underactuated ship. The model had good generalization ability and could automatically divide the input space, determine the number of membership functions and the number of fuzzy rules. 2. Sine wave curve and sawtooth wave curve were used as input learning samples of ANFM, respectively, and inverse dynamics simulation experiments of ship were carried out to obtain two different sets of ANFM structures. 3. The trained ANFM was used as an inverse controller in parallel with a fractional-order PI\({}^{\lambda}\)D\({}^{\mu}\) controllers. The ANFM-FOPID controller for course control was constructed and the effectiveness was evaluated by simulation. 4. The effectiveness was further evaluated by comparative experiments of four different controllers, which are the PID controller [57], the PID controller based on PSO algorithm [58], the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller based on GA algorithm [59] and the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller based on ANFM (proposed).
## 2 Outline of the Lane Multiplexing-Based Method for IBA Setting
### Operation Mechanism of the Bus Lane with Time-Division Multiplexing
To control the lane state of each space slice, a lane-changing area (the yellow area in the lane in Figure 2) and a VMS are set at the beginning of each space slice. The lane control signal of the current space slice is displayed and switched through the VMS, and the vehicles can perform lane-borrowing or lane-returning operations in the lane-changing area according to the instructions of the VMS. Figure 3 gives an example of the lane control signals displayed by a VMS, but this is not the best design. In actual traffic implementation, gantry (or cantilever) type, high-resolution VMS panels, or more concise and visual signals can be used to better serve road users. Each lane-changing area is actually the entrance of each corresponding space slice, and vehicles are only allowed to perform lane changing in the lane-changing area. In this way, the VMS at the entrance can easily control the entry of vehicles, ensuring the enforceability of the lane operation strategy. In practice, when a low-priority vehicle violates the signal order to continue to use the bus lane, it will attract a fine from the traffic control department with the evidence from video surveillance equipment. Figure 3: Lane control signals displayed by a VMS. Figure 2: Layout of BLTDM in a road section. \"When\" and \"where\" open the bus lane to general vehicles under the premise of ensuring bus priority is the key to the operation of BLITDM. Based on this, the operation mechanism of BLITDM can be summarized as follows. First, on the basis of considering the spatial operation information of buses and the traffic status of the road section, the time slices of various types of vehicles are divided and allocated; second, the VMS system is used to dynamically control the lane state of the corresponding space slice according to the time slice division result. Therefore, various types of vehicles can \"borrow\" and \"return\" the bus lane in an orderly manner without interfering with each other according to the instructions of the VMS.
## 3 Methodology
### Network Model  
In the proposed network model, there are four main components: [PERSON], [PERSON], the encrypted key, and notification databases. The role of each component is elaborated as follows. The details are also depicted in Figure 1.  
* [PERSON]'s role: From the figure, [PERSON] has the following numbered roles in Figure 1: 1--[PERSON] creates an independent notification database. 2--[PERSON] encrypts sensitive data on her device. 3--[PERSON] stores the encrypted data in the independent notification database. 4--[PERSON] sends the key (that was used for the encryption) to [PERSON] securely  
Figure 1: Network model.  
using the proposed protocol and deletes the key from her device. The next role of [PERSON] is to receive a notification message from [PERSON] containing her secret key in encrypted form. The notification facilitates the establishment of a shared secret key. The primary purpose of the shared secret key is to enable bidirectional communication in the specified scenario.
* [PERSON]'s role: The role of [PERSON] is to securely receive a communication message from [PERSON] containing the key she used for encryption. After decrypting the message, [PERSON] sends a notification to [PERSON] containing the key she sent him earlier.
* Encrypted key: [PERSON] sends [PERSON] the key she used for encryption along with her message in encrypted form and deletes it from her list. [PERSON] stores the key, concatenates it with a new secret, and returns it to [PERSON] in encrypted form.
* Notification database: In the proposed scheme, an independent database is created to store cryptographic keys when the device is locked. The database is encrypted for data confidentiality and consists of two tables: a receiving table and a sending table, as shown in Figure 2.  
Receiving table: The receiving table consist of [PERSON]'s secret data encrypted with a unique key and the identity public key \(IDPK_{Bob}\) of [PERSON].  
Sending table: The sending table consists of [PERSON]'s identity public key \(IDPK_{[PERSON]}\) and the key \(T_{key}\) by which she trusts [PERSON] in encrypted form.
# Two-Wire Power Line Microwave Communication Using Low-Loss Modes
## 3 The Spectral Solution  
Solving (30) involves deforming the integration along the real \(\gamma\) axis into the complex \(\gamma\) plane, as shown in Figures 2 and 3. To accomplish this, it is first necessary to discuss the singularities of the integrand of (30) in this plane. Given the multivalued square root function, branch point pairs can be identified at \(\gamma_{b}=\pm k_{0},\mp j\infty\) with branch cuts connecting them, defined as shown in Figure 2. As a further comment, the \"proper\" Riemann sheet is defined as \(\text{Im}(\zeta)\leq 0\). Since the branch cuts in Figures 2 and 3 are defined so that \(\text{Im}(\zeta)=0\), the visible (i.e., top) portion of the complex plane is the proper sheet where \(\text{Im}(\zeta)\leq 0\). In addition to the branch points and cuts, there is a pole \(\gamma_{p}\), which occurs on the proper sheet at \(\tilde{D}_{\pm}(\gamma_{p})=0\), which is different for the common and differential modes.  
First, (30) for the common mode will be evaluated. In this case, (i.e., Figure 2), the solution will be the branch cut integration plus the residue of a pole \(\gamma_{mSG}\), which corresponds to the SG pole described in [22] modified by the presence of the second wire.  
The result is quite similar to the case discussed in [22], except that the details of the branch cut integration and the mSG mode are different. Also note that if the conductivity becomes infinite, the mSG pole will disappear into the branch cut at \(k_{0}\). One consequence is the more complicated branch cut integration due to the additional term containing the distance d.  
Figure 2: Contour deformation for evaluating (4) for the common mode along with a definition of the relevant branch cut integration and the generalized mSG pole.  
The case for the differential mode is somewhat different, as illustrated in Figure 3. Here, there is no pole that corresponds to the SG pole in the case of a single wire. This can be illustrated through an examination of the denominator of (30). In the single-wire case, there is only a single Hankel function, and the SG pole occurs because this function has a logarithmic singularity near \(k_{0}\). In the case of the two-wire differential mode, the second Hankel function cancels this singularity, and there is no mSG pole. However, there is another pole that corresponds to the well-known quasi-TEM mode for a pair of parallel wires.
## 2 Materials and Methods
### Data
#### 2.2.5 Weather Data  
Air temperature data were provided by ERA5. ERA5 is the fifth generation of the global climate reanalysis dataset released by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA5 dataset provides an accurate description of climate (e.g., temperature, precipitation and wind speed) from local to global scales in the past and has been applied in areas such as yield prediction and disaster monitoring [40]. Average air temperature data were downloaded with a spatial resolution of 2 m based on the GEE platform.
# Nonlinear Effect and MAI Impact on SAC-OCDMA System Based on 2D Multi-Diagonal Code and Laser Array
## 5 Results and Discussion
### First Scenario: Without Considering MAI Effects
#### 5.1.3 Q-Factor versus Fiber Length  
Figure 5 shows a comparison between the system performance with nonlinear effects (i.e., SPM and SRS) and without nonlinear effects (i.e., without NLE) versus the distance (i.e., fiber length) at a bit rate equal to 10 Gb/s per user and an effective area equal to 23 um\({}^{2}\). One can see that the system limitation in terms of distance at the Q-limit is observed around 600 km and 480 km with linear and with nonlinear effects, respectively. In addition, when nonlinear effects are considered a degradation of system performance of 1.6 dB, 2.7 dB and 11.4 dB is observed at 300 km, 400 km and 500 km, respectively. In fact, SPM is related to the degradation of system performance due to the optical shift phase during the propagation through the core of the fiber [35]. The optical launch power was optimized for each transmission distance. The effect of SPM can be reduced through allowing a certain amount of residual dispersion in the design of the dispersion map.
###### Abstract  
The Dst index is the geomagnetic storm index used to measure the energy level of geomagnetic storms, and the prediction of this index is of great significance for geomagnetic storm studies and solar activities. In contrast to traditional numerical modeling techniques, machine learning, which emerged decades ago based on rapidly developing computer hardware and software and artificial intelligence methods, has been unprecedentedly developed in geophysics, especially solar-terrestrial space physics. This study uses two machine learning models, the LSTM (Long-Short Time Memory, LSTM) and EMD-LSTM models (Empirical Mode Decomposition, EMD), to model and predict the Dst index. By building the Dst index data series from 2018 to 2023, two models were built to fit and predict the data. Firstly, we evaluated the influences of the learning rate and the amount of training data on the prediction accuracy of the LSTM model, and finally, \(10^{-3}\) was thought to be the optimal learning rate. Secondly, the two models were used to predict the Dst index in the solar active and quiet periods, respectively, and the RMSE (Root Mean Square Error) of the LSTM model in the active period was 7.34 nT and the CC (correlation coefficient) was 0.96, and those of the quiet period were 2.64 nT and 0.97. The RMSE and CC of the EMD-LSTM model were 8.87 nT and 0.93 in the active period and 3.29 nT and 0.95 in the quiet period. Finally, the prediction accuracy of the LSTM model in the short time period was slightly better than the EMD-LSTM model. However, there will be a problem of prediction lag, which the EMD-LSTM model can solve and better predict the geomagnetic storm.  
machine learning; Dst index; LSTM; EMD-LSTM; prediction 2023
# A Framework of Wearable Sensor-System Development for Urban 3D Modeling
## 1 Introduction  
The demand for detailed 3D modeling of urban landscapes has recently increased in tandem with the growing demand for smart cities and autonomous driving technologies [1]. Precise 3D modeling can be used to solve urban problems [2], as well as for autonomous driving and vehicle localization in GNSS-denied environments [3]. Furthermore, 3D modeling that reflects spatiotemporal changes facilitates the implementation of digital twins and smart cities [4]. Thus, it is considered an essential element of future technologies, with applications in the city and for transportation.  
Multi-sensor systems have been considered among the important research topics of the past few years, owing to their ability to increase the efficiency of 3D modeling [5, 6]. In particular, the usability of wearable multi-sensor systems equipped with LiDAR and cameras is increasing with the demand for 3D modeling of complex urban structures, such as indoor and underground spaces, urban canyons, alleys, and mixed traffic streets [3]. Wearable systems have several advantages, the system offers sufficient 3D information even in environments with insufficient texture or discontinuous depth [7, 8, 9]. Wearable systems mounted on the human body are available in various environments. More specifically, wearable systems can be operated in complex environments where the accessibility of mobile platforms is severely restricted, such as indors, underground spaces, narrow alleys, mixed traffic streets, and historical buildings [10, 11, 12].  
Although there are significant advantages of the wearable multi-sensor system for urban 3D modeling, for accurate data acquisition and fusion, the following must be carefullyconsidered in system development. First, the sensor arrangement method for efficient data acquisition must be reviewed. Wearable sensor systems have a small maximum payload, thereby limiting the number and types of sensors used. Therefore, the configuration and arrangement of sensors must be reviewed, with the aim of acquiring data more efficiently using a relatively small number of sensors. Second, self-calibration of the on-board sensors is required. Self-calibration is the process of estimating the interior orientation parameters (IOPs) and the distortion parameters of the camera by determining the mathematical model equation related to the data acquisition of each sensor. Finally, for accurate data fusion, precise system calibration should be conducted to determine the geometric relationships between sensors. In particular, the system calibration for wearable systems should be conducted especially carefully for the following reasons. For efficient data acquisition, wearable systems have generally been designed to minimize the overlapping of FOV (Field Of View) between sensors. However, such a sensor arrangement obstructs the acquisition of common control features between sensors and limits the accuracy of system calibration. Therefore, the design and characteristics of the wearable system should be considered during the system calibration procedure.
## 3 Results and Discussion
### Plugging Performance
#### 3.2.2 Graphite Plugging Performance  
While studying the plugging efficiency of graphite, it was observed that the material was able to plug a maximum fracture width of 0.5 mm and maintained the same fluid loss volume both for situations of shearing and without shearing. The size degradation did not affect the material performance in plugging the fracture width size because the graphite did not experience any fluid loss, as shown in Figure 6.  
Figure 5: Walnut maximum plugged fracture diameter before and after shearing force.  
The size degradation of graphite was recorded at only 1%, and the D\({}_{50}\) values before and after degradation were 445 and 448 \(\upmu\)m, respectively. Therefore, the newly developed particle size selection criterion is given below in Equation (3). It states that the median particle size of graphite over the fracture width size must be equal to or greater than 0.9 to plug the fracture.  
\[\frac{\text{D}_{50}}{\text{Fracture width size}}\geq 0.9 \tag{3}\]