esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Descriptive Findings  
The research findings reported that the scientific production on the IoT in relation to Business Model Innovation subject domains had 71 publications in the WoS Core Collection database (SSCI- and SCI-indexed). All articles were written in the English language. The descriptive results of the publications depicted that the majority of them corresponded to peer-reviewed journal articles (66/71, 92.96%), followed by review articles (9/71, 12.68%), and editorial materials (2/71, 2.82%). The top five publishers among 13 were Elsevier (34), Emerald Group Publishing (12), MDPI (9), Springer Nature (6), and Taylor & Francis (4). Publication records started from 2015. Since the starting period, the production has increased steadily. From 2020 to 2021, publication count doubled, showing a dramatic increase in the published results. Further, (\(n=8.88\)) indicates the mean number of published documents in each year, with a 110% rise in the publications recorded for the 2020-2021 period. The proportional rise in the average number of articles published each year throughout this period was (\(n=16.5\)).  
The WoS Core Collection topic category dataset was utilized to classify the associated research areas under the top five major themes out of a total of 34, containing themes with at least eight papers as follows: Business (\(n=30\)), Management (\(n=24\)), Engineering Electrical Electronic (\(n=10\)), Environmental Sciences (\(n=8\)), and Regional Urban Planning (\(n=8\)). There were 50 publication sources in the dataset. The most productive journal sources with (\(n>=3\)) are _Technological Forecasting and Social Change_ (\(n=6\)), _Journal of BusinessResearch (n = 5), Industrial Marketing Management (n = 4), Sustainability (n = 4), International Journal of Production Economics (n = 3), and Long Range Planning (n = 3).  
There were 242 authors/co-authors from 35 countries led by Germany, the People's R. China, Italy, Finland, England, and the remaining 30 countries. A total of 157 institutions worldwide contributed to the domain fields. The most productive organizations with (n >= 4) publication count are Linkoping University, Lulea University of Technology, Parthenope University Naples, University of Erlangen Nuremberg, University of Padua, University of St. Gallen, and University of Vaasa. The citation records of the 71 publications derived from the WoS database statistics between 2015 and 2022 (until November) showed that the average number of citations per item was 28.95 equating to 257 per year (2015 to 2022). The total number of cited papers in that period was 2056. The top 10 publications' citation count was 1221 (average \(n=122.1\) per item).
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 3 Microstructure Evolution
### Weld Metal Microstructures
#### 3.3.1 Root Pass Welding with Ni-Based Wire and Stainless Steel Clad  
Electron beam welding (EBW) of Inconel 617 and 310 SS provided a complex microstructure due to nonequilibrium conditions and fast solidification [61]. Due to higher \(G\)/\(R\) values, the WM consisted of small columnar dendrites, which were located near the edge towards the Ni-alloy solid surface. The core of the dendrites consisted of Ni, Mo, and Nb. No cracks or other defects were found. At the weld centerline, a uniform equiaxed cellular structure was obtained. The XRD analyses showed the same \(\gamma\)-Ni matrix lattice in both the base metal and the WM of Inconel 625. The WM hardness was at the same level as Inconel 625 (230 HV), but dropped sharply to 150 HV close to the 304L SS.  
Inconel 617 and 310 SS as base metals were joined by applying different filler materials, such as Inconel 82, Inconel 617, and 310 SS. Each filler alloy provided different dilution rates by GTAW [62], and hence different microstructures were formed in the WM. With the use of Inconel 82 wire, the WM was fully austenitic with a substantial amount of dispersed intergranular precipitates (NbC and \(\gamma\)-Laves eutectic). The 304 SS filler wire also provided fully austenitic columnar dendrites. Due to the high Ni\({}_{\text{eq}}\) from the nickel-based superalloy, no \(\delta\)-ferrite was obtained. The Inconel 617 wire provided fine columnar dendrites, where Mo-rich particles were found in interdendritic regions.  
With deposition of Ni-based tubular filler wire on the 316L SS base metal for underwater welding purposes [63], no cracking was observed due to austenite microstructure (5-7% of ferrite is needed to prevent cracking) with equiaxed dendrites with appropriate toughness. However, the tensile test showed a decrease in mechanical properties in WM, corresponding to 85% tensile strength of BM (base metal) with high elongation (up to 32%) and ductile fracture mode, possibly due to lower hardness in the WM. A sharp increase of the hardness was seen near the FL, specifically in the unmixed zone, due to significant iron and some chromium diffusion in from the BM.  
Alloy 59 has been used offshore for hot-tapping, while both Alloy 59 and Inconel 625 are candidates for subsea tie-ins and weld repair [13]. These two fillers will give similar weld metal microstructures. The main difference is that the Laves phase and NbC are found in Inconel 625 welds due to the high Nb and C contents, as indicated in Table 1.
# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 1 Introduction
In summary, the contributions of this paper are as follows:  
* We innovatively introduce news framing theory into the text summarization task. This paper combines framing theory from the field of news communication with the text summarization task in natural language processing, constructing a novel frame-aware text summarization model that enriches the theoretical foundation and technical approach of text summarization. * We propose a deep learning-based news frame identification model. This paper designs a deep learning model that can automatically identify core frame elements (e.g., factual frames, responsibility frames, conflict frames) from news texts, laying the foundation for frame-aware summarization. * We construct an encoder-decoder summary generation model that integrates frame information. This model incorporates frame identification results into text representation and attention mechanisms, generating summaries that closely adhere to the core of the news frame and have high information coverage and readability. * We conducted a systematic empirical evaluation of our proposed model on the standard CNN/Daily Mail dataset. Through quantitative and qualitative analysis, we assessed the model's performance across multiple dimensions, including information completeness, news frame relevance, and reading experience readability. Experimental results show that our model outperforms several existing summarization baseline models on the standard dataset, validating the guiding value and innovative significance of introducing framing theory into the news text summarization task.
## 5 The Cyber-Physical Performance Evaluation of the ADR Control Strategy
### The QoS-Based Approach for ADR Performance Improvement in Real Communication  
The previous simulations have illustrated the impact of communication reliability on the proposed ADR control strategy. Even in a pretty good communication scenario such as PRR = 95%, the performance degradation of the ADR control strategy still occurs. One of the practical solutions is taking communication reliability into consideration while designing the ADR control strategy. However, a more cost-effective solution is to utilize the QoS mechanism provided by MQTT/MQTT-SN.  
Figure 19 compares the sum of absolute indoor temperature deviation of 30 rooms of QoS = 0 and QoS = 1. As mentioned earlier, QoS = 0 means that sender just transmits a message for one time and whether receiver receives the message or not is not the concern of the sender. QoS = 1 means that the sender will retransmits a message until it receives a respond from the receiver or reaches the maximal resend number. In Figure 19, the maximal resend number is 5.  
Figure 20 illustrates the maximal resend numbers when the sum of absolute indoor temperature deviation of 30 rooms is required less than 1 \({}^{\circ}\)C.  
Figure 19: The sum of absolute indoor temperature deviation of 30 rooms of QoS = 0 and QoS = 1.  
Figure 18: The ADR performance from PRR = 50% to PRR = 100%: (a) Power consumption; (b) Electricity fare.  
Although the QoS-based approach could significantly improve the effect of the ADR control strategy, it essentially makes a trade-off between communication reliability and communication traffic. As illustrated in Figure 21, when PRR = 50%, the network traffic grows 50%. It should be noted that the network congestion caused by the retransmissions of QoS = 1 will further reduce the PRR in return.
# Extensible Hook System for Rendezvous and Docking of a CubeSat Swarm +
## 6 Conclusions  
This paper proposed an EHS that allows a CubeSat swarm to be connected with the main objective of saving thruster propellant. This propellant saving could be achieved in two stages: during the approaching stage, by bringing the CubeSat closer, and the formation flight, keeping the CubeSat swarm together by the use of the EHS that connects the parts together.  
However, the use of the EHS in combination with the CubeSat actuators requires an advanced GNC. This paper proposed a three-level navigation component by combining GNSS, vision-based navigation, and electromagnets for the docking stage. In the case of guidance, it requires complex motion planning methods to coordinate the CubeSat swarm. Finally, control requires classical PIDs in combination with other advanced techniques, such as gain scheduling and MPC due to the non-linearities in the EHS.  
The proposed system was simulated using Matlab Simscape Multibody, obtaining meaningful information about the required forces and torques to move the CubeSat and the EHS. The obtained measurements are realistic, i.e., they can be generated by thrusters, reaction wheels, and motors. It is pending the selection of the particular actuators according to the obtained measurements.  
The proposed concept has been designed and evaluated from a mechanical and dynamic point of view; however, it is pending investigating and development of the GNC stack, which would allow a CubeSat swarm to be connected through the proposed system. This challenge is proposed as future work, with the aim of using the current simulation environment to test the GNC stack using the software in the loop concept.  
C.P.-d.-P. conceptualization, methodology, investigation, and writing; A.L.P. formal analysis and software; J.J. conceptualization and formal analysis; M.M. supervision, project administration, and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Sirin Orbital Systems AG as prime of the ESA activity: Swarm of 16U CubeSats for Space-Based Solar Power (16U4 SBSP).  
The authors declare no conflicts of interest.
## 5 The Concept of IoV
### Security Issues in IoV Communication
#### 5.5.1 Regular Network Monitoring  
Fog systems must be monitored on a regular basis to rid them of any suspicious activities. A fog network can be scanned using either static or dynamic techniques. Typically, a normal network scan involves an antivirus program and a firewall to identify threats and eliminate them to protect the network. Fog environments, however, require more security. Therefore, a new and more effective mechanism for network monitoring is required [95].
###### Abstract  
The excess sludge in the petrochemical industry is large in quantity, complex in composition, and highly harmful, and its rational disposal is of great significance for environmental protection and sustainable development. In the present study, a classification and disposal strategy for the excess sludge in the petrochemical industry is proposed. The strategy first analyzes the dioxin, flammability, corrosivity, reactivity, and leaching properties of the sludge, from which the waste type of the sludge (general waste or hazardous waste) can be determined. Then, methods of disposal can be selected depending on the type of waste and the corresponding risk analysis, enabling rationalized disposal of the sludge. To verify the effectiveness and practicability of the proposed sludge classification and disposal strategy, research on petrochemical excess sludge samples (i.e., Ah, Bl, and Cq) originated from three different regions in China is carried out as a case study. The component analysis of the above three sludge samples revealed that they are all general wastes. In addition, the possibility of employing Cq sludge for landfill, soil modification, and greening mud, as well as the risk of landfill and incineration disposal in solid waste landfills are investigated. Furthermore, natural radioactive elements uranium and thorium in Cq sludge sample are studied. The results show that Cq sludge cannot be used for landfill, soil modification, and greening mud due to excessive arsenic content. The proposed strategy provides a basis for the selection of reasonable petrochemical excess sludge disposal methods.  
Keywords:excess sludge; disposal strategy; radioactive elements; petrochemical industry +
Footnote †: journal: Applied Sciences
## 2 Thermodynamic Analysis
### Configuration II  
Regarding Configuration II, it is possible to recover a certain amount of energy, by circulating the heat transfer fluid between adsorber and desorber during the switching phase, thus transferring part of the sensible heat from the bed starting to act as a desorber (along the path (P-Q) to heat up the bed starting to work as an adsorber (along the path  
Figure 3: Configuration I (**a**): One adsorbent bed connected to one condenser/evaporator. Configuration II (**b**): Two adsorbent beds connected to a condenser and an evaporator (right-hand side). The temperature levels are described in details in Figures 1 and 2.  
R-S). Therefore, the first step is to evaluate the theoretical equilibrium temperature \(\rm T_{eq}\), which can be obtained when the temperatures of adsorber and desorber are equalized and thus no heat recovery can be performed anymore:  
\[\rm T_{eq}=\frac{\left(cp_{s}+w_{1}\;cp_{L}\right)\;T_{A}+\left(cp_{s}+w_{2}\;cp _{L}\right)\;T_{L}}{2\;cp_{s}+\left(w_{1}+w_{2}\right)\;cp_{L}} \tag{8}\]  
This equilibrium temperature is evaluated by equalizing the energy recovered by the desorber and provided to the adsorber. Clearly, also in this case, in real application in order to guarantee a proper heat transfer efficiency between the two components, a given temperature gradient, \(\rm\Delta T\), must be considered. Accordingly, the ending temperatures of the heat recovery phase will be:  
\[\rm T_{P}^{*}=T_{eq}+\frac{\rm\Delta T}{2} \tag{9}\]  
\[\rm T_{R}^{*}=T_{eq}-\frac{\rm\Delta T}{2} \tag{10}\]  
where \(\rm T_{P}^{*}\) [\({}^{\circ}\)C] represents the end desorber temperature during the recovery phase, while \(\rm T_{R}^{*}\) [\({}^{\circ}\)C] is the end adsorber temperature.  
For Configuration II, the specific upgraded heat can be calculated according to Equation (11) for the ideal case and to Equation (12) considering the real case.  
\[\rm Q_{A}=\rm\Delta H_{Ad}-cp_{s}(T_{A}-T_{eq})-\rm\Delta w\;cp_{v}\;(T_{A}-T_ {eq})-\;w_{2}\;cp_{L}\;(T_{A}-T_{eq}) \tag{11}\]  
\[\rm Q_{A}=\rm\Delta H_{Ad}-cp_{s}(T_{A}-T_{R}^{*})-\rm\Delta w\;cp_{v}\;(T_{A}- T_{R}^{*})-\;w_{2}\;cp_{L}\;(T_{A}-T_{R}^{*}) \tag{12}\]  
Similarly, to calculate \(\rm Q_{L}\), Equation (13) can be used for the ideal case and Equation (14) for the real case scenario.  
\[\rm Q_{L}^{\prime}=\rm\Delta H_{Ad}-cp_{s}(T_{eq}-T_{L})-w_{1}cp_{L}(T_{eq}-T_ {L}) \tag{13}\]  
\[\rm Q_{L}^{\prime}=\rm\Delta H_{Ad}-cp_{s}(T_{P}^{*}-T_{L})-w_{1}cp_{L}(T_{P}^ {*}-T_{L}) \tag{14}\]
## 5 Model Results
### Hourly Prediction
#### 5.1.2 Feature Importance Plot  
Although random forest cannot obtain the direction and absolute value of the regression coefficient like linear regression, it uses the decision tree as a weak classifier, which classifies data based on Gini coefficient, so the relative importance of variables can be identified through the Gini coefficient of each variable.  
The ranking results of the importance of each variable can be returned. It can be found that:  
1. The factor that has the greatest influence on short-term accidents is the average speed in this period, which accounts for a much higher proportion than other factors. This is because a usual traffic crash is related to congestion. When speed is higher, it means traffic flow is smoother. Crashes are less likely to take place. It seems contradictory  
Figure 4: Feature relative importance in Nantung direction.  
Figure 5: Feature relative importance in Soochow direction.  
with common sense that the higher the speed, the more risk of road safety. The reason is that our study is designed for general road crash analysis, aggregating driving behavior, weather, traffic volume, speed, and other factors together, not for a specific vehicle.
2. Traffic flow in the three lanes has a similar influence on accident occurrence, and traffic flow in the first lane (overtaking lane) has the largest influence weight, which may be because the average speed of vehicles in the first lane is faster and accidents are more likely to occur.
3. Truck to car ratio also has an impact on traffic accidents, but smaller than vehicle speed.
4. Whether it was a holiday had little effect on whether the accident occurred.
## 3 In-Depth Analysis of Utilizing Machine Learning in Maritime Systems
### The Impact and Benefits of Using Machine Learning in Marine Traffic Management and Prediction  
The application of Machine Learning (ML) in maritime systems has considerable potential to revolutionize marine traffic management and prediction. It can lead to more effective decision making, improved safety measures, reduced operational costs, and more sustainable practices. Below are some of the key benefits and impacts that ML brings to this field:1. Improved Decision Making: ML algorithms can process a vast amount of data and deliver valuable insights, facilitating data-driven decision making. This can lead to more accurate and efficient choices in various aspects of marine traffic management, such as route selection, risk management, and regulatory compliance [16; 40].
2. Enhanced Safety Measures: By predicting potential risks and anomalies, ML can significantly enhance maritime safety. Predictive models can alert operators about possible collisions, machinery breakdowns, or adverse weather conditions, allowing timely preventive measures to be taken [31; 41].
3. Operational Efficiency: ML can optimize various aspects of maritime operations. From automating routine tasks to predicting optimal routes based on traffic and weather conditions, ML can enhance efficiency and reduce operational costs [30].
4. Environmental Sustainability: ML can predict the environmental impact of maritime activities, enabling efforts towards more sustainable practices. It can monitor and predict emissions, fuel consumption, and waste production, assisting in reducing the environmental footprint of maritime operations [36; 37].
5. Proactive Management: By predicting future conditions and potential issues, ML allows for proactive management. Anticipating congestion, adverse weather, or equipment failure can help in taking early action, thereby minimizing potential disruptions [20; 39; 40].
## 2 Materials and Methods
### Procedures  
In order to obtain position data, the players were monitored with WIMU PRO devices (RealTrack Systems, Almeria, Spain) using the global positioning system (GPS). The GPS device used in this study can operate at 10 Hz, and it is compatible with the Galileo and American satellite constellation, which seems to provide more precision [21]. For the analysis, data were collected on outdoor football fields without any possibility of infrastructure interfering with the data collection. During the sessions, a mean of 12 satellites were connected with each device. The value of DDOP was 0.95. This equipment and its measurements are valid and reliable using the GNSS for time-motion analysis in football (distance covered variable: accuracy = 0.69-6.05%, test-retest reliability = 1.47, inter-unit reliability = 0.25; mean velocity variable: accuracy = 0.18, intra-class correlation = 0.95, inter-unit reliability = 0.03) [22], and has been awarded with the FIFA Quality Performance certificate. Each WIMU PRO device was placed in a vertical position between the players' shoulder blades, in a pocket of a specific chest vest (dimensions of the devices = 81 \(\times\) 45 \(\times\) 16 mm). The GPS devices were activated 15 min before the start of each session or match in accordance with the manufacturer's instructions. All the players were familiar with the use of GPS. Only the players that completed official matches or training sessions were included in the analysis. To avoid possible differences between devices, during the entire registration period, each player wore the same device [23; 24]. The records were downloaded using the SPRO software (RealTrack Systems, Almeria, Spain) after the end of each session. Once the data were filtered through the software, they were imported into a Microsoft Excel spreadsheet (Microsoft Corporation, Washington, DC, USA) to configure a matrix.
# A Cross-Lingual Media Profiling Model for Detecting Factuality and Political Bias
## 4 The Proposed Method
### Problem Formulation  
Following prior work [18; 19; 63], the media profiling tasks are divided into factuality and political bias rating tasks. Given a set \(A_{i}=a_{1},a_{2},...,a_{j}\) representing all reported articles of a news media outlet \(i\), the corresponding label is denoted as \(y_{i}\). Our objective is to train a media profiling predictor \(P\), such that \(P(A_{i})=g_{i}\sim y_{i}\).
## 3 NLP-Based ECMS Algorithm
### Nonlinear Programming
Because the speed (\(w_{ref}\)) is a known disturbance, the maximum/minimum torque can be known in advance and is an inequality constraint, as shown in Equation (14):  
\[\left\{ \begin{array}{l}g_{6}(x)\to f_{eng,min}\Big{(}w_{ref}\Big{)} \leq T_{eng}\leq f_{eng,max}\Big{(}w_{ref}\Big{)}\\ g_{7}(x)\to f_{mot,min}\Big{(}w_{ref}\Big{)}\leq T_{mot}\leq f_{mot,max} \Big{(}w_{ref}\Big{)}\end{array} .\right. \tag{14}\]  
The third part involves a cost function. The algorithm includes a term for fuel consumption and a term for tracking the required torque by using the sum of ICE torque (\(T_{eng}\)) and EM torque (\(T_{mot}\)). The cost function of an NLP-based ECMS is shown in Equation (15). \(Q_{fuel}\) is the weight cost of fuel consumption (\(cost_{fuel}\)), and \(Q_{trq}\) is the weight cost of the reference torque (\(cost_{trq}\)). We have  
\[cost=Q_{fuel}\cdot cost_{fuel}+Q_{trq}\cdot cost_{trq}. \tag{15}\]  
The cost of fuel consumption can be calculated by using Equation (16). Surface-fitting for a fuel-consumption rate map was performed to include fuel consumption in the cost function of NLP. To derive a polynomial with an appropriate degree, various models were analyzed as shown in Figure 6. We have  
\[cost_{fuel}=sum\Big{(}p_{20}\cdot T_{eng}^{2}+p_{10}\cdot T_{eng}+p_{11}\cdot T _{eng}\cdot w_{ref}+p_{01}\cdot w_{ref}\Big{)}. \tag{16}\]The cost for tracking the required torque using the sum of ICE torque (\(T_{eng}\)) and EM torque (\(T_{mot}\)) can be calculated by using Equation (17). The required torque is the reference torque given to the ICE and EM. The cost function was designed to use reference torque, not speed, to decrease the computational time by reducing the size of the system model. We have  
\[cost_{trq}=sum\bigg{\{}\Big{(}T_{eng}+T_{mot}-T_{ref}\Big{)}^{2}\bigg{\}}. \tag{17}\]  
The constraint and cost functions were defined in the form of an OCP for receding horizons. The defined problem was solved by an NLP solver. The IPOPT solver, which is a rapid and powerful NLP solver, was used for the NLP-based ECMS algorithm.