esa-sceva/satcom-chunk-collection huggingface dataset:
# A Terminal-Oriented Distributed Traffic Flow Splitting Strategy for Multi-Service of V2X Networks
## 6 Conclusions  
This paper examined the service requirements and communication modes of 5G vehicle networking terminals. As the typical service mode of 5G vehicular networks, a V2X-based vehicle service model is proposed. On this basis, the distributed traffic flow splitting strategy adapted to multi-service of V2X networks is studied. In the service classification process, multiple nodes are selected as the interfaces for the corresponding service data streams flowing into the sub-network in the V2X scenario. Then, each node distributes the data streams to the heterogeneous sub-networks to implement the offload transmission according to the adaptive offloading algorithm. The traffic flow splitting strategy takes delay and cost as joint optimization goals, and realizes the combination of the single service offloading scheme and the prioritized multi-service diversion scheme. Throughout the research in this paper, we realized that the following aspects need to be studied: (1) based on the V2X networking scenario, the multi-service shunting algorithm combined with vehicle mobility needs further study and (2) this paper mainly studies the parallel multi-network transmission for single-vehicle terminals, and does not consider the situation of regional multi-terminals. These are the directions for further research.  
[PERSON] completed the methodology; [PERSON], [PERSON] and [PERSON] completed the design; [PERSON] and [PERSON] wrote the paper. [PERSON] revised the paper. [PERSON] and [PERSON] reviewed the paper.  
This work is partially supported by the National Natural Science Foundation of China (Nos. 61427801 and 61872423), the Ministry of Education-China Mobile Research Foundation, China (No. MCM20170205), the Scientific Research Foundation of the Higher Education Institutions of Jiangsu Province, China (No. 17 KJB510043), the Six Talent Peaks project in Jiangsu Province (No. DZXX-008), and the Research Foundation for Advanced Talents of Nanjing University of Posts and Telecommunications (No. NY217146).  
The authors declare no conflict of interest.
###### Abstract  
This paper presents a method describing the application of artificial neural networks to evaluate the change in undrained shear strength in cohesive soils due to principal stress rotation. For analysis, the results of torsional shear hollow cylinder (TSHC) tests were used. An artificial neural network with an architecture of 7-6-1 was able to predict the real value of normalized undrained shear strength, \(\tau_{fu}/\sigma^{v}_{v}\), based on soil type, over-consolidation ratio (OCR), plasticity index, \(I_{P}\), and the angle of principal stress rotation, \(\alpha\), with an average relative error of around \(\pm 3\%\), and a single maximum value of relative error around \(6\%\).  
artificial neural network analysis; cohesive soil; normalized undrained shear strength; principal stress rotation; torsional shear hollow cylinder test +
Footnote †: journal: Applied
# Development of Navigation Network Models for Indoor Path Planning Using 3D Semantic Point Clouds
## 2 Methodology
### Data Preprocessing
Additionally, to ensure the consistency of the file storage structure, all data are saved following a uniform file tree structure. For example, in the hypothetical Floor 1 scene of a building, this scene includes 2 hallways, 1 stairwell, 1 elevator lobby, 1 office, and 1 conference room. The corresponding file tree for the semantic point clouds in this scene is shown in Figure 5. Taking stairwell_1 as an example, stairwell_1.txt corresponds to the point cloud file for the stairwell, while the annotations folder contains the point cloud files for the doors and staircase associated with the stairwell. Through data preprocessing, we obtained the refined semantic point cloud files, ensuring that the input files to the algorithm follow the same directory structure. Figure 4: The illustration of the extracted doors and virtual doors from the hallway.
# A Novel IEEE 802.15.4e DSME MAC for Wireless Sensor Networks
## 5 Performance Analysis
### Reliability  
A transmission is successful if exactly one device is transmitting without any collision and channel error. A collision occurs if at least one of the remaining devices starts sensing the channel in the same slot and transmits data upon finding the channel idle. Hence, the probability of collision is  
\[P_{Collision}=1-(1-\phi)^{M-1} \tag{21}\]  
where, \(M\) is the number of devices. A transmission will be successful, if the device receives the acknowledgement packet corresponding to its transmission. The coordinator sends the acknowledgement, if exactly one device transmits data without any channel error. Therefore, the probability of successful transmission by a device is  
\[P_{S}=\frac{M\phi(1-\phi)^{M-1}}{(1-(1-\phi)^{M})}(1-P_{b}(\zeta))^{L_{Data}}. \tag{22}\]  
The packets are discarded mainly either due to channel access failure or due to retry limits. Channel access failure occurs, if the tagged device does not find the channel idle during \(maxMacBackoff\) number of backoffs. Thus, the probability of channel access failure is  
\[P_{cf}=\{p+(1-p)\alpha+(1-p)(1-\alpha)\beta\}b_{m,0}. \tag{23}\]  
Considering the \(maxFrameRetries=r\), the probability of transmission failure in \(r\) attempts is  
\[P_{tf}=(1-P_{S})^{r+1} \tag{24}\]Probability of successful packet reception within the maximum number of channel access and retransmission is called as reliability. Hence the reliability is  
\[P_{R}=1-P_{cf}-P_{tf} \tag{25}\]
## 3 Results and Discussion  
Firstly, the model was validated by using the experimental data for an adsorber at the lab scale reported in the work of [PERSON] [74], with the experimental setup as in Figure 2. In this work, CO\({}_{2}\) is captured from air (CO\({}_{2}\) concentration between 400 and 440 ppm) with the SI-AEATPMS sorbent (50 cm\({}^{3}\)) at 298 K. The air flow rate is of 2 \(\times\) 10\({}^{-5}\) m\({}^{3}\)/s, with 0-40% of relative humidity. Adsorber diameter and height are 40 mm, ensuring small pressure drops. The steel adsorbent bed is contained in a water bath for heating and cooling, during adsorption and desorption stages. Hot water at 75-95 \({}^{\circ}\)C is used for the regeneration stage, while cooling water at 20 \({}^{\circ}\)C is used for the cooling stage. All operating conditions are regulated by electronic controllers. At the equilibrium condition, adsorption and desorption times are 24 h and 2 h, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline MOF-5 & 1.95 & USD/kg & [PERSON] et al. [95] \\ MOF-177 & 1.95 & USD/kg & [PERSON] et al. [95] \\ MIL-101 & 1.95 & USD/kg & [PERSON] et al. [95] \\ SI-AEATPMS & 1.30 & USD/kg & \\ APDES-NFC-FD & 1.16 & USD/kg & \\ Cooling water & 8 \(\times\) 10\({}^{-2}\) & USD/m\({}^{3}\) & [PERSON] et al. [95] \\ Electricity from MWI & 1 \(\times\) 10\({}^{-1}\) & USD/kWh & [PERSON] et al. [96] \\ Waste heat from MWI & 2.4 \(\times\) 10\({}^{-2}\) & USD/kWh & SDH [97] \\ \hline \hline \end{tabular}
\end{table}
Table 9: Variable operating costs.  
This means that the entire bed is in equilibrium with the feed in about 24 h, as obtained by our mathematical model and shown in the breakthrough curve of Figure S1 in the Supporting Information. This demonstrates good agreement between experimental and simulated results, validating our mathematical model.
# A Novel Magnetic Equivalent Circuit Model for the Corner of Inductor Core
## 4 Results and Discussion
### Inductance Comparison of MEC and Prototype  
The physical prototype inductor was made and the incremental inductance with the current (\(L_{inc-i}\)) curve was measured using the DPG10 Power Choke Tester, which has an accuracy of 1.9%, as shown in Figure 11. The \(L_{inc-i}\) curve corresponding to the MEC with the local corner model and IEC corner is solved by Equation (11) and the \(L_{inc-i}\) curves are shown in Figure 12a.  
The initial inductance of the IEC corner model was greater than the measured value and the descent rate was high. After 150 A, the inductance was already less than the measured value. In the local corner model, the initial inductance was slightly less than the measured value and fit with the prototype measured data very well with the current 20 A to 1000 A. Both of the MEC \(L_{inc-i}\) curves were compared with the measured data to calculate the relative error of each point, as shown in Figure 12b.  
Figure 11: The physical prototype and tester: (**a**) physical prototype; (**b**) DPG10 Power Choke Tester.  
Figure 12: The incremental inductance and relative error with the current: (**a**) the incremental inductance; (**b**) relative error of MEC with IEC corner and local corner model.  
The maximum relative error \(e_{max}\) and average error \(e_{avg}\) were calculated by Equations (11) and (12), and presented in Table 2.  
\[e_{avg}=\frac{100}{(1000-20)}\times\sum\
olimits_{i=20}^{1000}\frac{\left|L_{test,i}-L_{MEC,i}\right|}{L_{test,i}}, \tag{11}\]  
\[e_{max}=100\times\max_{i=20\sim 1000}\frac{\left|L_{test,i}-L_{MEC,i}\right|}{L _{test,i}}, \tag{12}\]  
where \(L_{test,i}\) is the measured inductance and \(L_{MEC,i}\) is the calculated inductance of the MEC method at each current \(i\) from 20 A to 1000 A.  
Compared to the IEC corner model, the \(e_{max}\) of the local corner model decreased by 8.7%. The \(e_{avg}\) of the local corner decreased by 5.0%. This shows the local corner model has higher accuracy than the IEC corner model. Compared to the measured data, the maximum relative error of the local corner MEC was \(<6\%\), and the average relative error was \(<4\%\). The local corner MEC model provided a \(L_{inc-i}\) curve calculation with high accuracy and a high fit degree.
## 3 Results and Discussion
### Functional Performance  
In terms of functional properties, the Spanish technical standard establishes different limits based on the type of mixture (see Table 2) [20].  
AC mixtures and open graded friction courses presented a log-normal distribution, while gap-graded mixtures were well fitted by a normal distribution. Following the explained criteria and the minimum requirements established by the technical standard, the boundaries of the labels were clearly defined for each family of mixtures (see Figure 19).  
Sideway force coefficients were measured with the SCRIM device, one of the most commonly used methods in Europe. In this case, AC and open graded friction courses mixtures approached a log-normal distribution, while gap-graded mixtures followed a normal distribution pattern (see Figure 20).  
The label scale for skid resistance was based on the skid resistance value obtained after 3-6 months of traffic, after the initial fluctuations and at the beginning of long-term skid resistance due to polishing.  
In case the type of the mixture is unknown, a global label that covers all the families was defined both for macrotexture and skid resistance (see Figure 21).  
Concerning the \"splash and spray\" effect, all the values were obtained during the monitoring stage. The test trailer measured the number and distribution of drops that crossed a reference plane at a given time. Even though it is well known that this effect is directly related to the loss of visibility, it is not considered in the European technical standards. Hence, the upper limit was established based on the monitoring experience [24]. As the obtained results did not follow a normal or log-normal distribution, the boundary criterion was directly applied to the sorted values (see Figure 22).  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Mean Depth (mm)** & **Sideway Force Coefficient (\%)** \\ \hline AC mixtures & 0.7 & 65 \\ Gap-graded mixtures & 1.1 & 65 \\ Open graded friction courses & 1.5 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Minimum requirements in terms of texture and skid resistance according to the Spanish technical standard [20]. AC—asphal–concrete.  
Figure 19: Macrotexture labels based on the type of mixture.  
Figure 20: Skid resistance labels based on the type of mixture.
## 2 Materials and Methods
### Statistical Analysis  
All data were analyzed using SPSS v.23.0 for Windows (IBM Corp., Armonk, NY, USA). Prior to analysis, normality was assessed using the Kolmogorov-Smirnov test, and homogeneity was assessed using [PERSON]'s test. Descriptive statistics were calculated using analysis of variance for continuous variables and the chi-square test for categorical variables. Continuous variables are presented as the mean \(\pm\) standard deviation (SD), while categorical variables are presented as a number (\(n\)) or percentage (\(\%\)). To test the research hypothesis, study participants were categorized into four subgroups based on their AMMI and SPPB scores, as mentioned earlier (i.e., normal, low MM, poor PP, and sarcopenia). One-way analysis of variance (ANOVA) was used to compare the means of gait parameters among the four groups. Following the one-way ANOVA, post hoc analysis was conducted using the Bonferroni method for group comparisons. Stepwise multivariate linear regression analysis was used to explore the association between gait parameters and the prevalence of low muscle mass, poor physical function, and sarcopenia. Covariates included age, sex, education, BMI, WC, and comorbidities. Receiver operating characteristic (ROC) curves were constructed to determine the best association between gait parameters and low muscle mass, poor physical performance, and sarcopenia. The significance level was set at \(p<0.05\).
## 2 Method  
In order to verify the geometric properties of the SAR products, the signal propagation path has to be completely retrieved: from transmitting the SAR pulses up to receiving the backscatter power from the point target. The main parameters to be considered are depicted in Figure 1: the geometry between spacecraft and point target is determined by the orbit state vectors and the point target position on Earth. The information about transmitted and received pulses is saved by the SAR instrument as a timeline in the SAR raw data headers. Along the propagation path, the pulses are affected by several effects (mainly due to the troposphere and the ionosphere), which cause additional delays and need to be considered.  
The proposed method evaluates the SAR pulse propagation time by using the speed of light and the interpolated positions of the SAR antenna, which are related to the position of the spacecraft. The exact position of the point target's phase center is precisely surveyed by differential GPS.
# Intelligent Traffic Engineering for 6G Heterogeneous Transport Networks
## 4 Proposed ML Techniques, MILP Framework, and Methodology
### Clustering Model  
The clustering process transformed the packets from \(T(t)\) into the aggregated volume of packets within clusters, denoted as \(D_{j}(t)\), where \(j\) is the cluster label set to equal the number of transport network technologies equal to three, represented as \(j\)\(\epsilon\) {\(\mathcal{C}\)#1, \(\mathcal{C}\)#2, \(\mathcal{C}\)#3}. Thus, \(T_{j}(t)=D_{\mathcal{C}\#1}(t)+D_{\mathcal{C}\#2}(t)+D_{\mathcal{C}\#3}(t)\). Meanwhile, the series of instances, t, exhibited different \(D_{j}(t)\) values over time based on the dataset and clustering output. Subsequently, a variation of traffic types in a cluster, \(u_{mn}(t,j)\), determines the value \(D_{j}(t)\). Each \(D_{j}(t)\) was analyzed to produce the total downlink (DL) or uplink (UL) denoted by \(V_{dl/ul}\), minimum error rate, \(\epsilon_{\text{min}}\), and packet delay budget, \(\beta_{max}\), values in forming \(D_{j}(t)\):(\(V_{dl/ul}\), \(\epsilon_{min}\), \(\beta_{max}\)). Each instance in the format in the clusters, \(D_{j}(t)\), incorporated the total sums of packets handled by the transport network. Given that the number of transport network technologies is \(K\), \(\lambda_{k}\) is the respective denotation of \(K\) transport network technology. Moreover, a similar format, \(\lambda_{k}\):(\(V_{T\text{-}dl/ul}\), \(\epsilon_{T\text{-}min}\), \(\beta_{T\text{-}max}\)), represents the transport network technology, where \(V_{T\text{-}dl/ul}\) is the capacity of the transport network, \(\epsilon_{T\text{-}min}\) is the error rate, and \(\beta_{T\text{-}max}\) is the packet trip time.
## 2 The Selection of the Fault Feature
### Analysis of the Output Characteristics of Photovoltaic Array in Fault State  
When the photovoltaic array fails, different fault types have different influences on the output of photovoltaic array. Figure 4 shows the I-V curves of the 3 \(\times\) 2 photovoltaic array in normal and different fault conditions, of which the photovoltaic modules are in same test conditions (1000 W/m\({}^{2}\), 25 \({}^{\circ}\)C); the model of photovoltaic module is CHN310-72P.  
Figure 4: The V-I curves of the 3 \(\times\) 2 photovoltaic array in different states.  
Figure 4 shows that when the fault of short-circuits occurs in the photovoltaic array, the short-circuit current \(I_{\text{SC}}\) and maximum power current \(I_{\text{m}}\) are basically unchanged, while the open-circuit voltage \(U_{\text{OC}}\) and the maximum power voltage \(U_{\text{m}}\) are significantly decreased. When the fault of short-circuits occurs in the photovoltaic array, the current of faulty photovoltaic module is zero; then, the current and voltage of its string decreases, resulting in a decrease in the output voltage of the photovoltaic array. From the I-V characteristic curve of a photovoltaic cell, we can know that when the output voltage of normal string reduces, its output current will increase, resulting in little change in the total output current of photovoltaic array.  
When the fault of open-circuits occurs in the photovoltaic array, the open-circuit voltage \(U_{\text{OC}}\) and maximum power voltage \(U_{\text{m}}\) are basically unchanged, while the short-circuit current \(I_{\text{SC}}\) and the maximum power current \(I_{\text{m}}\) significantly decrease. When the fault of open-circuits occurs in the photovoltaic array, the current of its string is zero, which causes the output current of the photovoltaic array to decrease dramatically, and the output voltage of normal strings and the photovoltaic array are basically unchanged.  
When the photovoltaic modules in the photovoltaic array are abnormally aged, the open-circuit voltage \(U_{\text{OC}}\) and short-circuit current \(I_{\text{SC}}\) are basically unchanged, while the maximum power voltage \(U_{\text{m}}\) and the maximum power current \(I_{\text{m}}\) significantly decrease. The literature [20] points out that the open-circuit voltage \(U_{\text{OC}}\) and the short-circuit current \(I_{\text{SC}}\) contain the information of temperature and light intensity. The above analysis and the literature [20] show that the open-circuit voltage \(U_{\text{OC}}\), the short-circuit current \(I_{\text{SC}}\), the maximum power voltage \(U_{\text{m}}\), and the maximum power current \(I_{\text{m}}\) can be regarded as the external characteristic parameters of the photovoltaic array fault diagnosis model.
# Best Practices Evidenced for Software Development Based on DevOps and Scrum: A Literature Review
## 3 Results
### Quantitative Evidence of Scrum and DevOps Benefits
#### 3.7.1 Productivity Gains and Delivery Speed  
The integration of DevOps and Agile methodologies has consistently shown improvements in software development efficiency according to the authors of [45], who conducted a case study on an IT team transitioning to DevOps by merging development and operations teams. The work analyzed six DevOps capabilities by measuring team productivity before and after the transition. The results found a 20% increase in productivity and a 30% reduction in deployment time.  
Likewise, the authors of [48] examined DevOps adoption in various real-world companies using Grounded Theory and a case study at a government institution. Their research emphasized that automation alone is insufficient for DevOps success and that collaboration plays a crucial role. The same result was found using Scrum. Therefore, actively involving stakeholders, creating transparent communication channels, and creating a collaborative culture will help companies achieve continuous improvement. While exact quantitative measures were not provided, the case study reported a 25% faster release cycle and a 40% improvement in incident resolution time.  
Additionally, the authors of [20] demonstrated substantial benefits in real-world settings by examining Portuguese IT companies migrating from waterfall to Agile, reporting a 25% reduction in time to market and an 18% increase in project success rates, indicating that constant quality measurement or concurrent testing and early and continuous feedback proposed from Scrum is necessary; moreover, versioning and automation of pipeline opera tions from DevOps are also required. Adding to their findings, the authors of [29] studied Agile adoption in China's global software development industry, demonstrating a 20% improvement in project delivery speed and a 30% increase in customer satisfaction scores.  
These findings align with industry-wide observations, where continuous feedback loops, automated deployment, and improved cross-team collaboration have been highlighted as key drivers of efficiency gains [40].
## 1 Introduction
### Trends  
Regarding the recent trends in 6G communication, the study of [PERSON] et al. [6] has provided significant insights. Intelligent connectivity is the leading trend in 6G communication. This is because the earlier systems of communication, such as 4G or 5G, do not possess the technical sophistication needed for the rapid development of intelligent connectivity. It has been a challenge for 5G networks to provide a single network platform that can enable enhanced connectivity. This is clearly unsupported by the current state of communication networks and requires high-speed networks such as 6G that rely on artificial intelligence for network services and edge computing. Another emerging trend discussed in the realm of 6G is the use of IoT systems. [PERSON] et al. [7] stated that the major motivation behind the use of 6G in IoT systems is to effectively connect each and every device to the network. Based on the network speed of 6G connectivity, technologies based on IoT can enable the integration of many technologies and communication between devices that could not previously have been connected. It can also provide real-time data processing and communication and provide users with an excellent user experience in different IoT applications, such as smart homes and smart cities. However, this trend is largely undeveloped due to the limited research that has been conducted and high cost of implementation [8]. In future, given the rate of technological advances and the current interest shown by countries such as China and the US in the development of 6G, it is only a matter of time before 6G is widely applicable in IoT systems in smart cities. The aspect of security in the development of 6G communication also needs to be highlighted. As per [PERSON] et al. [7], security is one of the key features leading to the advancement of a new communication paradigm, also known as 6G communication. In addition to the major aspects of the 6G network, such as real-time intelligent edge computing, distributed artificial intelligence, intelligent radio, and 3D intercoms, the issues of security and privacy must also be brought into the limelight. Through the advanced security system that is possible in 6G, the smart cities' IoT-based systems will be more secure, to keep up with the expectations of privacy and confidentiality. The study has illustrated the evolution of security under different networks; see Figure 1.  
Figure 1: Phases in the development of networking technology source [9].