esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of OMMT on the Properties of Vehicle Damping Carbon Black-Natural Rubber Composites
## 2 Experiment Section  
### Materials and Preparation  
Natural rubber (SCR, Hainan Natural Rubber Company's product, Haikou, Hainan, China) was used as basis material. Sulfur (99.5% purity) was purchased from Jinchangsheng Co., Guangzhou, Guangdong, China. Organic montmorillonite (OMMT, 85%, organic modified by quaternary ammonium salt, particle size 44 \(\upmu\)m, colloidal viscosity 4.0 mPa\(\cdot\)s) was purchased from Zhejiang Fenghong clay chemical Co., Anji, Zhejiang, China. Cabon Black (N330) was purchased from Cabot Corporation, Tianjin, China. \(N\)-cyclohexyl thio-phthalimide (CTP-70 GR, a kind of scorch retarder), \(N\)-1,3-dimethylbutyl-\(N^{\prime}\)-phenyl-p-phenylenediamine (4020, a kind of antioxidant), Poly (1,2-dihydro-2,2,4-trimethyl-quinoline) (RD, a kind of antioxidant) and Zinc oxide (ZnO-80) were purchased from Ningbo Actmix Rubber Chemicals Co., Ltd, Ningbo, China The remaining ingredients were provided by Jinchangsheng Co., Guangzhou, Guangdong, China. Table 1 shows the specific formula proportion of samples.  
The formula of this study was listed in Table 1, on the basis of weight. In the preparation process, all raw materials were weighed according to the formula ratio, and the actual weight of NR was 200 g. Firstly, NR was dried at 60 \({}^{\circ}\)C for 4 h before use. Then, it was processed with stearic acid and microcrystalline wax in sk-160B two-roll mill (Shanghai Plastics and Rubber Machinery Co., Shanghai, China) at room temperature, the initial distance between the two rollers was 2 mm, completely encased on the roller for 2 min. Next, the CTP (\(N\)-cyclohexyl thio-phthalimide, a kind of scorch retarder), 4020 (\(N\)-1,3-dimethylbutyl-\(N\)-phenyl-p-phenylenediamine, a kind of antioxidant), RD (poly (1,2-dihydro-2,2,4-trimethyl-quinoline), a kind of antioxidant), mixed for 3 min. The following were carbon black N330 and OMMT, mixed for 3 min. After that, sulfur and ZnO (Zinc oxide) were added into the blended compound. Next, set the distance between the two rollers to 1 mm, made the \"triangle bag\" in the process for 5 times. Lastly, the compound obtained was molded into sheets in a Type Plate Vulcanizer (XLB-400 \(\times\) 400 \(\times\) 2 50 T) (Qingdao Yadong Machinery, Qingdao, China) at 150 \({}^{\circ}\)C for 15 min.
## 4 Smart City Service and the CEI Continuum
### Key Benefits of CEI Continuum in Support of Smart City Applications
By leveraging these benefits, the edge-cloud continuum can significantly enhance the effectiveness and efficiency of Smart City initiatives, enabling more responsive, resilient, and intelligent urban systems. We summarise these benefits in table form (see Table 1) with more detail in the points below:  
* Smart governance: The CEI continuum can enhance smart governance by providing secure, scalable infrastructure for e-services, with edge computing ensuring low-latency responses for critical services and cloud computing supporting data analytics for policymaking. * Smart Economy: The CEI continuum can support smart economy applications by providing the infrastructure for secure, distributed systems, with edge computing supporting local economic activities and cloud computing enabling broader economic analysis and planning. * Smart Mobility: The CEI continuum can support smart mobility applications in several ways. For traffic management systems, it enables real-time data processing at the edge for immediate traffic management decisions, while using cloud resources for long-term pattern analysis and planning. IoT devices like traffic sensors and connected vehicles provide the data input [40]. For personal mobility services such as bike and car share, edge processing enables context-aware suggestions/decisions and support for personal mobile devices. * Smart Environment: The CEI continuum can support smart environment applications by, for example, enabling real-time data collection and processing from distributed IoT sensors, with edge computing providing immediate responses to environmental issues and cloud computing supporting long-term environmental modelling and policy planning. * Smart Living: The CEI continuum can enhance smart living applications by providing a seamless integration of IoT devices in homes and public spaces. For example, in emergency management scenarios, edge computing ensures low-latency responses for critical services such as emergency response and in extreme situations (disaster) supports disconnected operations. Conversely, cloud computing can augment edge and IoT processing in situations such as data-intensive applications like personalised health recommendations. * Smart People: The CEI continuum can support smart people initiatives by providing accessible, user-friendly interfaces at the edge for citizen engagement, while leveraging cloud resources for data-intensive applications like collaborative urban planning and social network analysis.
# Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
## 4 Font Recommendation System through Content Emotion Analysis
### Comparison and Selection of Computational Models  
To evaluate the two computational models presented in this paper, participants were provided with ranked lists of recommended fonts based on specific expressions and text using each calculation method, as conducted in previous studies [6]. A total of 34 participants from diverse age groups, ranging from their twenties to their fifties, were involved in the evaluation. As a result, the model employing the Pearson correlation coefficient was assessed as the one that effectively recommends appropriate fonts based on the emotional content. Consequently, the font recommendation system proposed in this study is implemented using a computational model that utilizes the Pearson correlation coefficient.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Emotion of Content**} \\ \hline Font keywords (19) & angry & contempt & joy & trust \\ Angular & \(-0.7627\) & \(-0.909\) & & 0.388 & 0.914 \\ Formal & \(-0.872\) & \(-0.972\) & & 0.557 & 0.975 \\ Disorderly & \(0.93\) & \(0.995\) & & \(-0.665\) & \(-0.996\) \\ Technical & \(0.132\) & \(-0.14\) & & \(-0.14\) & 0.152 \\ Gentle & \(-0.984\) & \(-0.996\) & & \(0.797\) & 0.994 \\ Modern & \(0.93\) & \(0.995\) & & \(-0.665\) & \(-0.996\) \\ Harmonious & \(-0.95\) & \(-0.999\) & & \(0.706\) & 0.999 \\ Stiff & \(0.837\) & \(0.658\) & & \(-0.994\) & \(-0.649\) \\ attention-grabbing & \(-0.838\) & \(-0.954\) & & \(0.501\) & 0.958 \\... & & & & \\ Determined & \(-0.94\) & \(-0.997\) & & \(0.684\) & 0.998 \\ Playful & \(-0.645\) & \(-0.415\) & & \(0.921\) & 0.404 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The correlation coefficient between content and font keyword emotions (Partial Data included).
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 2 Internet of Vessels Architecture
### The Technical Architecture of the Networks Layer
#### 2.3.1 Self-assembling Networks of the IoV with Ad Hoc Technology
The node could judge the correction of the message from the native broadcast. If the node finds the link of active routing breaking in the routing table, the node could send the RERR to the host and delete the link in the routing table. The node which received the RERR messages could delete the route including the disconnected link in the routing table. When the route fails to connect to the table due to the source node movement, the source node would make a route request again. The TORA is a kind of distrusted algorithm. It could provide various routing between the source node and destination node [22]. The TORA could distribute a height value to each node and the height value of the destination node is the lowest. As a result, compared with the height value between adjacent nodes, various efficient paths could be established and the direction of the path is from the higher node point to the lower node. The advantage of the TORA is reducing the time delay during the exchange of information. The application of the ad hoc network technology in the IoV system has the great advantage that using the ad hoc communication to monitor and report the position of the vessels could have a large network latency and does not need the high speed data transmission [23]. However, there are some technical barriers to applying the ad hoc technology in the IoV system. For instance, in the ocean, there is a long distance between ships. From chart data of the vessel traffic services (VTS), it can be seen that ships in ports, wharfs and offshore have a concentrated distribution, while the ships are far from each other in open sea. As a result, it is suitable to use ad hoc network technology in ships distributed in concentrated areas. Otherwise, it is better to choose other networks such as the satellite centric model networks, opportunistic networks and so on.
# Improved Smart Pillow for Remote Health Care System
## 3 Results
### Comparision with ZHF and DHF Approaches  
Technically, the \"Smart Magic Pillow\" is incomparable with ZHF and DFH as both have a CBT in each period, but the pillow only has a CBT when position and time requirements are met. Since the principle is similar, the evaluated comparison is shown in Table 3.  
Firstly, ZHF requires a heater, so this is its biggest weakness, not only because of its energy demands, but also because it must connect to mains power--making it less flexible and uncomfortable for the user. The advantage for ZHF from the use of a heater is its initial response time being lesser than DHF, so with the help from a heater, it can reach thermal balance faster.  
For DHF, it needs a longer initial response time, so without a heater, thermal equilibrium for heat flux takes more than 20 min. It means the user must wait at least 20 min for a relatively accurate reading from DHF.  
Accuracy is significant for measurement. There is no absolute error consideration as factors such as attached position, ambient temperature, different products and so forth could affect the test result. From the works of various researchers [6; 8; 31], the error of ZHF is about 0.3 to 0.4 \({}^{\circ}\)C and DHF is 0.1 to 0.5 \({}^{\circ}\)C.  
The possible error area of DHF is wider except for the previously mentioned factors, so the distances between sensors in both the horizontal and vertical axes matter, such that it decides the size of DHF and results in a degree of comfort as it is physically taped to the human body.  
The smart pillow is not qualified as a thermometer as it cannot guarantee a reading. Only if both two conditions are met: firstly, if the head rests right on a sensor--fortunately, the area of head, including cervical vertebra, is relatively large, and three sensors increase the occurrence of the condition; secondly is time, as the head must rest on the sensor for more than 10 min.  
From the simulation work in Figure 11, only two minutes for the sensor reaching 36.8 \({}^{\circ}\)C is within an ideal environment, but in the actual experiment, even 5 min is not enough as it is influenced by ambient temperature and the material of pillowcase, so 10 min is required to pick up the temperature reported every 5 min. From Figures 12 and 13 there are three temperatures that qualify for CBT. If there is a qualified CBT, which is satisfied in one night, it is more accurate and more comfortable as the measurement is determined without any user awareness.  
In next section, the Remote Healthcare System with the Smart Magic Pillow will be concluded, including what was achieved and the parts that need improvement, and then future research directions are considered.
# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
## 4 Heterogeneous WBAN Simulator
### The Results of Measurements Used in the Simulator
#### 4.2.1 Measurement Stand and Scenarios  
In [42], the measurement stand and measurement scenarios are presented in detail. In this section, only the key information will be highlighted. All measurement scenarios for off-body communication were implemented by attaching the stationary reference node (RN) to the corridor wall. A single mobile node (MN) was placed on the body of a moving person who was walking along the axis of a narrow corridor. The measurements were carried out for two dynamic scenarios (SCN), i.e., approaching (APR) and departing (DEP), with relation to the RN.  
Each mobile and reference node was heterogeneous, in the sense that it had two simultaneously working, time synchronized radio interfaces:  
* NB radio module CC1120 from Texas Instruments, working in the UHF (Ultra High Frequency) 868 MHz band [43];
* UWB radio module UWB DWM1000 from DecaWave, working in the SHF (Super High Frequency) 6489 MHz band [27].  
From the many typically selected placements for wearable devices, three were selected as representative for WBAN applications, including heart rate monitors, multimedia glasses, wireless headphones, smartwatches, etc. [2,44]. These are: the right side of the head (HE\({}_{\text{R}}\)), the front of the chest (TO\({}_{\text{F}}\)), and the left wrist (AB\({}_{\text{L}}\)).
# Targeting KRAS in PDAC: A New Way to Cure It?
## 3 KRAS Mutations in PDAC  
RAS (rat sarcoma virus) genes constitute one of the most commonly mutated gene families in malignant tumors [52]. The RAS gene family includes three genes: KRAS, HRAS and NRAS. KRAS is the most common mutation type of the RAS gene, accounting for 80% of RAS gene-related malignancies. The KRAS gene encodes two splice variants using different exon 4 s, producing KRAS4A and KRAS4B. It has been experimentally demonstrated that both the isoforms are associated with tumor formation [11]. KRAS mutations have mainly been found in lung cancer (32%), PDAC (86%), and colon cancer (41%) [53; 54; 55]. The most common isoforms of KRAS in PDAC are KRAS\({}^{\text{G12D}}\) (45%) and KRAS\({}^{\text{G12D}}\) (35%) [56].
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 1 Introduction
Finally, a credibility incentive mechanism evaluates each node's contribution to the global model and the impact of latency on global training. This approach enhances the FL system's ability to identify malicious and low-quality ENs throughout the entire FL training process. Additionally, CS-FL employs hybrid encryption algorithms to effectively protect the privacy of model parameters. 4. Extensive experimental evaluations demonstrate that the proposed CS-FL method significantly outperforms FedAvg, FedCS [13], and Oort [14] in terms of accuracy and convergence speed in FL tasks under three major attack models: local poisoning attacks, communication delay attacks, and identity spoofing attacks. Specifically, in the presence of 30% malicious nodes, CS-FL achieves a 12% higher training accuracy than Oort in a model poisoning attack scenario. In the channel deception attack scenario, CS-FL reduces the average waiting time by nearly 40% compared to FedCS. Moreover, in complex EC threat scenarios, CS-FL reduces the attack success rate from 75 to 85% to below 5-20%, significantly outperforming the classical FedAvg method. These results highlight CS-FL's superior robustness and defense performance across various attack scenarios. The remainder of this article is organized as follows. Section 2 reviews recent research focused on utilizing blockchain to decentralize FL in EC scenarios and enhancing system robustness by introducing novel node selection mechanisms. Section 3 provides a detailed description of the Zone-Chain architecture. Section 4 introduces the proposed CS-FL algorithm, which incorporates a security credibility evaluation mechanism, a hybrid encryption algorithm, and a node selection strategy. Section 5 presents a comparative analysis of CS-FL against other baseline algorithms through extensive experimental simulations under various attack models. The simulation results are analyzed to validate the security and effectiveness of CS-FL. Finally, Section 6 concludes the paper with a summary of the findings.
## 3 Design of Trajectory Tracking Control System  
Figure 2 shows a frame diagram of the proposed trajectory tracking control system. The vehicle obtains the target road information and the real-time state parameters of the vehicle through the planning layer, which are input into the MPC, and the ideal front wheel angle is obtained by PSO optimization calculation. At the same time, the ideal yaw rate, the actual yaw rate, and the actual yaw angle are obtained by the ideal reference model as the control input of the sliding mode controller. The stability controller is designed based on the sliding mode control theory, the rear wheel angle is calculated, and the additional yaw moment based on the yaw rate and the yaw angle of the center of mass is obtained. In the stability controller of the phase plane method, the instability degree of the vehicle is identified according to the \(\beta-\dot{\beta}\) phase plane of the vehicle, and the additional yaw moment \(\Delta\)M is calculated according to the designed partition controller. PID control is used to track the target vehicle speed and solve the driving force \(\text{F}_{t}\). Finally, a torque distributor is designed to distribute the torque of the in-wheel motor of each wheel according to \(\Delta\)M and \(\text{F}_{t}\) and considering the driving anti-slip and other factors.
## 6 Experimental Verification  
### Control Logics Used for Experimental Comparisons  
In order to verify the performance of the proposed model in a laboratory environment, four different control logics were used. The first was a default logic integrated with the MLS model, linear quadratic tracking control logic [29]. The next was based on dynamical neural networks, the approach that showed significant performance in control of nonlinear dynamical systems [30]. The third was the OEI controller, the previous proposal of the authors of this paper [17], a hybrid structure based on the combination of OENN and OEANFIS that showed a significant performance in the control of nonlinear systems weighted with notable disturbances. Finally, the last was the model proposed in this new research, OENN + AOG structure. A brief explanation of all four control logics that will be used in this section, follows.
# Explore Long-Range Context Features for Speaker Verification
## 7 Results and Analysis
### Comparison and Analysis of Three Methods on the Vox Test Sets  
We experiment with three methods on three test datasets, Vox1-O, Vox1-E, and Vox1-H. EER and minDCF are used as metrics. The training dataset of all experiments in this part only contains Vox2-dev without augmentation. As displayed in Table 2, three methods and their combinations are compared.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Spks** & **Utts** & **Trials** \\ \hline Vox1-O & 40 & 4708 & 37,611 \\ Vox1-E & 1251 & 145,160 & 579,818 \\ Vox1-H & 1190 & 137,924 & 550,894 \\ CN-eval & 196 & 17,973 & 3,484,292 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The detail of test datasets.  
Firstly, we analyze the results of three methods. Three methods all achieve performance improvements. HS-ResNet50 exceeds the ResNet50 by 30% on EER and minDCF. The stronger ability to model long-range dependencies with HS-ResNet has proven that it is able to achieve great performance improvements through experiments. Its unique _split-stack_ structure effectively collects more long-scale features and more long-range information. The cross-conv module achieves 20% improvements on EER and minDCF, compared with the no cross-conv method. By replacing the original 3 \(\times\) 3 convolution kernel with a 1 \(\times\) 5 and 5 \(\times\) 1 convolution kernel, the RFs of the model are enlarged, and the ability of long-range feature extraction is enhanced. DSSA module achieves 15% improvements on EER and minDCF, which uses an explicit sparse attention strategy to capture effective long-range dependencies globally in each channel, and its parameters are increased just a little.  
Then, we analyze the complementarity between the three methods. The HS-block and cross-conv focus on enlarging the local receptive field by stacking convolution filters and enlarging RFs in a single layer, respectively. The DSSA module focuses on integrating long-range information and extracting long-range context features globally. In theory, they are complementary to each other. Thus, we combined these methods into ResNet50. Compared with HS-Net50, HS-Net50+cross and HS-Net50+DSSA both achieve 15% improvements on EER and 25% improvements on minDCF, and HS-Net50+cross+DSSA achieve 25% improvements on EER and 35% improvements on minDCF. These demonstrate that there are strong complementarities between the three methods. The detection error trade-off on Vox1-H are shown in Figure 5. As can be seen from the figure, our proposed method achieves significant performance improvements.
# Fatigue Effects on the Lower Leg Muscle Architecture Using Diffusion Tensor MRI
## 3 Results
On the other hand, the ratio between V\({}_{\text{m}}\) and the product of ACSA\({}_{\text{max}}\) and L\({}_{\text{m}}\) showed the lowest CV%, which may be a good indicator that the shape of the muscles was similar between subjects. The lower the CV%, the lower the variability. Within each muscle of the leg, and in both conditions, the CV was the lowest (4.2-8.6%) for the ratio between V\({}_{\text{m}}\) and the product of ACSA\({}_{\text{max}}\) and L\({}_{\text{m}}\), followed by the ratio between V\({}_{\text{m}}\) and ACSA\({}_{\text{max}}\) (4.6-11.2%). The ratio between V\({}_{\text{m}}\) and L\({}_{\text{m}}\) had the highest CV (12.8-29.6%). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**V\({}_{\text{m}}\)/ACSA\({}_{\text{max}}\)-L\({}_{\text{m}}\)**} & \multicolumn{3}{c}{**V\({}_{\text{m}}\)/ACSA\({}_{\text{max}}\)**} & \multicolumn{3}{c}{**V\({}_{\text{m}}\)/I\({}_{\text{m}}\)**} \\ \hline  & \multicolumn{3}{c}{**Pre**} & \multicolumn{3}{c}{**Post**} & \multicolumn{3}{c}{**Pre**} & \multicolumn{3}{c}{**Post**} & \multicolumn{3}{c}{**Pre**} & \multicolumn{3}{c}{**Post**} \\  & \multicolumn{3}{c}{**Mean**} & **CV** & **Mean** & **CV** & **Mean** & **CV** & **Mean** & **CV** & **Mean** & **CV** & **Mean** & **CV** \\ \hline GM & 0.59 \(\pm\) 0.04 & 7.5 & 0.59 \(\pm\) 0.05 & 8.6 & 13.50 \(\pm\) 0.86 & 6.3 & 13.50 \(\pm\) 1.29 & 9.5 & 8.23 \(\pm\) 2.43 & 29.6 & 8.40 \(\pm\) 2.35 & 27.9 \\ \hline GL & 0.56 \(\pm\) 0.03 & 5.1 & 0.56 \(\pm\) 0.05 & 8.5 & 11.32 \(\pm\) 1.26 & 11.2 & 11.28 \(\pm\) 1.18 & 10.5 & 4.68 \(\pm\) 1.13 & 24.2 & 4.87 \(\pm\) 1.12 & 23.0 \\ \hline SOL & 0.49 \(\pm\) 0.03 & 5.3 & 0.49 \(\pm\) 0.02 & 4.2 & 14.77 \(\pm\) 0.91 & 6.1 & 14.79 \(\pm\) 0.69 & 4.6 & 12.31 \(\pm\) 2.08 & 16.9 & 12.29 \(\pm\) 1.98 & 16.1 \\ \hline TA & 0.50 \(\pm\) 0.04 & 7.3 & 0.50 \(\pm\) 0.02 & 4.2 & 13.76 \(\pm\) 0.96 & 7.0 & 13.78 \(\pm\) 0.78 & 5.6 & 3.96 \(\pm\) 0.53 & 13.4 & 3.91 \(\pm\) 0.50 & 12.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean values \(\pm\) SDs and the coefficient of variance (in %) of the ratio between V\({}_{\text{m}}\) and the product of ACSA\({}_{\text{max}}\) and L\({}_{\text{m}}\), the ratio between V\({}_{\text{m}}\) and ACSA\({}_{\text{max}}\), and the ratio between V\({}_{\text{m}}\) and L\({}_{\text{m}}\) for the GM, GL, SOL and TA muscles.