esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper described the collision avoidance system by proactive braking control with an oncoming vehicle that darts out under occluded vision when turning right at an intersection in left-hand traffic. We proposed a proactive driver assistance system that avoids the right-turn dilemma zone, with the assumption that the vehicle under test (VUT) uses only on-board sensors without infrastructure coordination facilities. The right-turn dilemma zone is defined as the region of the vehicle's hazardous speed where the vehicle cannot avoid collision safely when a vehicle darts out suddenly. The proposed proactive braking system provides braking assistance to avoid the right-turn dilemma zone. The simulation study using a right-turn across a path scenario with various conditions of the position of the oncoming vehicle and the darting-out velocity of that vehicle reveals that the proactive braking control system is able to decelerate the vehicle in advance before a darting-out of oncoming vehicle occurs, and consequently stop the vehicle with mild deceleration without activating AEBS. In addition, we confirmed that the proactive braking control system effectively reduces the possibility of collisions and near-miss situations, using situational risk assessment such as Safety Cushion Time (SCT), when a darting out is detected, compared to the conventional AEBS. Comparison with the previous system described in [24] showed that the proposed system solved the problems of the previous system: rear-end collision risk due to emergency braking and certain conditions where collisions cannot be avoided.  
Future research will focus on enhancing the performance of the control system to ensure its effectiveness in accident prevention. In particular, we will examine the effect of collision avoidance performance and the interaction between human driver and the braking control system due to variations in intersection geometry (e.g., the intersection size, the number of lanes, the angle of the intersection, etc.) through simulations.  
Conceptualization, S.A., Y.F. and P.R.; methodology, S.A., Y.F. and P.R.; validation, S.A.; data analysis and visualization, S.A., Y.F. and P.R.; discussion, S.A., Y.F. and P.R; writing--original draft preparation, S.A.; writing--review and editing, S.A., Y.F., P.R., and R.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS Grant-in-Aid for Scientific Research(C) 21K03977 in coordination with Fund for the Promotion of Joint International Research 22 KK0237.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 4 Proposed Scheme
### Binary Power Control Scheme (BPCS)  
Here, we present an analytical characterization of the power control scheme to achieve distributed solutions with low computational complexity. The use of the binary power control scheme (_BPCS_) for network-assisted multicell cellular networks is well known [29; 30]. This approach can achieve a near-optimal solution when multiple DPs simultaneously reuse the same cellular resource in a multicell communication environment, thus accommodating a high system capacity. To solve the optimization problem formulated in Equation (26), we can reformulate the throughput problem for CU \(n\) and DP \(m\) as follows:  
\[T_{n}^{*}=\ \frac{1}{C}\sum_{n=1}^{C}log_{2}\Big{(}1+\gamma_{n}^{k}\Big{)}. \tag{35}\]  
and  
\[T_{m}^{*}=\ \frac{1}{F}\sum_{m=1}^{D}log_{2}\Big{(}1+\gamma_{m}^{k}\Big{)}, \tag{36}\]  
respectively, where \(T_{n}^{*}\) and \(T_{m}^{*}\) represent the optimal throughput for CUs and DPs, respectively.  
Therefore, the transmit powers of CUs \(\big{(}P_{1}^{1},\ \ldots,\ P_{N}^{K}\big{)}\) and DPs \(\big{(}P_{1}^{1},\ \ldots,\ P_{M}^{K}\big{)}\) should satisfy the following conditions:  
\[\text{A2.}\Big{(}P_{1}^{1},\ \ldots,\ P_{N}^{K}\Big{)}=\arg_{n}\max_{e\in C, \ k\in X}\sum_{n=1}^{C}[T_{n}^{*}] \tag{37}\]  
\[\text{A3.}\Big{(}P_{1}^{1},\ \ldots,\ P_{M}^{K}\Big{)}=\arg_{m}\max_{e\ D,\ k \in\ X}\sum_{m=1}^{D}[T_{m}^{*}] \tag{38}\]  
subject to:  
\[G_{n,B}\leq G_{n,B}^{Th},\ \forall n\in C, \tag{39}\]  
\[G_{m,B}\leq G_{m,B}^{Th},\ \forall meD, \tag{40}\]  
\[P_{n}^{min}\leq P_{n}^{k}\leq P_{n}^{max},\ \forall n\in C,k\in X, \tag{41}\]  
\[P_{m_{t}}^{min}\leq P_{m_{t}}^{k}\leq P_{m_{t}}^{max},\ \forall m_{t}eD,\ k \in X, \tag{42}\]  
\[\delta_{m,n}^{k}\epsilon\{0,1\},\ \forall\ m\ e\ D,\ n\ e\ C,\ k\in X. \tag{43}\]  
The pseudo code for the _BPCS_ is shown in Algorithm 2. Aiming at a higher throughput with large reuse factor, the channel conditions are analyzed for CUs and DPs. In _BPCS_, the transmission is initiated only if the channel quality is sufficient, i.e., \(G_{n,B}\leq G_{n,B}^{Th}\) and \(G_{m,B}\leq G_{m,B}^{Th}\). This constraint implies that the smaller values of \(G_{n,B}\) and \(G_{m,B}\) minimize interference between CUs and DPs, resulting in higher throughput and spectral efficiency. Therefore, Algorithm 2 is applied to control the transmit power of CUs and DPs that have a higher channel gain for the eNB and low interference. The transmit power can be increased until the required SINR is no longer satisfied. When the maximum user power has been reached and the required SINR value is achieved, the process stops.
# Hierarchical LSTM-Based Network Intrusion Detection System Using Hybrid Classification
## 4 Performance Analysis
### Confusion Matrix  
The confusion matrices for each dataset are shown in Figures 14 and 15. In the figures, the darker color means the higher value. From Figure 14, it can be observed that the proposed method had the highest accuracy for all classes. In particular, the detection accuracy for the infiltration and normal classes was significantly higher than that of the other methods.  
Figure 13: Results for the comparison of the intrusion detection rate for the CIC-IDS2017 dataset.  
Figure 14: Confusion matrix for each algorithm using the ISCXIDS2012 dataset: (**a**) Proposed; (**b**) ELM; (**c**) DNN; (**d**) CNN; (**e**) TSE.  
Figure 15: Confusion matrix for each algorithm using the CIC-IDS2017 dataset: (**a**) Proposed; (**b**) ELM; (**c**) DNN; (**d**) CNN; (**e**) TSE.  
It is important to accurately detect network intrusion in NIDS, but it is more important to accurately detect normal traffic as a normal class in order to provide stable network service to users. In this respect, the proposed NIDS shows the highest normal class detection accuracy compared to all existing NIDSs. Since the normal class has the largest number of sessions, it has the most diverse characteristics. Therefore, it is difficult to achieve high detection performance, but the proposed NIDS has high classification accuracy even for normal class because it uses all packet traffic to create features and detects network intrusion based on them.  
As shown in Figure 15, the proposed method achieved the highest accuracy for all classes even for the CIC-IDS2017 dataset. Moreover, the performance for the benign and DoS GoldenEye classes was very high compared to the other algorithms. Therefore, Figures 13 and 14 confirm that the proposed method improves the overall detection performance as well as the detection performance for individual classes compared to the existing methods.
## 2 Materials and Methods
### Screening of Fungi and Insets  
We collected agarwood tissue from the base of the _Aquilaria_ tree and removed surface soil and debris using a soft brush. We then disinfected the samples with 75% alcohol for 2 min and transferred them into a 10%-20% sodium hypochlorite solution for 10-20 min under gentle shaking. The samples were rinsed 3-5 times with sterile distilled water for 1-2 min each and cut into small wood chips (5 mm in length and 1 mm in width) and then rinsed 2-3 times with sterile water. The sterilized samples were placed on potato dextrose agar (PDA) medium and incubated at 25 \({}^{\circ}\)C in the dark. Colony isolation was performed until pure fungal strains were obtained. The core workflow for fungal sequencing identification involves the following steps. Fungal DNA was extracted using a QIAGEN DNeasy Plant Mini Kit (Wuhan Sevier Biotechnology Co., Ltd., Wuhan, China) and assessed for purity and integrity through agarose gel electrophoresis. A PCR protocol targeting the ITS regions was performed with specific primers (ITS1/ITS4) (Supplementary Materials, Table S1) under an annealing temperature of 50-60 \({}^{\circ}\)C, followed by electrophoretic verification of amplified products. Purified PCR amplicons were sequenced using Sanger sequencing. Downstream analysis included BLAST alignment against the NCBI database, with taxonomic classification determined based on sequence similarity thresholds (\(\geq\)98% for species-level, \(\geq\)95% for genus-level). For ambiguous species, multi-locus phylogenetic analysis supplemented by morphological characterization was employed to ensure reliable identification. By consulting the literature, we identified fungi reported to induce agarwood formation in _A. sinensis_. The obtained fungi were analyzed, and then the agarwood test was carried out using the fungal treatment induction method, as shown in Section 2.3. The induced agarwood samples were analyzed using GC-MS to detect whether they contained chemical components similar to the main components of agarwood, and strains with the ability to induce agarwood formation in _A. sinensis_ were determined.  
We disinfected the insects by immersing them in 3% hydrogen peroxide for 1-2 min. Then, we immediately rinsed them 3-5 times with sterile distilled water to thoroughly remove any residual disinfectant. The insects were transferred to a Petri dish for observation of their survival condition. Based on literature findings, the most appropriate insects for inducing agarwood in _A. sinensis_ were selected. Subsequently, insect analysis was carried out using the insect boring treatment, as described in Section 2.3. After 7-15 days, the survival and feeding activity of the insects in the tree were observed to assess their potential to promote agarwood formation.
# The Effect of Countermeasure Readability on Security Intentions
## 2 Theoretical Background  
Self-efficacy (SE) is defined as \"beliefs in one's capabilities to mobilize the motivation, cognitive resources, and courses of action needed to meet given situational demands\" [18]. Self-efficacy has been argued as the most focal or pervasive mechanism of human agency which motivates and regulates individual behavior [19].  
When people have a high level of SE, they have a stronger internal conviction about their ability to mobilize the motivation, cognitive resources, and courses of action needed to successfully execute the task at hand [20]. It influences the amount of effort, self-regulation, and the initiation and persistence of coping efforts when facing obstacles (relating to the task at hand) [21].  
Determinants of SE are performance accomplishments, vicarious experiences, verbal persuasion, and physiological feedback. Performance accomplishments are the most important factor in SE and refer to experiences of executing similar behaviors or tasks. If a person performed well previously in a similar task, they are more likely to feel confident about the current task.  
Vicarious experience refers to derived confidence from other people (that one can relate to) carrying out a task. If one sees a peer failing at a task, this negatively affects his/her SE. Verbal persuasion describes encouragement or discouragement to one's performance or capability to perform a task. The effect of this factor is dependent on the credibility of the one persuading.  
Lastly, physiological feedback refers to sensations from a person's body and how this is interpreted by that person. For instance, one could sense a tension in the stomach after realizing that their operating system is prone to security breaches due to a lack of updates. Interpreted as anxiety, this will negatively impact SE. Conversely, when this is interpreted as excitement, the person's SE will be increased in the task at hand [22]. The task being updating the system with the latest security patches.  
The concept of SE is an important construct in several theories on human behavior and motivation; examples being protection motivation theory (PMT) [23], self-efficacy theory of motivation [21], self-determination theory [24], and health belief model (HBM) [25]. Studies applying HBM and PMT to the security context, have pointed out that SE is a strong determinant of computer SB [26, 27] and desktop SB [10], respectively. Other studies showed a significant relationship between SE and intention to information system security policy compliance [11] and intention for compliant SB [28].  
The three domains, computer SB, desktop SB, and information system security policy compliance, can be paralleled to the more general idea of users' integration of cybersecurity controls into their SB. [PERSON] and [PERSON] [28] showed that in this general sense, SE had a significant effect on user intentions for compliant SB. The next subsection will elaborate on these theories applied to this context and what role SE plays in them.
## 3 Simulated Results and Discussions
### Surface Current Density  
The surface current density (J-Surf) of the FSS array obtained at 10 GHz frequency, under normal incidence (TEz-0\({}^{\circ}\)), for the TE mode is shown in Figure 3a. The current distribution reveals that the current is more saturated at the U-shaped stubs at the FSS surface. This current intensity indicates inductance while the slots contribute to capacitance.
## 2 Review of Related Works
### Network Slicing Solutions in 5G Networks
The proposed algorithm maximizes user access through RAN and core network slices through dynamic resource allocation. Results of the simulations showed that the proposed reinforcement learning approach yielded a higher number of user access in both delays constrained and rate constrained slices. A summary of the related works is provided in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline
**Ref** & **Objective** & **Proposed Solution** & **Performance Metrics** \\ \hline \multirow{4}{*}{[14]} & Optimal slice selection and prediction for mobile devices and adaptive slice assignments in the case of network failures & A Deep Learning and machine learning-based network slicing scheme that analyzes and predicts network traffic patterns for optimal resource allocation & Slice prediction accuracy \\  & Secure network slicing for user equipment access & A neural network-based network slicing model for proactive threat detection and elimination & A & Threat detection accuracy \\ \hline \multirow{4}{*}{[16]} & Efficient slice management and resource allocation for RAN and CN & An intent-based network slicing framework for upper-level slice configuration and orchestration & A & Data rate \\  & Efficient slice management and framework for upper-level slice configuration and orchestration & A & Network throughput \\ \hline \end{tabular}
\end{table} 
Table 1: A Summary of Related Work on Resource Allocation and Network Slicing.
## 3 Results and Discussion
### Beauty Dish  
The beauty dish consists of several parts. The trajectory of a light beam first hits the reflector dish positioned in front of the light source and is then reflected back to the main reflector surrounding the light source. The main reflector (BD_R) is a constant in this correlation of results. In combination with a xenon light source, the CRI value is on average 1.07 higher than for the light source itself, while it is 0.17 lower in combination with a halogen light source (Figure 5). In Figure 8, we show the CRI results for each TCS for all seven combinations tested.  
If a golden deflector dish is added to the xenon light source with the main reflector, all CRIs per TCS increase by an average of 0.2. The CRIs of R9 (red) and R10 (yellow) increase even more significantly by 2.5 and 0.77, respectively. When combined with a halogen light source, however, the difference is more pronounced and negative, with the CRI per TCS generally falling by 1.0. If a grid is added, no significant difference in the CRI per TCS can be observed with xenon, while the R9 value drops additionally with halogen.  
If the main reflector is coupled with a silver deflector dish, less pronounced differences are observed. When coupled with xenon, only the R9 value increases by 0.8, while there is no major difference when coupled with halogen. The addition of a grid increases the difference for R9 with xenon by 2.46 in the positive range.  
Figure 7: Influence of snoot and snoot with grid on every CRI TCS for (**a**) xenon and (**b**) halogen.  
A completely different trend, or more precisely, no trend at all, is observed when a white deflector dish is used, which shows only minimal differences for all TCS for xenon and halogen. However, when a grid is added, the CRI decreases for xenon, especially for red TCS (R9).  
In general, the reflection of xenon light through a silver or gold deflector dish leads to better CRI values, while the opposite is true for halogen light. The white deflector dish does not affect the original light to any significant extent. In any case, the addition of a grid emphasises the original differences, either in a positive or negative sense.
# Proxy Signature-Based Management Model of Sharing Energy Storage in Blockchain Environment
## 2 SES and Its Management Model  
### Basic Concept and Characteristics of SES  
SES is not a novel energy storage technology akin to superconducting the magnetic energy storage or Li-Ion batteries, but a business model based on a sharing economy for energy storage technology. Under this model, suppliers, owners of idle energy storage resources, could transfer the right to use the resources to demanders. The suppliers obtain the improved utilization rate of the idle resources and additional economic profits, and the demanders create greater value by using the energy storage resources. Consequently, total social welfare is improved.  
There are three types of resources that can be shared as follows:  
* Energy storage technologies and facilities: With the permission of policy and technology, the idle energy storage resources based on different kinds of technologies (mechanical storage, electrochemical storage or electrical storage) can all be shared and provide services to demanders.
* Energy storage application scenarios: According to the location and function of energy storage facilities, the application scenarios are classified into three types: power generation side, grid side and user side. The energy storage facilities in these scenarios can all be shared. For example, a wind farm can store electricity either in the SES facilities on the power generation side or the facilities on the user side, which depends on the cost of use. Moreover, the energy storage resources from different scenarios could be integrated as a whole to provide sharing services.
* Energy storage product form: Electric energy is the main product form for SES because energy storage facilities are shared to store or release electric energy. Otherwise, the service provided by energy storage is also a sharing product. For example, the peak regulation service of several RESs could be provided by an SES facility. Information is a by-product in the sharing process of energy storage resources.  
A basic framework of SES is shown in Figure 1. It can be seen that the SES is made up of one or more energy storage facilities within a certain range. The electricity from centralized or distributed generation will be stored in SES facilities through the power network when wind and solar curtailment happens or power generation exceeds its planned output. Moreover, SES can also purchase electricity from power companies when electricity price is low. In the discharge mode, SES simultaneously releases the stored electricity to different users such as residential users, industrial users and power plants. For SES, the existing energy storage functions and profit models, such as peak-valley price arbitrage, smoothing generation curve and power auxiliary service, can be realized in a sharing way.  
Figure 1: The basic framework of sharing energy storage (SES).
## 5 Implementation and User Scenario  
### Implementation  
We opt Eclipse ATL (Atlas Transformation Language) model for its graphical modeling and transformation capabilities to ensure model-to-code transformations across different platforms. Furthermore, Python (Version 3.10), Google Colab is used to simulate user behavior, providing a convenient method to test different scenarios and verify user engagement. The e-commerce application is modeled in the editor zone, and user behavior processes are simulated using Python. In Table 3, we have developed a set of rules to model a reasoning-based predictive decision support system. To improve the user experience, we integrate the AI-MDD-UX framework to enhance the system's ability to dynamically update UIs based on evolving user preferences and behaviors. It continuously collects data on user interactions, analyzes behavior patterns, and monitor past purchases to personalize and refine UIs with relevant products. The system then adjusts UI elements dynamically based on browsing history, search queries, and real-time user actions to ensure a personalized and engaging experience.
# Single-Shot Coherent X-Ray Imaging Instrument at PAL-XFEL
## 2 Materials and Methods
### Pump Laser System  
Combining XFEL and a femtosecond pump laser enables us to carry out pump-probe experiments. As both sources generate femtosecond pulses, ultrafast dynamics in various fields can be studied [24; 25; 26; 27]. The PAL-XFEL beamline is equipped with the femtosecond IR laser (Legend Elite DUO HE, Coherent Inc., Santa Clara, CA, USA) system to provide the pump source [28]. The maximum pulse energy of the fundamental mode (\(\lambda\) = 800 nm) is 5 mJ, and its pulse duration is \(\sim\)40 fs in full width at half-maximum (FWHM) after passing a compressor. The harmonic generator (\(\lambda_{2}\) = 400 nm and \(\lambda_{3}\) = 266 nm) and the optical parametric amplifier are equipped to generate a broad wavelength range, covering from 240 nm to 2600 nm. By reducing or extending the optical path using a motion stage, a time delay is changed for time-resolved experiments. Furthermore, a phase shifter will be established to change the time delay with improved beam pointing stability.