esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Although it appears difficult to determine if RF or SVR models are better when built with non-spectral and spectral variables together, ultimately the ideal result is a model which can most accurately predict canopy nitrogen in wheat. In both the UAV and satellite models with different variable combinations, the top variable groups had good overall performances. In comparison to studies with spectral-only variable models, the variable combination models in this study all had lower RMSE values. In the context of nitrogen estimation and practical application, lower RMSE (g/m\({}^{2}\)) in models is most beneficial for fertilizer management recommendations.
# Distraction Potential of Vehicle-Based On-Road Projection
## 1 Introduction
### State of the Art  
In addition to the aforementioned studies that point out the benefits of on-road projections for drivers, the impact on other road users is also part of investigated [46; 47; 48; 49; 50; 51]. These studies are often based on a relatively small number of participants. Frequently, the experimental conditions are insufficiently specified or the studies take place in artificially generated scenarios, so that they can only be transferred to real traffic situations with reservations.  
In the study by [PERSON] and [PERSON] [49], a construction site situation with two lanes is recreated on an unlit, closed test track. Nineteen test persons are driving on the right lane through the construction site several times while being overtaken on the left by a projection vehicle. The distraction potential is analyzed based on the driver's eye movements using an eye-tracking system. The duration of gaze fixations on the left lane is significantly higher with activated on-road projections than without. Overall, the duration of gaze aversion from one's own lane is not rated as critical. This is supported by an additional collection of subjective data with the help of a questionnaire. In a study by [PERSON] and [PERSON] [50], statistically verifiable differences in the gaze behavior of other nearby car drivers are demonstrated in various traffic situations in which a test vehicle projected various symbols onto the road. These distractions are also classified as non-critical.
## 4 Methodology
### Influence of the Presence of Infill in the Global Response
Table 5 shows the values of maximum top displacement, maximum base shear, maximum inter-story drift and the energy of the plastic deformation damping by the building for the models studied. For the studied models, the approved output values (base shear, top displacement, ISDMax, energy) were attributed to the corresponding values in the bare frame model to obtain a direct comparison between each case and the case in the bare frame, in addition to the ability to compare between each of the cases of infill wall with the other cases of infill, as shown in Table 6. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Base Shear (KN)**} & \multicolumn{2}{c}{**Max Top Displacement (m)**} & \multirow{2}{*}{**ISD Max (\%)**} & \multirow{2}{*}{**Energy (KN.m)**} \\ \hline
**FF** & 3.78 & 0.46 & 0.52 & 1.82 \\
**SF** & 2.50 & 0.41 & 0.60 & 0.71 \\
**2 SF** & 1.60 & 0.41 & 0.71 & 0.78 \\
**3 SF** & 1.25 & 0.57 & 0.84 & 0.84 \\
**RF** & 2.84 & 0.50 & 0.57 & 1.54 \\
**MF** & 2.84 & 0.55 & 0.63 & 1.56 \\
**UF** & 2.29 & 1.22 & 1.89 & 2.18 \\
**DF** & 2.30 & 0.61 & 0.73 & 1.56 \\
**MHF** & 1.70 & 0.88 & 1.33 & 1.50 \\
**MXF** & 2.39 & 0.60 & 0.69 & 1.67 \\
**RLF** & 1.93 & 0.62 & 0.70 & 1.31 \\
**2 RF** & 1.90 & 0.63 & 0.70 & 1.28 \\
**2 DF** & 2.02 & 0.83 & 0.96 & 1.86 \\ SDF & 2.12 & 0.48 & 0.61 & 1.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The values of maximum top displacement, maximum base shear, maximum inter-story drift and the energy for all models are attributed to their corresponding values in the bare frame model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Base Shear (KN)**} & \multicolumn{2}{c}{**Max Top Displacement (m)**} & \multirow{2}{*}{**ISD Max (\%)**} & \multirow{2}{*}{**Energy (KN.m)**} \\ \hline
**BF** & 192.21 & 0.313 & 0.0230 & 91.45 \\
**FF** & 726.34 & 0.143 & 0.0119 & 166.45 \\
**SF** & 480.36 & 0.128 & 0.0138 & 64.92 \\
**2 SF** & 306.85 & 0.128 & 0.0164 & 71.39 \\
**3 SF** & 240.74 & 0.179 & 0.0194 & 76.89 \\
**RF** & 545.83 & 0.158 & 0.0132 & 140.57 \\
**MF** & 546.47 & 0.173 & 0.0144 & 142.45 \\
**UF** & 440.13 & 0.381 & 0.0434 & 199.52 \\
**DF** & 442.9 & 0.19 & 0.0168 & 142.47 \\
**MHF** & 326.95 & 0.275 & 0.0305 & 137.02 \\
**MXF** & 459.04 & 0.187 & 0.0158 & 152.76 \\
**RLF** & 370.61 & 0.194 & 0.016 & 119.5 \\
**2 RF** & 365.88 & 0.197 & 0.016 & 117.34 \\
**2 DF** & 387.45 & 0.26 & 0.022 & 169.79 \\
**SDF** & 407.45 & 0.149 & 0.0141 & 108.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The values of maximum top displacement, maximum base shear, maximum inter-story drift and the energy of the plastic deformation damping for the models studied. It is difficult to observe the differences between the infill models, so the comparison was made in a more effective way, considering the base of the bare frame values and the models were divided into groups that converge on the type of effect as shown in the following Table 7.
# An Intelligent Ensemble-Based Detection of In-Vehicle Network Intrusion
###### Abstract  
The Controller Area Network (CAN) bus has been implemented in most modern Vehicles. Various attacks can be launched against the CAN bus protocol because it is designed without security mechanisms. It is essential to develop a highly accurate intrusion detection system (IDS) for CAN bus attacks. We design an effective ensemble learning-based IDS scheme for detecting and classifying DoS, fuzzing, replay, and spoofing attacks. These are common CAN bus attacks that can threaten the safety of a vehicle's driver, passengers, and pedestrians. For this purpose, we utilize supervised machine learning in combination with ensemble methods. We first perform data balancing and feature selection. We build and fine-tune random forest, Xtreme gradient boosting, and decision tree supervised learning models. We then combine these models with voting, stacking, and bagging ensemble methods. The results obtained demonstrate the effectiveness of the proposed scheme when trained on real-life CAN traffic datasets to detect and classify these four attacks. The stacking method achieved the highest performance in terms of accuracy, precision, recall, F1-score, and area-under-the-curve receiver operator characteristic (ROC-AUC). The stacking method outperformed other recently proposed methods with an F1-score, precision, recall, and accuracy of 0.993, 0.993, 0.993, and 0.986, respectively.  
machine learning; intelligent transportation systems; intelligent vehicles; intrusion detection system; ensemble learning Article
## 4 Hardware Implementation of CPU-SPU Collaborative SoC
### Performance Optimizations of SPU Security Monitoring
#### 4.3.2 I-Cache Structure Optimization  
In the SoC architecture, improving the searching efficiency of the \(INF_{ref}\) block is not the only strategy in performance overhead optimizations. Moreover, reducing the number of times on BBs integrity checking is another approach to decrease the performance overhead caused by LHash recalculation and searching the \(INF_{ref}\) table. In order to better  
Figure 9: The M-Cache searching method and internal structure of the BB reference information table.  
utilize the locality principle of I-Cache directly mapping main memory (InsCode segment), we tagged the instructions that had been cached in I-Cache and validated for integrity signature during the other BB executions as security statuses.  
Figure 10 presents the internal implementation mechanism of the I-Cache with a size of 8 KB. The structure consists of two parts of IC_TAG and IC_RAM. When CPU sends the request 32-bit physical address (\(ADD_{phy}\)) to the I-Cache for fetching an instruction, then, the I-Cache searches the high 19-bit \(ADD_{phy}\)[31:13] to identify whether it is within its address space range, while the indexed cache line is appointed by \(ADD_{phy}\)[12:4] (where the cache-line depth is 512).  
Afterwards, the physical address can accurately find the target address of the request instruction according to line block offset address of \(ADD_{phy}\)[3:0]. The _Validity_ (\(V\)) mark bit in the appointed cache line is \"1\", which indicates that the I-Cache hit. If the I-Cache misses, then CPU fetches instruction via accessing the external InsCode memory.  
As shown in the IC_RAM of I-Cache structure, a cache line has four instruction words, and when the four instructions at the same cache line are read for execution, the _Tag_ (\(T\)) bit in the cache line turns from \"0\" to \"1\" to indicate that the instructions in current cache line were verified for integrity. From the partition principle of program BBs, one BB contains at least three instructions and occupies one or two cache lines. The long BB occupies several cache lines.  
Therefore, the I-Cache outputs the tag signal of security when all the instructions of the current BB are cached in I-Cache, also the tags of cache lines they occupied are all signed with \"1\" ( for logic AND). Then, the delayed Tag from a synchronizer is input into the FSM controller, and the instruction monitor directly asserts the valid status to the processor (as shown in Figure 6). This optimized approach plays an important role in reducing the number of BB integrity checks in the situation that the BB overlaps with the other BBs.
## 3 Results
### WBC Lines and Breast Cancer Cell Lines Presented in 2D Holograms with DHC  
The two WBC lines THP-1 and Jurkat, and the six different breast cancer cell lines were analyzed for cell mean volume, mean area and mean thickness with DHC (Figures 2 and 3). As shown here, the WBC cell lines THP-1 and Jurkat were consistently smaller than the six breast cancer cell lines.  
The Morphology Parameters Analyzed with DHC Clearly Discriminate between Breast Cancer Cells and WBC Lines  
As measured by DHC, the WBC lines Jurkat and THP-1 cells have smaller cell areas, cell volumes and cell thicknesses than the six breast cancer cell lines. Indeed, there were significant differences in the cell area, cell volume and cell thickness when comparing the WBC lines and breast cancer cell lines, as calculated by two-tailed unpaired Student's t-tests (Figure 3a-c).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & Jurkat & THP-1 & Hs-578T & MDA-MB-231 & MDA-MB-468 & T-47D & Coma-1 & MCF7 \\ \hline _EpCAM_ \% & 0.2 & 0.1 & 0.1 & 0.3 & 11.7 & 48.6 & 79.9 & 91.4 \\ CD45 \% & 100 & 100 & 0.1 & 0.4 & 0.1 & 1.2 & 1.2 & 0.9 \\ \hline \end{tabular}
\end{table}
Table 1: EpCAM and CD45 expression on different cancer cell lines were analyzed with flow cytometry. The results are from one representative experiment out of three, and the results are presented as % positive cells.  
Figure 2: DHC holographic phase image in 2D. Two white blood cell lines (THP-1 and Jurkat) and six different breast cancer cell lines were analyzed with DHC. The color scale bars represent the thickness of the cells in \(\upmu\)m, and the scale bars represent 10 \(\upmu\)m.
# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
## 2 Materials and Methods
The active interaction of occupants with building energy systems is a complex mechanism, where numerous parameters, including the number of occupants, occupant behavior, type of space, type of work, day, and time, exert a significant influence. The aforementioned parameters can be classified into three distinct groups: temporal, spatial, and occupancy. Temporal parameters indicate the time scale, while spatial parameters indicate spatial information. Occupancy parameters, on the other hand, indicate occupant behavior. It can be reasonably argued that psychological and sociological aspects exert a considerable influence on occupant behavior. Given the inherent unpredictability of occupants' behavior, it remains challenging to identify any specific circumstances that may influence the consumption of energy. This uncertainty has a significant impact on the indoor environment, which in turn contributes to the observed variations in energy consumption. Other factors that influence occupants' behavior include temperature, humidity, illuminance factor, and several others, which collectively affect the building's energy consumption. The most common forms of occupant behavior are adjusting the thermostat, dimming the lighting, switching off lights, and using electronic devices. 2. Variations due to the passive interaction of occupants with building systems. Passive interaction depends on the presence of occupants, reported as hours of occupancy in the building, and the absence of occupants, reported as hours of unoccupied building operation. It is possible that there is inefficient operation of the electrical and mechanical systems, which would result in high energy wastage during the unoccupied/occupied hours; this issue was further investigated to estimate the amount of energy wasted during unoccupied hours, with between 26% and 65% of energy being used during unoccupied/occupied hours, compared to working hours between 7.30 a.m. and 5 p.m. Of the total consumption, between 19% and 28% (mainly electricity and HVAC) is used during the weekend when the building is unoccupied [44].
## 3 Cases  
### Case 1  
A 15-year-old girl was evaluated for severe AD and moderate-to-severe asthma. Her family history was positive for atopy, and she had atopic dermatitis, allergic rhinitis and food allergies from the age of 2. Despite an initial first-line treatment with TCs followed by subsequent add-on of SCs, the child experienced several exacerbations of AD that greatly affected her school performance and quality of sleep. She, therefore, started oral cyclosporine A for a total of 6 months, without reporting significant positive effects. At the age of 10, she was started on controller therapy for asthma with inhaled fluticasone, according to the GINA guidelines for a total of 5 years, nonetheless experiencing multiple exacerbations throughout the years [3]. Finally, at the age of 15, she was started on dupilumab (400 mg (SC) at baseline and then 200 mg every two weeks), with a quick improvement in AD symptoms and better control of asthma and QoL already after the first few doses. She reduced the use of TCs and SC antihistamines as well. About 8 weeks after the introduction of dupilumab, the girl reported a slight headache and mild conjunctivitis and no further AEs, justifying the interruption of the therapy. We observed a progressive minor extent and severity of the skin lesions already after the first four administrations. The patient continued follow-up for a total of six months, in which she reported a further improvement in itching and sleep quality. She concurrently complained of fewer asthma exacerbations, with an overall significant improvement in asthma control.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 2 Description
### Supervisory Platform of EMS  
The EMS of the charging station is equipped with a Supervisory Control and Data Acquisition (SCADA) system to achieve the following functions:  
1. Control and Supervision: This part is primarily responsible for the design of optimization and control strategies. It discerns varying operational messages from EVs based on time and allocates the power plans formulated for each time point to the battery, EVs, and grid interface.
2. Data Collection: Through an industrial computer, the charging station periodically retrieves system data, including power, voltage, phase angle, and charging and discharging times. Simultaneously, the computer also accesses information from the environmental service database, such as sunlight, ambient temperature, humidity, and wind speed. These data points are consolidated, stored, and transmitted to a cloud server for subsequent data processing.
3. Monitoring and Visualization: Through Human-Machine Interface (HMI) software, interaction and information exchange are facilitated between the system and users. This empowers users to monitor real-time information such as grid-supplied power, environmental conditions, power generated by photovoltaics (PV), profits, and costs via an intelligent panel. The control end collaborates in generating cumulative charts or tables at various time intervals (daily, weekly, monthly, and overall). Additionally, personalized settings are adjustable, encompassing language translation and the customization of EV charging preferences (e.g., fast charging or other special requirements).
4. Communication and Connectivity: The converter, EV supply equipment (EVSE), industrial computer, and intelligent panel are all linked to the backend server through Internet Protocol (IP) configuration. Communication is established via HTTP with any network client, enabling remote web services. Moreover, on-site sensors and other RS485 communication devices can potentially acquire data through wireless means.
## 3 Integrated User-Oriented Framework
### Local Service: 3D Printing Manufacturing  
It is intended to use a protocol that is as open and implementable as possible so that the 3D printer can communicate remotely with the end-user app to achieve a process compatible with most low-medium cost commercial 3D printers. This is possible with Octoprint, which allows the printer to be connected via USB and at the same time to communicate via Wi-Fi with third-party applications, as explained above. Thus, the integrated framework becomes independent of the 3D printing machine used.  
In the proposed architecture, the print manager is a service running on embedded hardware such as a Raspberry Pi. This new added module acts as a print server on a local network capable of attending to requests from a client and responding accordingly. This is the main element of the local service within the integrated user-oriented architecture. Furthermore, the addition of this hardware allows other parallel services to interact with the process carried out by the printing service.  
Print servers generally include a service management web interface with many functionalities that a non-expert user does not need, and this can lead to confusion. However, there is a resource connection mechanism that allows information to be exchanged be  
Figure 1: Integrated service architecture.  
tween the resource provider (host) and consumer (end-user application), known as a REST API [67]. This host allows the development of a system to manage the 3D printer remotely via local network requests. In addition, this host is the key element in the local service configuration, as the printer, external sensors and an application interact with it using different protocols (USB, Wi-Fi, serial, etc.), as shown in Figure 2.  
Communication via REST API is done with HTTP requests addressed to specified routes, where a message-body in JSON format with the corresponding data is sent or received [68].  
When a request to start a new printing process is received with this protocol, all the movement commands (G-code) for a selected file are sent line-by-line via USB communication. Trajectories, extruder temperature and current manufacturing state are monitored during this process, and even streaming images of the printing platform can be obtained.
## 4 Experimental Results and Discussion
### Training  
The proposed watermarking network was trained in a PC with an Intel (R) Core (TM) i7-9700 CPU @ 3.00 GHz, 64 GB RAM, and the RTX 2080 ti GPU. The hyper-parameters and their values used in training are listed in Table 3, set empirically. A mini-batch includes 100 host images, and a newly generated random-pattern WM data was used for each mini-batch. The training was continued until the loss value is stable, which was 4000 epochs. It used Adam optimizer [18] with learning rates 0.0001 and 0.00001 for the embedding network and the extraction network, respectively. During the training, the strength factor was set to 1, and the weight decay rate was 0.01.  
The values of the \(\lambda\)s were set by separate experiments after determining the other parameters. The finally determined \(\lambda\)s values were 45, 0.2, and 20 for \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda_{3}\), respectively. The values of \(\lambda_{1}\) and \(\lambda_{2}\) are to balance the invisibility and robustness for embedder, while the value of \(\lambda_{3}\) is to balance the training speed of the embedder and the extractor with the weight decay rate. All three values are correlated that we have experimented for the large ranges of values for them.  
With the hyper-parameters in Table 3, the training took about four days with the BOSS dataset.
# A New COVID-19 Detection Method Based on CSK/QAM Visible Light Communication and Machine Learning
## 4 Methodology
### AI-Based Procedures to Replace Human Processing
#### 4.3.1 Logistic Regression  
The weighted sum of the input attributes is used in logistic and linear regressions. However, the logistic regression bias has a binary output as opposed to a direct output. According to [PERSON] [76], a logistic regression model predicts that if the probability is less than 50%, it belongs to the negative class denoted by \"A\" or \"0\", and if it is greater, it belongs to the positive class denoted by \"B\" or \"1\".  
To find the value of the prediction, Equation (21) can be used:  
\[\frac{\partial}{\partial\theta_{j}}MSE(\theta)=\frac{2}{m}\sum_{i=1}^{m}( \theta^{T}\cdot x^{(i)}-y^{(i)})x_{j}^{(i)}\, \tag{21}\]  
where \(m\) is the number of partial derivatives, \(\mathbf{x}\) is the input, and \(y\) is the predicted value. Equation (22) represents the logistic regression model's estimated probability in vector form \(\hat{p}\):  
\[\hat{p}=h_{\theta}(x)=\sigma(\theta^{T}\cdot x)\, \tag{22}\]  
where \(\theta\) is the vector of the model parameters, \(\theta^{T}\) is the transpose of \(\theta\), \(h_{\theta}\) is the hypothesis function, and \(\sigma(\cdot)\), a logistic or logit sigmoidal function, generates a number between 0 and 1, as shown in Equation (23).  
\[\sigma=\frac{1}{1+exp(-t)} \tag{23}\]  
After estimating the probability \(\hat{p}=h_{\theta}(x)\) that an instance \(x\) belongs to the positive class, the Logistic Regression model can easily make its prediction \(\hat{y}\). The logistic regression model's prediction is shown in Equation (24).  
\[\hat{y}=\begin{cases}0\ if\ \hat{p}<0.5\\ 1\ if\ \hat{p}\geq 0.5\end{cases} \tag{24}\]  
Note that \(\sigma(t)<0.5\) when \(t<0\), and \(\sigma\geq 0.5\) when \(t\geq 0\), so a logistic regression model predicts 1 if \(\theta^{T}\cdot x\) is positive and 0 if is negative.
## 3 Results and Discussion
### SCB Test
#### 3.2.4 Effect of Asphalt Type on the SCB Test Result  
As shown in Figure 21, the \(G_{\rm f}\) value of sample 5 is less than that of sample 7 and the \(G_{\rm f}\) value of sample 6 is less than that of sample 8, which means that SBS modified asphalt has better reinforcing effect on the cracking resistance of asphalt mixtures. As shown in Figure 22, the FI value of sample 5 is less than that of sample 7 and the FI value of sample 6 is less than that of sample 8, which means that the SBS modified asphalt can better help decrease the crack propagation rate of asphalt mixtures. The SBS modified asphalt could both help enhance the crack resistance of asphalt mixtures with and without basalt fibers. This is mainly because SBS modified asphalt has better mechanical properties than the pure asphalt which could help to better enhance the property of asphalt binder inside the asphalt mixtures. It can be inferred from the mechanics of composite materials theory that the properties of the composite material could enhance when the property of its component are strengthened. Therefore, the asphalt mixtures using SBS modified could have better crack resistance than the pure asphalt.  
Figure 21: Effect of asphalt type on \(G_{\rm f}\).  
Figure 20: Effect of NMAS on FI value.