esa-sceva/satcom-chunk-collection huggingface dataset:
# A Literature Review on the Charging Behaviour of Private Electric Vehicles
## 3 Results
### Classification of Charging Behaviors  
The identification of groups of users with similar charging behaviors can be helpful in determining charging demand in different scenarios.  
Applying clustering techniques to a set of charging sessions revealed four prevalent charging behaviors. The first is the morning behavior, with an average connection duration of 8.5 h, which is primarily associated with charging at work. The second is the daytime behavior, with an average duration of 1.5 h. The third and fourth behaviors are afternoon and evening charging, with average durations of 4.5 and 15 h, respectively. These charging behaviors are mainly associated with home charging [89]. An analysis of approximately 5 million charging transactions at public charging points identified 13 main behaviors. The most prevalent of these are night-time charging at home and charging at work. For quick charges, there are a variety of behaviors linked to different types of users and purposes [90]. According to a survey of electric vehicle (EV) owners in the United States, there are three main types of charging behavior. The first type is charging an EV based on price and need, while the second type is recharging a vehicle whenever there is an opportunity. The third type concerns a wider range of factors, such as charging power, dwell time, and the cost of home charging [53]. Another study by [PERSON] et al. [91] classified EV charging behavior during weekdays, weekends, and holidays for a month in 2018. The findings showed that the largest group of users recharged their EVs primarily during weekdays after dawn, with slow recharges and small amounts of energy. Meanwhile, on weekends, most users started charging mainly after sunrise, with short charges but with a high amount of energy.  
In a study conducted by [PERSON] et al. [116] on 821 charging stations in Illinois, correlations between charger/vehicle characteristics and charging behavior were analyzed. The results indicate that home charging corresponds to a longer dwell time than other charging points at the same charging level, while at commercial destinations, the dwell time is short, which may indicate that customers use these charging points for opportunistic charging. The same study also found that charging sessions are generally shorter on weekdays and in the morning than in the afternoon. Cars with higher-capacity batteries exhibit longer dwell times and are more likely to be recharged at home. At DC charging stations, the dwell times are the lowest, and the initial state of charge (SOC) is more than 20% lower than that of other charging stations, which may indicate that fast charging is used only when needed.  
Although home charging is preferred in general, studies show fast public charging is preferred during peak hours or activities beyond one's daily routine [39; 40].
## 3 Results and Discussion
### Influence of Multiple Interactions on the Structure of Inclusion and Stress State of the MD System
The vacancy generation in the region leads to the vertical displacement of dislocation segments to adjacent slip planes, where \(\theta^{\prime}\) phase material is not damaged. This is followed by a sequence of interactions that reduce the strength of inclusion, and then the process of transition to a new plane is repeated. Since the transition from the initial slip plane to the adjacent ones is accompanied by a decrease in the effective diameter of the obstacle swept by the Orowan loop, each subsequent peak on the graph is, on average, below the first maximums related to the interaction of the dislocation with the widest part of copper disks. Successive violation of the disk structure and dominant displacement of dislocation to the underlying slip planes leads to a decrease in the strength of the MD system. Figure 7 demonstrates that at the later stages of deformation, the inclusion almost completely breaks up into individual atoms, significantly spaced apart due to repeated passage of the dislocation. Figure 8 demonstrates the time dependence of the averaged shear stress in a region 27 nm in thickness containing \(\theta^{\prime}\) precipitate of 18 nm in diameter. Dislocation passes 97 times through the system. The results obtained differ significantly from the case of smaller inclusions. During the first 8 ns, a slight increase in the region-averaged shear stresses is observed, after which there is a sharp decrease in stresses. This behavior of the stresses is due to the fact that the dislocation practically did not move vertically for a long time, interacting both with copper atoms of inclusion and point defects in the slip plane. In this case, \(\theta^{\prime}\) phase split into two halves, which retain their structure (Figure 8b) with formation of a clearly distinguishable bridge of copper atoms between the two halves. The recent experimental methods allow registering both \(\theta^{\prime}\) phase cutting and their significant bending in various regions of deformed micropillar [32]. The patterns obtained experimentally in the bands of localized deformation completely repeat the shape of \(\theta^{\prime}\) phase shown in Figure 8b. Active displacement in the vertical direction is activated after 8 ns, which will lead to a gradual violation of the inclusion structure in other slip planes. By analogy with smaller inclusions, a further decrease in the acting stresses in the system can be expected.
## 3 Simplified NCO
#### 3.2.2 High Precision Loop Filter  
A proportional integral filter is employed in our structure. In [8], the information carried by most of the TEDs, except for the last one, are dismissed and only the last TED's timing error serves as the input of proportional element, which leads to a great deviation. To guarantee the accuracy, the average value of all timing errors is employed as the input of loop filter in our work. Simulation  
Figure 4: Implementation structure of the error detector.  
results confirmed that the performance is better when using the average value. The equations of the improved loop filter is shown from Equation (7) to Equation (9).  
Proportional element  
\[P_{n}=k_{1}\times(err_{n,1}+err_{n,2}+\cdots+err_{n,32}), \tag{7}\]  
Integral element  
\[I_{n}=I_{n-1}+k_{2}\times(err_{n,1}+err_{n,2}+\cdots+err_{n,32}), \tag{8}\]  
Then the output of loop filter is  
\[W_{n}=P_{n}+I_{n}, \tag{9}\]  
where, \(k_{1}\) and \(k_{2}\) stand for the coefficient of proportional element and integral element separately, \(err_{(n,i)}\) is the error of the \(i\)th PM at time \(n\), \(W_{n}\) is the output of loop filter at time \(n\). The structure is depicted in Figure 5.  
When implemented on FPGA, there will be an error smooth module before the first adder, because the input of loop filter is modified to the average value of parallel timing errors as mentioned above. In an \(m\) parallel system there exists \(m/2\) TEDs, so only a \(log_{2}m/2\)-bit shift operation on FPGA can accomplish the smoother. Besides, the first adder is an \(m/2\) input adder, so in an \(m\) parallel system the delay brought in by the smoother is \(log_{2}m/2\). To save the hardware resources, the multipliers will be replaced by shift operations on FPGA as aforementioned. So \(k_{1}\) and \(k_{2}\) in the loop filter are approximated to the nearest integer power of \(1/2\) to replace the multipliers by shift operations on FPGA. As the multiplier operation on FPGA is achieved by shift and add operation, so even though these approximations would change the closed loop bandwidth and the damping factor achieved by the system, the approximation is reasonable.  
Therefore, the total adders consumed in the loop filter is \(1+m/2\), and the corresponding delay is \(2+log_{2}m/2\).
## 4 Security and Performance Analyses
### Security Analyses
#### 4.2.2 Session-Key Security (AKE-Security, Threat Model 1)  
In order to ensure the security of \(PHI\), the proposed scheme uses the chaotic-map-based Diffie-Hellman key exchange to negotiate the session keys \(SK_{MCSP}=h(T_{a+}(x)mod\ p)\) of \(Pat\) and \(MCS\), \(SK_{D}=h(T_{a+b}(x)mod\ p)\) of \(Doc\) and \(MCS\), and \(SK_{PD}=h(T_{a+T_{D}}(x)mod\ p)\) of \(Pat\) and \(Doc\). Therefore, the session-key security of the proposed scheme is based on ECM-DHP and one-way property of the hash function, and thus is negligible. Hence, the proposed scheme provides session-key security.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Design
#### 4.5.1 Hiding Pattern  
The covert channel stores hidden information in the PDCP, i.e., can be categorized as a network covert storage channel. More precisely, a header element of the PDCP data PDU is exploited by the modification of the nonpayload. Since the hidden information is embedded into the reserved bits, the header structure of the PDCP is not modified. So, the covert channel is structure preserving and applies the _reserved/unused_ hiding pattern.
## 5 Implementation
### Sdr1  
In order to enable the partial reconfiguration on SDR1 we have extended the reference design provided by Analog Devices for the Xilinx ZC706 board and the AD-FMCOMMS-2/3 RF-frontend ([[https://wiki.analog.com/resources/eval/user-guides/ad-fmcomms2](https://wiki.analog.com/resources/eval/user-guides/ad-fmcomms2)]([https://wiki.analog.com/resources/eval/user-guides/ad-fmcomms2](https://wiki.analog.com/resources/eval/user-guides/ad-fmcomms2)) -ebz/reference_hdl, accessed on 1 February 2024) (Figure 4). In the static part of the reference design, we added the PRC IP core, a wrapper around the ICAP port, a MIG IP core, a block controlling the onboard LEDs, and a DSP block, which represents the reconfigurable part of the design. The block controlling the LEDs aims to show an uninterrupted operation of the static part of the design. We also applied changes to the original blocks of the reference design to enable multiplexing of data on the digital-to-analog converter (DAC) path, to be able to switch between two sources of data (i.e., generated by the PS or the PL). The resumed version of the Vivado block design responsible for the partial reconfiguration of the PL is shown in Figure 5.  
Regarding the RR, two functions have been successfully implemented that can be replaced at run-time through the PRC controller: a direct digital synthesizer (DDS) and a 5G new radio (NR)-like signal that is cyclically play-backed. The first block reuses the Xilinx DDS IP core [41] and allows for generating a tone signal. The second block comprises  
Figure 4: The AD9361 hardware description language (HDL) reference design in SDR1.  
a memory array built with memory elements embedded in the FPGA fabric (i.e., block-RAMs-BRAM-) that allows for storing a 5G NR-like signal, along with a state-machine controlling the playback process of the signal. Each processing block incorporates an AXI4 slave interface; this allows the PS to write into memory-mapped registers that define the behavior of each processing block. For example, in the case of the DDS, the register sets a parameter allowing to change the synthesized frequency at run-time. Changing the processing block behavior is done in a part of the firmware responsible for the RR, which could be reconfigured by REWIRE. The reconfiguration flow of the PRC core is controlled by the static part of the REWIRE firmware running at Processor 0.  
A view of the implementation results after placement and routing featuring a single RR are shown in Figure 6 and a breakdown of the resources' usage in Table 1. It reveals that the FPGA implementation is low-dense and occupies a relatively small amount of the total resources, leaving the possibility of having more than one RR.  
Figure 5: The top-level Vivado Block Design of SDR1.  
Figure 6: The routed FPGA design in SDR1 showing the RR.
# A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques
## 3 Smart Building Monitoring Applications
### Lighting Management  
IoT-based lighting control is crucial for effective smart building management and control. The primary goal of implementing an automated lighting management system in a building is to minimize energy consumption while ensuring visual comfort for occupants. Also, light management is necessary to meet lighting requirements for specific visual tasks like drawing, soldering, reading, etc.  
There are several lighting management strategies presented by [PERSON] et al. [17] and Pacific Northwest National Lab. [18]:  
* Time scheduling--uses a schedule with a determined time when lights should be turned on and off automatically. This strategy is good for spaces that are constantly occupied during a predetermined time and unoccupied for the rest of the day.
* Daylight harvesting--measures total illuminance from daylight and artificial lighting to adjust luminaire brightness to reach the required illuminance level in a room. This strategy shows high effectiveness for rooms with high daylight availability.
* Occupancy control--turns on lighting automatically when human presence is detected and switches off the light when there is no human presence. This approach is typically used in areas that are occupied intermittently, such as stairwells, break rooms, conference rooms, and restrooms.  
Each of the described approaches is strongly dependent on the type and occupancy pattern of the room where it is applied, yet the energy efficiency and occupants' visual comfort can be improved by combining the above strategies. Table 1 represents an estimation of energy savings for each automated lighting management strategy, including also manual control for comparison, based on a review made by [PERSON] et al. [19].  
Light sensors are commonly located on ceilings for ease of installation, yet more precise measurement results can be obtained when light sensors are placed closer to the points of interest for occupants, for example, desks, walls, etc. This solution allows the required illumination for specific tasks to be measured directly; however, the downside of such an approach is that the sensors can be easily shadowed by an object or a person [20].  
Another important aspect of indoor lighting management is the light color adjustment described by [PERSON] et al. (2018). The authors employ a real-time adjustable lighting system that varies both correlated color temperature (CCT) and illuminance. This system achieves the desired CCT by blending the colors of cool-white and warm-white light-emitting diodes (LEDs). The proposed lighting system is versatile in human-centric applications, enhancing occupant productivity and supporting proper circadian rhythms. It adjusts dimming and warm lighting according to the time of the day (e.g., if a person works after daylight).
## 1 Introduction
### End of Service Life  
As indicated above, the end of service life is assumed to be a superstructure rating of 5 for non-reconstructed and non-rehabilitated bridges. A deck or superstructure rating of 4 would automatically classify a bridge as structurally deficient by FHWA guidelines [29]. Such a designation has significant policy and regulatory implications. Therefore, various states such as Wisconsin, Minnesota, and Virginia limit the number of bridges with ratings of 4 or below to less than a few percent of the total bridge population (e.g., 5% in Wisconsin and 8% in Minnesota and Virginia) [30; 31; 32]. A report on bridge management practices in Idaho, Michigan, and Virginia [33] states that, in general, structures with an NBI condition rating of 5 or lower are restored (defined as rehabilitated or replaced). According to [PERSON] et al. [34], Virginia Department of Transportation (VDOT) considers structures with a condition rating of 5 as candidates for restorative actions because of the risk of becoming structurally deficient. VDOT uses the percentage of structurally deficient bridges as a global performance measure for its bridges [31].  
[PERSON] and [PERSON] [35] used the time to reach rating of 5 as the \"time to first rehabilitation of the bridge deck and, also the initial service life of the deck.\" [PERSON] et al. [18; 19; 20] used the deck rating of 5 as indication of the end of service life for survival analyses of bridge decks. The Michigan Department of Transportation considers the NBI rating of 5 as the break point between fair and poor conditions [34].  
The NBI ratings of 4 or 3 were not selected as the end of service life because a great majority of bridges are rehabilitated or replaced before reaching those ratings. For example, of the total of 12,011 Wisconsin bridges (from 2012 NBI data), the number of bridges with NBI superstructure ratings of 4 and 3 are 308 (2.5%) and 82 (0.7%), respectively. [PERSON] and [PERSON] [36] state, \"A bridge element only rarely receives a condition rating of 3 since... they are generally either rehabilitated or replaced before reaching this level.\"
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 2 Literature Review
### Orbital Scale

Table 5: References considered in this study–orbital scale. L, M, and H mean low-, mid-, and high-level data fusion, respectively. The numbers in the fourth column are those adopted in Table 2 for each “fused data” class. Another important challenge is the difficulty of obtaining/collecting reference data for validation of the techniques applied. This problem can be particularly difficult if the reference data need to be gathered in-loco. It is also important to consider that, even if reference data can be collected, differences in granularity and the positions of the sample points can make the comparison with the fused data difficult or even unfeasible [112]. These and other challenges related to data fusion applied to satellite data were discussed in depth in [9], so they are not explored in detail here, although some of them are revisited in a more general context in the next section.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Recommendation ITU-R P.845-3  
The Radiocommunications section of the International Telecommunications Union (ITU-R) considered in 1997 that the HF field-strength prediction methods should be compared against measured field- strength data with a certain level of accuracy [31]. It also considered that accurate field-strength data should be indispensable for the use of HF spectrum. Therefore, it published some recommendations on:  
* Measurement of sky-wave signal intensities above 1.6 MHz;
* HF field-strength measurement technique. Specifications for field-strength measurement campaign with the aim to improve future prediction methods.
# Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models
## 4 Characterization of the Simulated Data
### Dynamic Train-Rail Interaction  
The methodology used for vehicle-structure interaction analysis, detailed and validated in [PERSON] et al. (2015) [52], to take into consideration the lateral dynamics alongside vertical interaction has been widely applied in various contexts [49; 51; 53; 54; 55].  
The coupling is established through a validated wheel-rail contact model [52], which is formulated using a specially designed contact finite element. This element employs a structured approach consisting of three main stages:  
* **Geometric contact determination**--The first stage involves identifying the precise contact point between the wheel and rail at each computational step. This is achieved using an online method [56], where a set of nonlinear equations is solved to ensure compatibility between the contact surfaces. Further details on the parameterization of these surfaces and the associated geometric equations can be found in [PERSON] et al. (2015) [52].
* **Normal contact force calculation**--Once the contact point is determined, the second stage focuses on computing the normal forces exerted at the interface. These forces are obtained using Hertzian contact theory [57], which provides an analytical approach to estimating pressure distribution and deformation within the contact patch.
* **Tangential contact and creep force computation**--The final stage deals with the forces generated due to rolling friction between the wheel and rail. The creep forces, which arise from relative motion at the contact interface, are evaluated using the USETAB routine [58]. In this method, precomputed values for longitudinal and lateral tangential forces are stored in a lookup table, allowing efficient interpolation during dynamic simulations based on creepage values and the aspect ratio of the Hertzian contact ellipse.  
For the dynamic analysis solver, the system's governing equilibrium equations are supplemented by constraint equations that link the displacements of the vehicle's contact nodes to the corresponding nodal displacements of the track structure. These equations form a unified system, with both displacements and contact forces as unknowns, which is then solved using an optimized block factorization algorithm (further details available in [PERSON] et al. (2015) [52]). Since the numerical framework is built on the Finite Element Method, it allows for the modeling of both structures and vehicles of varying complexity. The current formulation is implemented in MATLAB(version R2022a) [59], whereas the vehicle and track structures are modeled in ANSYS(r) [31], as illustrated in Figure 7. A more in-depth discussion of the train-track interaction tool and the wheel-rail contact model, along with their validation, is provided by [PERSON] et al. (2015) [52].  
Figure 7: Representation of the numerical modeling steps that form the study model, with the wheels considered in the analysis highlighted. (Adapted from [PERSON] et al. (2023) [60].)
## 4 Results and Discussion
### Experimental Settings
#### 4.1.4 Parameter Settings  
In this experiment, the implementation details of FedDeep were as shown in Table 2. The values of \(P\) and \(Q\) were set to 12, i.e., the \(PM_{2.5}\) concentrations for the following 12 h were predicted according to the \(PM_{2.5}\) concentrations and external factors for the previous 12 h. The pytorch library with 1 A100 and 13 Nvidia 3090 Ti GPU cards were applied in the experiment. In particular, 1 A100 card was treated as the central cloud, and 13 ECSs were simulated with 13 Nvidia 3090 Ti GPU cards. The size of the dataset in each ECS was consistent with the number of sites in Table 1. Each dataset used in the corresponding ECS was segmented into training, validation, and test sets based on the chronological order, with a split ratio of 7:1:2. In addition, the Z-Score normalization method was utilized as data normalization. For each local ESTNet deployed on the corresponding ECS, we selected the stochastic gradient descent as an optimizer to minimize the loss function MSE of 64 epochs. The batch size and learning rate were set to 32 and 0.0001. In addition, there were several hyperparameters in each local ESTNet: the number of heads in MHSA \(N_{h}\), the dimensions of each head \(N_{d}\), the MHSA layers \(L_{MHSA}\), the LSTM layers in the spatio-temporal learning layer \(L_{LSTM(ST)}\), and the LSTM layers in the decoder of seq2 seq \(L_{LSTM(dec)}\). These hyperparameters were fine-tuned on the validation set and the best prediction accuracy was observed with the following settings: \(N_{h}=4\), \(N_{d}=8\), \(L_{MHSA}=3\), \(L_{LSTM(ST)}=1\), and \(L_{LSTM(dec)}=1\). After receiving the optimal \(N_{h}\) and \(N_{d}\), we could determine \(d_{model}=32\). The parameter configuration of the global ESTNet was the same as that of the local ESTNet.