esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Formulation  
### Microgrid without Access to ESS  
When the microgrid has no access to ESS, the load demand of the system is provided by WT, PV, and DE together with the main grid. The objective function can be described as follows:  
\[min\quad F1=\sum_{s=1}^{S}\pi(s)\sum_{t=1}^{T}(C_{WT}(t,s)+C_{PV}(t,s)+C_{DE}( t,s)+C_{GRID}(t,s)), \tag{7}\]  
\[min\quad F2=\sum_{s=1}^{S}\pi(s)\sum_{t=1}^{T}[\sum_{j=1}^{J}c_{j}\alpha_{DE,j }P_{DE}(t,s)+\sum_{j=1}^{J}c_{j}\alpha_{GRIDj}P_{GRID}(t,s)], \tag{8}\]  
\[C_{WT}(t,s)=k_{WT}P_{WT}(t,s), \tag{9}\]  
\[C_{PV}(t,s)=k_{PV}P_{PV}(t,s), \tag{10}\]  
\[C_{DE}(t,s)=aP_{DE}^{2}(t,s)+bP_{DE}(t,s)+c+k_{DE}P_{DE}(t,s), \tag{11}\]  
\[C_{GRID}(t,s)=k_{GRID}(t)P_{GRID}(t,s), \tag{12}\]  
where \(S\) is the total number of scenarios; \(T\) is the total number of time intervals; \(s\) is the scenario index; \(t\) is the time index; \(\pi(s)\) is the probability of scenario s; \(C_{WT}(t,s),C_{PV}(t,s)\) are the generating cost of WT, PV respectively; \(C_{DE}(t,s)\) is the generating cost of DE; \(C_{GRID}(t,s)\) is the transaction cost with main grid; \(k_{WT}\) is the generation cost coefficient of WT, \(\text{yuan/kW}\); \(k_{PV}\) is the generation cost coefficient of PV, \(\text{yuan/kW}\); \(k_{GRID}(t)\) is electricity price during time period t, \(\text{yuan/kW}\); \(k_{DE}\) is the operation and maintenance cost coefficient of DE, \(\text{yuan/kW}\); \(J\) is the total number of pollutant types, including \(CO_{2}\), \(SO_{2}\) and \(NO_{x}\); \(c_{j}\) is the treatment cost of the \(j_{th}\) pollutant, \(\text{yuan/kg}\); \(\alpha_{DE,j}\) is the pollutant emission factor of DE, g/kW; \(\alpha_{GRID,j}\) is the pollutant emission factor of the main grid, g/kW.  
The following constraints should be satisfied:  
\[P_{LOAD}(t,s)=P_{WT}(t,s)+P_{PV}(t,s)+P_{DE}(t,s)+P_{GRID}(t,s)\quad\forall t, \forall s, \tag{13}\]  
\[P_{DE,min}\leq P_{DE}(t,s)<P_{DE,max}\quad\forall t,\forall s, \tag{14}\]  
\[-R_{down}\leq P_{DE}(t,s)-P_{DE}(t-1,s)\leq R_{up}\quad\forall t,\forall s, \tag{15}\]  
\[|P_{GRID}(t,s)|\leq P_{GRID,max}\quad\forall t,\forall s, \tag{16}\]  
where \(P_{LOAD}(t,s)\) is load demand of the microgrid, kW; \(P_{DE,min}\) is the minimum output power of DE, kW; \(P_{DE,max}\) is the maximum output power of DE, kW; \(R_{up}\) is the maximum ramp up rate, kW/h; \(R_{down}\) is the maximum ramp down rate, kW/h; \(P_{GRID,max}\) is the maximum exchange power between the microgrid and main grid, kW.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 4 Proposed Solution
### Proposed Design Requirements
#### 4.2.2 Network Delay from Sender to Each Receiver  
The sender must know the distance between the receivers from the sender. Definitely, each receiver will have a different distance from the sender and different network condition between sender to receivers. As sender calculated the single value of playout for all the receivers so it needs to have an idea of the network connecting each receiver.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 3 Results and Discussion
### gbXML Import Results
#### 3.1.4 Warehouse Case in Open Studio  
The model of the industrial warehouse in Open Studio was built with less inconsistencies than in Design Builder and all 21 thermal zones were imported. However, it also showed some errors in the construction of the walls and in its assembly with other constructive systems, which directly affected the thermal zone areas and volumes. The roof, again, was divided into many pieces, in which some parts were considered as shadow elements. Additionally, the windows were missing (see Figure 8).  
As the same phenomena occurred in Design Builder, it can be noted that, in the gbXML import in Open Studio, the bigger and more complex the building, the more issues appear (mainly on the envelope). It would not be a problem if the model had the same size as the residential case, as there would not be many difficulties to be corrected. On the other hand, if the project had the same proportion or bigger than the warehouse, it would be complicated to verify all the model issues and to fix them. Finally, Table 2 compiles the errors and possible resolutions for each case.  
Figure 7: Residential gbXML file imported in Open Studio.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Open Studio**} \\ \hline
**The model** & **Residential case** & **Warehouse case** \\ \hline \multirow{5}{*}{Dimensions} & 1. External dimensions. & Model between 1\% to 6\% smaller than in the BIM. & Correct. \\  & 1. Solution. & Modify the model in SketchUp. & \\  & 2. Thermal zones area & The areas are between 0.85\% to 8\% bigger than in the BIM. & As the thermal zones were subdivided and some parts were missing, it was not possible to measure its area. \\  & 2. Solution. & Modify the model in SketchUp. & Modify the model in SketchUp. \\  & 3. Thermal zones & The volumes are between 4\% to 12\% bigger than in the BIM. & As the thermal zones were subdivided and some parts were missing, it was not possible to measure its area. \\  & 3. Solution. & Modify the model in SketchUp. & Modify the model in SketchUp. \\ \hline \multirow{5}{*}{Geometry} & 4. Zones recognition. & Correct. Total of 13 zones. & Correct. Total of 21 zones. \\  & 4. Solution. & - & \\ \cline{1-1}  & 5. Building surfaces. & North walls and parts of the roof improperly constructed and presented some gaps. Facade of the first floor transferred as a shadow element. & Walls and parts of the roof improperly constructed, divided into many small parts and presented some gaps. Windows were missing. \\ \cline{1-1}  & 5. Solution. & - & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative table between the gbXML files imported in Open Studio.  
Figure 8: Warehouse gbXML file imported in Open Studio.
# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
## 2 Literature Overview
### Type Approval of Lane-Keeping Systems  
The UNECE specifies several mandatory type-approval regulations. Some of them allow computer simulation if accompanied by model validation [15; 16]. The future regulation for Automated Lane-Keeping Systems (ALKS) of SAE Level 3 falls into this category by stating:  
_Simulation tool and mathematical models for verification of the safety concept may be used [...]. Manufacturers shall demonstrate [...] the validation performed for the simulation tool chain (correlation of the outcome with physical tests)._  
[5] Section 4.2  
The predecessor regulation R-79 [4], which deals with continuously intervening lane-keeping systems as installed in many production vehicles of SAE Levels 1 and 2, is currently carried out by physical tests. Nevertheless, we select R-79 as the most suitable use case for a virtual-based type approval in this paper. We do not actually intend to release a vehicle to the market by precisely implementing the regulation. We have a Level 2 vehicle available and use the corresponding R-79 as a blueprint to develop a methodology for future virtual-based type approval [5].  
The regulation describes several tests for the lane-keeping assessment of several vehicle classes and system states. The current Revision 4 of R-79 is supplemented by further amendments that provide remarks on specific aspects such as signal filtering [17]. To illustrate our methodology on a specific example that focuses on the intended functionality of the lane-keeping assist, we choose the Lane-Keeping Functional Test (LKFT) shown in Figure 1. One goal of this test is to guide the vehicle into a quasi-stationary condition of the lateral acceleration \(a_{y}\) within the range from 80% to 90% of the maximum lateral acceleration \(a_{y,smax}\) specified by the vehicle manufacturer. Nevertheless, the regulation requires proof \"for the whole lateral acceleration and speed range\"([4], Section 3.2.1.3). Therefore, we will directly focus on a good coverage of the entire scenario space across both dimensions.  
Within these quasi-stationary ranges, it has to be checked whether the vehicle crosses or even touches the lane boundary and whether the change in movement is within defined limits. In a previous publication [18], we presented an approach to determine the distance to line \(y\) of the vehicle edges with high precision in post-processing. This makes it possible to determine the position of the vehicle on public roads with an accuracy of a few centimeters [19]. According to the test specifications, the driver is taken out of the loop by briefly removing his hands from the steering wheel. The criterion of crossing the lane boundary represents an important safety aspect, since serious consequences can occur if the vehicle leaves the lane.
## 3 Experiment Results and Observations
### Simulation and HILS Experiment Results
#### 3.2.3 Lateral Driving Simulation Conditions  
In the lateral driving simulation, the vehicle drove at a fixed speed of 70 km/h, and a DLC (double-lane change) was performed. The total length of the road was 60 m, and the frictional coefficient of the road was set at \(Mu=0.9\). Moreover, the initial power distribution ratio was set at 90:10 to clearly show the changes in lateral angular velocity.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.6 Resistance to Replay and MITM Attacks  
Adversary \(\mathcal{A}\) captures a message transmitted over the public channel and tries to replay or forge it. In the proposed scheme, a freshly generated timestamp and randomnumbers are included in every message for each section. Upon receiving a message, each entity verifies the validity of these values. Thus, \(\mathcal{A}\) cannot generate a valid message even if the original message is intercepted. Furthermore, any replayed message is rejected due to the timestamp verification mechanism. Therefore, our scheme provides resilience against replay and MITM attacks.
# Efficient Removal Of U(VI) Ions from Aqueous Solutions by Tannic Acid/Graphene Oxide Composites
## 3 Results
### Effects of pH and Ionic Strength  
The distribution of uranium ions and the protonation degree of functional groups on the adsorbent strongly depend on the pH value of the solution [35; 36; 37]. Figure 6 presents the adsorption behavior of uranium ions on TA/GO as a function of pH in 0.001 M and 0.1 M NaCl solutions. When the pH value was less than 6.0, free UO\({}_{2}\)\({}^{2+}\) ions were the main form of uranium. Because of the interaction of adsorption sites between H+/Na+ and UO\({}_{2}\)\({}^{2+}\) ions, the adsorption of uranium ions by the adsorbent was inhibited at pH \(<\) 6.0 [15; 27]. With the increase of pH, the concentration of H\({}^{+}\) ions in the solution gradually decreased and more surface functional groups became deprotonated [15]; thus, the adsorbent  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Pseudo-First-Order** & **Pseudo-Second-Order** \\ \hline \(\mathrm{q_{e}}\), (mmol g\({}^{-1}\)) & 0.0430 & 0.526 \\ k\({}_{1}\) (h\({}^{-1}\)) & 0.0022 & \\ k\({}_{2}\) (g mmol\({}^{-1}\) h\({}^{-1}\)) & & 0.0814 \\ R\({}^{2}\) & 0.7144 & 0.9994 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for adsorption kinetics.  
Figure 5: Effects of different solid contents at pH = 3.6 \(\pm\) 0.05 and I = 0.001 M NaCl.  
surface carried a large amount of negative charges. Therefore, when the pH value increased from 2.0 to 6.0, the adsorption percentage of U(VI) ions gradually increased.  
Figure 6 expresses that the adsorption of U(VI) ions by TA/GO strongly depended on the ionic strength. The concentration of Na\({}^{+}\) ions affected the thickness and interface energy of the electron double layer and also bound species [15]. It is reported that, as compared to outer spherical surface complexation and cation exchange, inner spherical surface complexation is insensitive to ionic strength [38]. It was found that the adsorption percentage of U(VI) ions decreases with increasing ionic strength, which may be due to the following reasons [15]: (1) the increase of NaCl concentration decreases the activity coefficient of uranium ions, thus limiting the transfer of uranium ions from the solution to the adsorbent surface and (2) the formation of electron double-sided complexes between uranium ions and TA/GO was beneficial for the adsorption of uranium ions under low NaCl concentrations; thus, ion exchange occurred between uranium ions and TA/GO complex functional groups. Therefore, the adsorption of U(VI) ions on TA/GO composites occurred due to ion exchange and complexation with the outer spherical surface of oxygen-containing functional groups on the TA/GO composite surface.
There are some methods for improving the accuracy of the low-cost INS, the most important being stochastic error modeling [31; 32; 33]. However, for low-cost INS, it is difficult to find an adequate error parameter due to statistically inconsistent in modeling stochastic errors (e.g., Allan variance method) [33]. In addition, the quality of the initial alignment is also another factor influencing the performance [31; 33], which is hard to ensure in civil navigation application. Also, this GNSS/INS combination does not always satisfy the product design due to the high power consumption, memory storage, and increased cost. Another popular sensor for aiding navigation is the odometer, which is a self-contained velocity sensor available as a standard component in antilock braking systems (ABS). It generates digital pulses for each revolution of the wheel and allows estimation of the distance covered by a vehicle [34]. For modern land-vehicle designs, this information can be obtained through On-board Diagnostics (OBD II), a standardized electrical data interface that communicates with a vehicle's computer. In urban areas, it generally provides more accurate and frequent velocity measurements than GNSS [35; 36]. However, velocity measurements from the odometer are obtained in the vehicle frame (v-frame), and not the navigation frame, such as earth frame (e-frame). Therefore, such velocity information cannot be directly used in the absolute coordinate system. Another drawback is the position drift phenomenon due to error accumulation of the measuring distance. Therefore, performance of only the navigation task over a long time is not adequate. It is clear that the current automotive GNSS solutions cannot easily meet the tradeoff between cost and performance in GNSS-challenging environments. Therefore, this study presents a GNSS/odometer integration scheme with odometer-aided constraints and robust regression for navigation applications. A state-augmented EKF-based GNSS estimator incorporated with the TDCP measurements is implemented for the online-smoothing of the state estimation in the position domain. The v-frame velocity information obtained from the in-vehicle odometer through the OBD II interface is integrated with the proposed vehicular-motion constraints, fault detection and exclusion (FDE), and dead reckoning (DR) aiding to enhance the performance without increasing the cost. Moreover, robust regression as an adaptive weighting method is combined with odometer-aided measurement, providing more reliable weighting indicators for state estimation in a scenario with increased multi-false measurements, without significantly burdening the navigation system with increased computation. This research investigates the feasibility of a cost-effective onboard solution for land-vehicle navigation applications without integrating INS and complex statistical models.
# A Network Traffic Abnormal Detection Method: Sketch-Based Profile Evolution
## 5 Conclusions  
In this paper, we propose a method based on an SPE algorithm for better anomaly detection of network traffic. The TG is established through the network traffic structuring process so that the SPE algorithm can directly deal with the original data of the network traffic, generate the user's Profile via CNN-LSTM, and use Power Distance to decide whether or not the user's behavior is abnormal. Then, in accordance to the real-time acquired data, the new data always updates the Profile, which realizes the Evolutionary analysis of user behavior in order to obtain both the location of abnormal points and the entire abnormal detection of dynamic data flow. The results obtained from experiments using real traffic show that the SPE algorithm can directly process the original dataset, has the ability to continuously process dynamic data stream, and can detect anomalies in the data stream with better performance than PCA and Basic Evolution.  
This study has proven that SPE can perform anomaly detection on a small-scale dynamic dataset. However, SPE only detects outliers and cannot analyze the types of outliers, and both CNN and LSTM have problems with high demand for computing resources, long training time, and difficulty in interpreting training and feature extraction results. They can lead to insufficient timeliness when dealing with dynamic real-time traffic difficulty in explaining the types of outliers. Meanwhile, it has not been utilized in a practical environment on a large scale, so we are still unable to determine its performance in this kind of environment. In the future, we will apply large-scale datasets to behavior in an in-depth way for research in real-world settings.  
Figure 10: Different \(\varphi\) comparison.  
**Author Contributions:** Conceptualization, J.Y.; Methodology, J.Y.; Software, S.Z. and Y.T.; Formal analysis, J.Y. and L.T.; Investigation, S.Z., L.T. and Y.T.; Resources, J.Y. and L.T.; Data curation, S.Z.; Writing--original draft, S.Z.; Writing--review & editing, J.Y., L.T. and Y.T.; Funding acquisition, J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data presented in this study are not publicly available due to the agreement with Esafenet.  
**Conflicts of Interest:** The authors declare no conflict of interest.
The effective characteristics of the emitted radiation, such as brilliance and pulse duration, are heavily influenced by the physical size of the source and its distance from the detector. Achieving an attosecond electron bunch would represent a major milestone, potentially enabling secondary attosecond X-ray sources and significantly advancing the study of matter on sub-femtosecond time scales. In this work, we present the generation of an attosecond ICS source from a density-modulated electron beam in LWFA. By employing the near-threshold ionization injection with a normalized laser intensity \(a_{0}=1.5\) (\(I=5\times 10^{18}\)W/cm\({}^{2}\)), we achieved an attosecond electron bunch train characterized by a comb-like current density profile after 2 mm of acceleration, yielding energies of approximately 300 MeV with energy spreads of about 0.71% for individual micro bunch through 2D PIC simulations. Subsequently, this beam scattered the reflected driving laser, transferring its characteristics to the ICS X-ray source and thereby producing ICS sources with corresponding durations. The separated gamma-ray pulses exhibit an average width of approximately 300 as, with a critical energy of 2.0 \(\pm\) 0.2 MeV. In the 0.1% bandwidth around 2 MeV, the generated gamma-ray source has a peak brilliance of \(\sim\)10\({}^{22}\) photons s\({}^{-1}\) mm\({}^{-2}\) mrad\({}^{-2}\) 0.1% BW. This brilliance is competitive in a laboratory for multi-MeV \(\gamma\)-ray sources with a laser intensity of I = 5 \(\times\) 10\({}^{18}\) W/cm\({}^{2}\). They can enhance the temporal resolution of sampling of the ultrafast fundamental physical processes by an order of magnitude while maintaining their other advantageous features such as a small source size of several microns enabling high-resolution images and a relatively small cost of the required laser systems compared to the large-scale facilities such as synchrotrons or free electron lasers [27; 28].
## 3 The Proposed Method
### Mdf-Gan Based Fault Diagnosis Steps  
To summarize, the fault diagnosis process based on MDFT-GAN includes the following five steps.  
Step 1: Collection of the original bearing vibration signals and construction of a dataset. The dataset includes signals from various operating conditions, encompassing normal signals and multiple fault modes. A multi-domain fusion method is applied to preprocess the one-dimensional signals into multi-domain two-dimensional images. The preprocessed dataset is then divided into training and test sets.  
Step 2: Establishment of an MDFT-GAN-based data augmentation framework and initialization of the model parameters. The generator and discriminator are trained using an adversarial learning mechanism. The generator employs the MDFT module to produce high-quality data, while the discriminator applies multi-domain feature distribution learning to differentiate between real and synthetic signals and perform supervised classification.  
Step 3: Use of the trained MDFT-GAN generator to create high-quality synthetic data. The generated data are automatically labeled with fault categories based on the input category labels.  
Step 4: Quantitative evaluation of the quality of the generated data using metrics such as SSIM and PSNR. The high-quality synthetic data are combined with real data to construct an augmented dataset, thereby expanding the training set and improving the model's generalization ability.  
Step 5: Training of the fault classification model using the augmented dataset. The classifier learns pattern features from both real and synthetic data. Finally, the classification model on the test dataset is validated to assess its fault diagnosis performance and ensure high accuracy, even with limited training data.  
In summary, MDFT-GAN not only generates high-quality synthetic data but also significantly enhances the accuracy and robustness of fault classification. By expanding the training dataset and improving its feature distribution, it provides an effective solution for small-sample fault diagnosis tasks.
# Blockage Effects in Wind Tunnel Tests for Tall Buildings with Surrounding Buildings
## 3 Blockage Effects of Isolated Model Wind Tunnel Test
### Discuses about the Cause of the Drag Coefficient Difference
The above analysis indicates that the drag coefficient is affected by both the Reynolds number effect and blockage effect, but compared with the blockage effect, the influence of Reynolds number difference can be ignored.