esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The irregularity of the frames supported by stepped foundation in mountainous areas often leads to problems such as continuity variation, redundancy reduction, and complexity of the transmission path, which are significantly different from common regular frame structures. This also makes the existing research results on the collapse of the common rule frame structure not fully applicable, so it is necessary to study the progressive collapse resistance performance of the frames supported by stepped foundation in mountainous areas. At present, the research on progressive collapse mainly focuses on earthquake-resistant mountainous structures, while the dynamic effects of progressive collapse of seismically isolated mountainous structures are less studied. The lateral stiffness of the isolation layer of the seismic isolation structure is weak; the alternative load transfer path and the remaining structure system exist in different ways from the non-seismic  
Figure 1: Schematic diagram of structure supported on stepped foundations. isolation structure under unexpected disaster loads such as impact, fire and foundation collapse. Therefore, compared with traditional seismic structures, the dynamic response characteristics and the transmission path of dynamic effects of progressive collapse of seismic isolation structures need to be studied. Based on the fact that China's earthquake disasters are frequent and that the inherent vertical rigidity of mountainous building structures is irregular, resulting in poor seismic performance [14], seismic isolation technology is constantly being developed and applied to mountainous buildings. Mountain isolation structures themselves possess their own particularity, so their failure mode and mechanical performance are very different from ordinary flat isolation structure systems [15]. After the individual support of the seismic isolation structure exits work due to accidental damage, the vertical load on the adjacent seismic isolation support will increase instantaneously. Even if the transient balance can be maintained under the action of vertical load, a slight horizontal excitation, such as an earthquake or wind vibration, may induce successive instability and collapse of the seismic isolation support. If the damage of the bearing is not identified in time, some of the bearings lose their vertical bearing capacity and the structure is subjected to earthquake action at this time. In this way, the vertical unbalanced load is coupled with the horizontal seismic action, causing the seismic isolation structure to appear disproportionate to the initial damage or collapse and spread, which is very dangerous for seismic isolation structures. Therefore, this paper conducted research on the collapse resistance performance of the bottom column (seismic bearing) of the base isolation mountain falling layer frame under the bidirectional dynamic coupling excitation after the accidental failure and provides a reference for the design of mountainous isolation buildings.
## 5 Conclusions  
In summary, the results of the pilot operation of the product recommendation system are as follows:  
* During the 4-week pilot period, all 91 newly introduced SKUs were sold, achieving a 100% sales rate;
* Out of the 91 recommended SKUs, 80 SKUs were sold within 1 week, demonstrating an 88% recommendation accuracy;
* The overall average daily sales of recommended products in the selected seven stores was 0.63, which is 1.75 times higher than the overall average;
* When compared to the average sales of identical SKUs across all stores, 66% of the SKUs recorded higher sales.  
These results indicate a substantial level of accuracy and effectiveness in the pilot operation of the recommendation system. The business owners' satisfaction rating of 4.2 also reflects continued interest in the system's ongoing use.  
The significance of this study lies in the expansion of personalized recommendation research, primarily conducted on online platforms, to offline businesses such as convenience stores. The study implemented and validated a store-based collaborative filtering model for product recommendation, considering users across various store locations. Store similarity was calculated based on each store's category-specific sales history. Leveraging collaborative filtering after analyzing SKU sales histories for each store, the model recommended products that were predicted to perform well in terms of sales, taking into account additional business considerations such as headquarter and store product operations.  
Several areas require further attention and research. The 12% failure rate of recommended products and the underperformance of 34% when compared to overall average demand indicate the need for additional algorithm refinement and optimization. Furthermore, additional validation is needed for store similarity assessment, considering factors such as store locations, product range, and sales volume. Exploring weight adjustments for various business requirements in recommendation score calculation is necessary. Hybrid recommendation systems using cluster analysis and item-based recommendation systems using basket analysis are potential areas for further research. Moreover, a financial evaluation, such as assessing the overall sales and profitability impact of introduced products, needs to be considered for a comprehensive assessment of the recommendation system's effectiveness from a financial perspective.  
Conceptualization, J.L. and J.K.; methodology, J.L.; software, J.L.; validation, J.L. and J.K.; formal analysis, J.L.; resources, J.L.; data curation, J.L.; writing--original draft preparation J.L. and J.K.; writing--review and editing, J.L. and J.K.; visualization, J.L.; supervision, J.K.; project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data is unavailable due to ethical restrictions of E company.  
The authors declare no conflict of interest.
# Optimization of Cellular Automata Model for Moving Bottlenecks in Urban Roads
## 2 A Cellular Automaton Model for Urban Roads Considering Vehicle Heterogeneity
### Vehicle Update Rules for Road Segments
#### 2.2.3 Lane-Changing Rules  
This article categorizes lane-changing rules into rational lane changing and forced lane changing. Rational lane changing refers to the behavior of a vehicle changing lanes without affecting the following vehicle in the target lane, whereas forced lane changing may cause the following vehicle in the target lane to decelerate.  
1. Rational Lane-Changing Rules: When the target vehicle cannot travel at its maximum speed in the current lane and cannot accelerate and there is sufficient distance in the target lane for acceleration without affecting the following vehicle in that lane, then a rational lane change is performed. \[v_{n}(t)<v_{max}(t),L_{n}(t)<L_{sa}(t)\] (17) \[L_{n,front}(t)\geq L_{sa,front}(t),L_{n,back}(t)\geq L_{sk,back}(t)\] (18) where \(L_{sa,front}(t)\) is the critical safe distance for the vehicle to accelerate in the target lane, and \(L_{sk,back}(t)\) is the critical safe distance for the following vehicle in the target lane to maintain its speed.
2. Forced Lane-Changing Rules: When the target vehicle cannot travel at its maximum speed in the current lane and cannot accelerate, but there is sufficient distance in the target lane for acceleration, which may cause the following vehicle in that lane to decelerate but not to perform emergency braking, then a forced lane change is performed. \[v_{n}(t)<v_{max}(t),L_{n}(t)<L_{sa}(t)\] (19) \[L_{n,front}(t)\geq L_{sa,front}(t),L_{sd,back}(t)\leq L_{n,back}(t)<L_{sk,back }(t)\] (20) where \(L_{sd,back}(t)\) represents the critical safe distance for the following vehicle in the target lane to decelerate.
## 4 Security Analysis of the MAPP Protocol
### Resistance to Attacks
#### 4.2.2 Modification Attack  
In our protocol, message integrity is achieved using a short signature generated using an elliptic curve. The sender generates a message m, then calculates a signature \(\sigma_{i}\) over m by hashing m and then mapping it to a point on the elliptic curve using the secret key \(sk_{i}\). The sender attaches the signature and the public key \(pk_{i}\) to allow the receiver to calculate a signature over the received message and then compare the transmitted signature and calculated signature to accept or reject the message. If the verification of \((g_{2 ZID},\sigma_{i})=e(pk_{i},~{}H(m))\) is true, the receiver accepts the message, else it drops it. In this way, by verifying the signature over each message, our protocol ensures the message integrity and prevents-message alteration that proves our protocol's ability to resist modification attacks.
## 4 Results and Discussion
### Security Analysis Using an Adversarial Attack Model
#### 4.2.1 Adversarial Attack Model  
Our adversarial attack model consists of four different types of attacks:  
1. DoS/DDoS: We simulated high-volume traffic from a single source and multiple sources to disrupt service availability. We used the same features, like packet rate, duration, and protocol type, to mimic attack patterns already existing in the real dataset (CSE-CIC-IDS2018).
2. Brute force: We simulated repeated login attempts with varying usernames and passwords to gain unauthorized access to the organization's network or systems. Also, we used features like failed login attempts and source IP addresses.
3. Port scanning: We simulated a port scanning attack by sending packets to multiple ports to identify vulnerabilities in the organization's network and systems. We used features like port numbers, packet count, and response time.
4. Malware: We simulated a malicious payload delivery and execution attack to gain persistent access to or control of the organization's systems. We used features like file size, execution behavior, and network activity.  
Figure 49: Hyperparameter tuning for XGBoost on CIC-IDS-2018 dataset.  
Figure 50: Hyperparameter tuning for XGBoost on CIC-IDS-2017 dataset.
# Effect of Ultrasonic Treatment on Taste and Flavor Quality of Japonic Rice
## 3 Results  
### Changes in Resistant Starch Content  
The effect of ultrasound treatment on resistant starch content in cooked rice JJ830 is shown in Figure 2. The effect of ultrasound treatment on resistant starch content in cooked rice was not significant under low ultrasound treatment intensity (100 W, 10 min). However, there was a significant increase in resistant starch content at 200 and 300 W for 20 min of treatment. The resistant starch concentration at 200 W was higher than that at 300 W. The resistant starch content increased from 123.75 mg/g to 229.76 mg/g, reflecting an 85.66% increase. The above phenomenon is attributed to the fact that ultrasound treatment with medium power density and longer duration may be more favorable for the formation of complexes, whereas vigorous ultrasound treatment (300 W, 30 min) may disrupt the amylose chains and produce short chains that are too short to participate in the production of single helix complexes, which reduces the number of complexes [10]. At the same time the powerful shock waves and mechanical forces of high-intensity ultrasound treatment can disrupt the structure of the already formed starch-lipid complexes. Based on these results, the subsequent experiment was conducted using 200 W ultrasound power.  
Figure 2: Effect of ultrasound treatment on amount of resistant starch in cooked rice JJ830. Different letters under the same parameter indicate the significant differences (\(p<0.05\)).
## 5 Validation of SRRV Deviation Identification Method with Field Measurement Data  
To verify the reliability of the proposed method for identifying SRRV deviation based on the wheel-rail force trend component, dynamic measurement data from high-speed integrated inspection trains passing through two sets of high-speed turnouts with different SRRV deviations were selected for analysis (Figure 12). A comparison and validation were conducted against the on-site measured data of SRRV deviation for the analyzed turnouts.
# On Analyzing Beamforming Implementation in O-RAN 5G
## 4 O-RAN Beamforming
### Open Research Issues
#### 4.2.1 Fronthaul Model Concept  
This is a new specification that O-RAN has introduced, which will allow the front-haul interface to carry spatial layers, which means fewer data and reduces the complex timing for O-RU and OCU/O-DU link compared to the 3 GPP specifications. Figure 6 shows the digital beamforming diagram in O-RAN compared to 3 GPP. The comparison in Figure 6 shows the difference in the implementation of digital beamforming between 3 GPP and O-RAN (Category B), where the precoding has been moved to O-RU. Thus, this implementation will achieve more reliability due to the low channel load in the data flow from O-DU to O-RU, where the link's capacity requirement will be less by reducing the data bit of scheduling and beamforming commands as its one of the main contents in C-plane data flow between the O-DU and O-RU, as shown in Table 1.  
C-Plane messages have two layers. The first layer is the transport layer, and the eCPRI header including corresponding entries implemented to indicate the message type. The second layer is an application layer including required fields for control and synchronization; moreover, within the application layer, some sections define the characteristics of the U-plane. Each section transfers specific parameters to O-RU individually. There are more than eight sections in C-plane. Each section has a set of parameters that carry the data between O-DU  
Figure 6: Digital beamforming block diagram in 3 GPP and O-RAN. and O-RU. These parameters are managed by M-Plane and can specify the packet size of the C-pane based on the used section from O-DU and O-RU, as shown in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Plane** & **Name of Messages** & **Contents** & **Periodicity** \\ \hline \multirow{8}{*}{C-Plane} & \multirow{4}{*}{\
\begin{tabular}{c} Scheduling Commands and \\ Beamforming Commands \\ \end{tabular} } & \multirow{4}{*}{
\begin{tabular}{c} Scheduling information, FFT size, CP \\ length, subcarrier spacing, UL \\ PRACH scheduling, DL and UL \\ beamforming commands (e.g., beam \\ index) and scheduling \\ \end{tabular} } & \multirow{4}{*}{\(\sim\)slot} \\  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data flow information mapping between O-DU and O-RU (Source: O-RAN Alliance).
## 3 Experimental Results and Analysis
### Test Load-Displacement Curve  
Through three-point bending fracture mechanics testing, the load-displacement curve can be obtained, as demonstrated in Figure 7. This curve aids in understanding the fracture characteristics of rock-like materials [37]. The complete load-displacement curve for coal and rock typically consists of four stages: compaction, elasticity, plasticity, and failure.  
Figure 7: Test load-displacement curves for the coal samples (A represents Arrester type, S represents Splitter type, and D represents Divider type).  
According to Figure 7, coal fractures after undergoing the compaction, elasticity, and plasticity stages. Almost all coal samples exhibit an upward concave shape at the initial stage. This phenomenon is attributed to the presence of numerous pores and fractures in coal; when initially subjected to stress, these primary pores, as well as those induced by water immersion, become compacted, resulting in a significant compaction stage. The immersion of mine water reduces the slope of the curve for the coal sample during the elastic stage, indicating a weakening of the elastic properties. Additionally, after the elastic stage, the non-immersed samples typically fail abruptly at the peak load, whereas the samples immersed for 120 days experience a significant ductile deformation stage before gradually becoming unstable. This behavior is due to the increasing physical and chemical effects of mine water on the coal samples over time. It is evident that immersion in mine water causes coal samples to transition from \"sudden instability\" to \"gradual instability\" concerning their fracture characteristics. Furthermore, for coal samples that have not been immersed or have been immersed for 120 days, the slope of the elastic stage also increases with the loading rate, which aligns with the observed changes in fracture toughness and the characteristics of unstable fracture. For the Crack-arrester, Crack-splitter, and Crack-divider samples, the load-displacement curve shapes are similar, with the primary difference being the peak load magnitude.
## 4 Evaluation
### Hyperparameter Tuning  
To improve the accuracy further, we performed hyperparameter tuning for the best neural network model. Hyperparameter tuning methods include scanning through the space of possible hyperparameter values to find possible model architecture candidates [82,83]. Finding the optimal values is also referred to as \"searching\" the hyperparameter space. If the learning rate is too low for a model, the model will miss important data patterns. If the model is too heavy, it can have collisions leading to decreased accuracy. Hence, choosing suitable hyperparameters is key to the success of our neural network architecture. Even for small models, the number of hyperparameters may be significant. It is quite an intricate task to tune the hyperparameters but doing so improves the efficiency of our model significantly. Here, in Table 9, we can see that our model improves on performing the hyperparameter tuning. We applied hyperparameter tuning to CNN networks in method one as it was the best performing model and method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Set** & **LSTM** & **NN** & **CNN** \\ \hline Baseline & 0.53 & 0.63 & 0.61 \\ +transitivity & 0.53 & 0.65 & 0.61 \\ +modality & 0.47 & 0.65 & 0.65 \\ +polarity & 0.55 & 0.66 & 0.57 \\ +syntax & 0.50 & 0.67 & 0.57 \\ +doc2 vec & 0.50 & 0.70 & 0.55 \\ +word2 vec & 0.48 & 0.61 & 0.59 \\ +word2 vecSmoothed & 0.52 & 0.67 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification of problems from non-problems using deep learning networks by 5-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature Set** & **LSTM** & **NN** & **CNN** \\ \hline Baseline & 0.50 & 0.59 & 0.64 \\ +transitivity & 0.51 & 0.56 & 0.63 \\ +modality & 0.38 & 0.59 & 0.64 \\ +polarity & 0.51 & 0.58 & 0.65 \\ +syntax & 0.37 & 0.57 & 0.61 \\ +doc2 vec & 0.50 & 0.62 & 0.56 \\ +word2 vec & 0.63 & 0.64 & 0.63 \\ +word2 vecSmoothed & 0.58 & 0.64 & 0.68 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classification of solutions from non-solutions using deep learning networks by 5-fold cross-validation.  
The best accuracy of **90%** occurs for **three one-dimensional convolutional networks** and **288 units**. The number of units is an essential hyperparameter. The neural networks are universal function approximators, and they need enough 'power' to learn for the prediction task. The number of units is the critical indicator of the learning ability of the model. The simple role can require fewer units. The greater the number of units, the more complex the role of the parameter tuning. The number of units used for the best model indicates that our model is highly complex. Hence, it achieves good accuracy.  
In a similar way to the tuning of the problem phrase classifier, we present the hyperparameter tuning results for the top 10 values of the best CNN model for the classification of the solutions from the non-solutions in Table 11 and Figure 4.  
The best accuracy of **86%** is achieved for **two one-dimensional convolutional networks** and **192 units**.
# Adaptive Clustering of Users in Power Domain NOMA
## 5 Proposed Model  
This section presents the proposed model for user clustering and power allocation in a NOMA network, which is illustrated in Figure 4. At time \(t=0\), each channel gain of each user equipment, Equation (4), is stored in the entry queue; after this, the proposed Algorithm 2 is executed online to obtain the firsts clusters formation.  
Each user in the entry queue attempts to merge in an existing cluster using Algorithm 1. Every \(T\) period, each user in a p-cluster is verified. If any channel gain value of each user has a variation \(\zeta\) in this period, this user is removed from the cluster and reinserted in the entry queue. If a user is identified as an outlier in period \(T\), this user is removed from the o-cluster and added to the input queue.  
Figure 4: Flowchart of the proposed method.  
At each transmission request of BS, the users in each cluster are associated with the power coefficients determined by the dynamic power allocation of the IFSPA technique [7]. Thus, the power coefficient of a user \(i\) in a cluster \(j\), within a total of \(M\) users, is defined by:  
\[p_{j,i}=\frac{\left(\frac{\left\|h_{j,M}\right\|}{\left\|h_{j,i}\right\|} \right)^{\alpha_{j}}}{\sum_{n=1}^{M}\left(\frac{\left\|h_{j,M}\right\|}{\left\| h_{j,n}\right\|}\right)^{\alpha_{j}}}, \tag{11}\]  
where \(\left\|h_{j,M}\right\|\) denotes the highest channel gain of the cluster \(j\), \(\left\|h_{j,i}\right\|\) denotes the gain of the user who is calculating the power coefficient, \(\sum_{n=1}^{M}\frac{1}{\left\|h_{j,i}\right\|}\) is the sum of the gain of all users in the cluster \(j\), and \(\alpha_{j}\) is the power control parameter of the cluster \(j\). The greater the value of \(\alpha\), the higher the power coefficient of the user with the weakest channel gain. After this, the BS is ready to transmit to its users on a NOMA network.  
In this model, cluster formation adapts to the new users' features; retraining is unnecessary. The execution of the evolutionary clustering strategy takes place in the BS. From the estimates of the user's channel state that arrives at the BS, the clusters are updated or formed, the power coefficients of the users for downlink transmission are calculated, and transmissions begin. Next, experiments to evaluate the proposed system are described, and the global transmission transfer rate is investigated for this system.
# In Situ Assessment of Uplink Duty Cycles for 4G and 5G Wireless Communications
## 4 Discussion  
In this study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were measured at ten positions near a 5G base-station site in Belgium. The distance of the measurement positions to the base-station antenna site varied between 95 m and 1067 m. The propagation paths between the smartphone and base-station antenna site were visually identified and covered LOS and NLOS paths. The number of measurement positions were too limited to evaluate the duty cycle as a function of the path type. Nevertheless, some possible trends were observed.  
We remark that we calculated the duty cycles from QualiPoc measurements on a Samsung S20+ with an Exynos chipset. Although we measured the uplink parameters using a specific smartphone, we expect that similar observations would be found with other smartphones with similar operations in terms of transmit power management.  
The presented duty cycles were calculated from measurements with a smartphone connected to a live commercial 4G/5G radio access network; these duty cycles can be used to estimate exposure in terms of the specific absorption rate for the considered configurations and similar smartphone operations.  
Figure 10: Impact of the RSRP on the PUSCH transmit power per RB for (**a**) the network technologies and (**b**) the applications.