esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Optimization-Based Trajectory Planning for Truck-Trailer Systems
### Problem of Trajectory Planning  
By trajectory planning, we mean the process of finding control inputs that cause the states to change from initial to target states when applied to a truck-trailer system. The initial or target value of the state variables may be specified with small hysteresis to ensure feasibility.  
It is common to use an optimization problem for trajectory planning. The published procedures can be further divided into two parts. Several authors have searched for trajectories based on an already-found path. Another group of authors combines the path planning and trajectory problems; instead of finding a path, this approach tries to find trajectories that are suitable for vehicle movement.  
A major problem in trajectory planning is the requirement that the the found trajectory be both continuous and feasible. This problem can be easily solved using curvilinear coordinates [12], and similar approaches have been taken using spline curves [13; 14].
## 3 Literature Review on Network Automation
### Network Automation Tools  
For configuration automation in data network equipment, there are several platforms and protocols, based on scripts and software. These tools are open source, and examples of them are Ansible, Chef, and Puppet [48]. These tools offer an automation model and provide a library of common commands or workflows that can be easily used [45].  
In recent years, automation has been used in the configuration of networking equipment. Software-based platforms have been developed that offer automation capabilities that work only with specific brands of networking equipment and use a specialized API [42; 44; 49]. All these tools simplify the amount of work and eliminate repetitive processes in the configuration of IT infrastructure equipment [50]. Some initiatives use Python libraries to perform data network automation, but these automation tools were not created for educational environments [44; 46; 51]. Examples include the web tool called Network Configuration Manager, which automates backups, monitoring, and change management, thus improving the productivity and reliability of the enterprise data network [52]. Another available web tool is Ansible Automation, which is used to install system software, automate daily tasks, prepare internal infrastructure, and improve data network security [45]. [PERSON], et al. [51] in their research shows positive results regarding the efficiency of using automation in network equipment configuration versus using CLI, but their results were not analyzed in an educational environment with students.  
Other network automation initiatives using emerging technologies include \"Implementacion de un sistema para la administracion de las funciones basicas de un Cisco Meraki MR33 mediante Cisco Devnet y Amazon Alexa en el Data Center academico de la Universidad de las Americas\". Its objective was to implement a system for the administration and management of the functionalities of Cisco Meraki equipment through Cisco DevNet Sandbox and Amazon Alexa, using Python as a tool for the migration of the UCSadmin system [53]. In \"Network Automation and Abstraction using Python Programming Methods\", a network was simulated in the GNS3 simulator with an Ubuntu container in Docker to run the Python scripts. Netmiko and Paramiko libraries were used to perform virtual network configuration, routing protocols, and backup configuration independently of the network equipment vendor [46]. The goal of \"Diseno e implementacion de un agente de administracion, configuracion y monitoreo para dispositivos de red Cisco\" was achieved by using functions that enable serial communication between the computer running the program and the network equipment to which it is connected [54].  
The research work presented in this paper differs from all previous initiatives in analyzing the understanding of a group of students regarding the configuration of network equipment using a web-based automation application. In addition, the analysis of usability, perception, and efficiency in the use of the automation application measured in a real higher education environment is performed.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 6 Experimental Results  
The underwater experiment on SOUNET was performed on 27 May, 2015 at the Gyeongcheon Reservoir in Mungyeong City, Korea. Figures 16 and 17 show the sound speed profile and channel impulse responses we measured at the Gyeongcheon Reservoir. The depth of water measured about 10-40 m and bottom of the reservoir was mud.  
Due to limited budget, only seven nodes, including one sink, were deployed, each consisting of an underwater modem, an RF antenna, a control box, a buoy, and an anchor as shown in Figure 18a. The control box contains a DSP board implemented by ATmega2560 to control the underwater modems. In addition, the DSP board reports every packet exchange between the nodes underwater to an offshore central station through RF communication. Then the central station implemented by a notebook computer shows the events through the Windows GUI program we developed. For the underwater modem, the model ATM-885 (9-14 kHz) manufactured by Teledyne Benthos was used. The buoy was used to carry the control box on the water surface as well as to denote the node location. Anchors were used to hold the nodes at a static position.  
Figure 16: Sound speed profile.  
Figure 17: Channel impulse response: (**a**) Channel 1; (**b**) Channel 2.  
Figure 19 shows the changes of node connection in a self-organizing manner according to the events we scheduled, which the control station displays. Node 1 plays the part of sink. Initially, the connections are built up by means of _HELLO_ flooding (Figure 19a). Since node 4 is blocked by an underwater mound and thus cannot hear from nodes 1, 2, and 3, it chooses node 5 as its parent. Node 6 that is connected to node 7 at the beginning updates the connection to node 2 upon overhearing node 2 to reduce the number of hops to the sink (Figure 19b). After that, we intentionally turned off node 5 to emulate a link hole between nodes 4 and 5 (Figure 19c). Soon after that, node 4 discovers the new parent, node 7 and retains the connection. Then we turned on node 5 again to emulate a node addition. The newly added node 5 temporarily tries to connect to node 4 (Figure 19d), but shortly recognizes a better possible connection and then tries to rebuild the connection. Finally, the network architecture returns to the beginning of Figure 19a.
## 4 Experimental Results
### Correlation Analysis of Indicators
#### 4.2.3 Analysis of Urban Road Emergencies and Road Correlation  
Some previous research suggested that traffic managers should establish traffic management strategies for urban roads to reduce the consequences of emergencies. Moreover, they should reconsider certain aspects of urban roadway and street design to harmonize transportation systems, land use and redevelopment. Their research focuses more on pedestrians and streets. The research in [44] achieves accident reduction by incorporating pedestrian safety considerations into the planning, design, implementation and management of urban transportation. There is also a systematic and team-based approach to redesigning transit streets to improve pedestrian, bicycle and public transport traffic and safety [45].  
Based on the results of the correlation analysis between emergency events and urban road traffic situation prediction indicators, we could conclude that there is a certain correlation between both of them. It is reliable to analyze urban road emergencies from three aspects: road, environment and human factors, so these indicators could be used as urban road traffic condition assessment indicators under emergencies.  
These indicators have been divided into positive and negative indicators of urban road emergencies in Section 4.2.1.  
According to Equation (16), when the positive indicators of urban road emergencies values increase, the possibility of urban road emergencies increases; therefore, for the positive indicators, traffic managers should reduce the increase in these indicator values in order to reduce the occurrence of emergencies or reduce the impact. For the road length and one-way road width, we can suggest the traffic decision makers to re-plan the road parameters appropriately. For the intersections, traffic managers need to focus on the roads with multiple intersections. For rainfall, traffic managers need to focus on the road areas with heavy rainfall and provide traffic alerts for those roads.  
Similarly, according to Equation (17), it can be seen that when the negative indicators of urban road emergencies increase in value, the possibility of urban road emergencies show a decreasing trend. For the negative indicators, traffic managers will choose to increase these indicators in order to maintain the normal urban road traffic operation. For traffic lights, law enforcement officers and road sections under construction, we could suggest the traffic managers strengthen the road traffic facilities, law enforcement and construction to ensure the smooth operation of road traffic; for visibility, traffic managers should implement traffic strategies to increase the brightness of street lights on roads with poor visibility; for temperature, traffic managers should pay attention to road conditions (icy roads, snowy roads, etc.) in low-temperature road sections and provide travel reminders; for the traffic flow, traffic managers should adopt reasonable traffic diversion strategies to ensure roads are normal.
## 4 Higher-Order Equivalent Single Layer Approach
\begin{array}{lll}\text{C}&\rightarrow&u_{1}^{(\tau)}=u_{2}^{(\tau)}=u_{3} ^{(\tau)}=0\\ \text{F}&\rightarrow&N_{21}^{(\tau)}=N_{2}^{(\tau)}=T_{2}^{(\tau)}=0\end{array}  \tag{49}\]  
for \(\alpha_{1}^{0}\leq\alpha_{1}\leq\alpha_{1}^{1}\) and \(\alpha_{2}=\alpha_{2}^{0}\) or \(\alpha_{2}=\alpha_{2}^{1}\). This is the case of the West (W) and East (E) edges. In the next sections related to the numerical applications, the boundary conditions applied along each edge will be specified following the order WSEN. Finally, it should be noted that another set of compatibility conditions must be enforced if the considered structure has a common edge, as in the case of complete shells of revolution or toroidal panels. If the closing edge involves the coordinates \(\alpha_{1}=\alpha_{1}^{0}\) and \(\alpha_{1}=\alpha_{1}^{1}\), the following conditions must be added in terms of stress resultants  
\[ \begin{array}{ll}N_{1}^{(\tau)}\big{(}\alpha_{1}^{0},\alpha_{2},t\big{)}=N_ {1}^{(\tau)}\big{(}\alpha_{1}^{1},\alpha_{2},t\big{)}\\ N_{1}^{(\tau)}\big{(}\alpha_{1}^{0},\alpha_{2},t\big{)}=N_{1}^{(\tau)}\big{(} \alpha_{1}^{1},\alpha_{2},t\big{)}\\ T_{1}^{(\tau)}\big{(}\alpha_{1}^{0},\alpha_{2},t\big{)}=T_{1}^{(\tau)}\big{(} \alpha_{1}^{1},\alpha_{2},t\big{)}\end{array}  \tag{50}\]  
and displacements  
\[ \begin{array}{ll}u_{1}^{(\tau)}\big{(}\alpha_{1}^{0},\alpha_{2},t\big{)}=u_ {1}^{(\tau)}\big{(}\alpha_{1}^{1},\alpha_{2},t\big{)}\\ u_{2}^{(\tau)}\big{(}\alpha_{1}^{0},\alpha_{2},t\big{)}=u_{2}^{(\tau)}\big{(} \alpha_{1}^{1},\alpha_{2},t\big{)}\\ u_{3}^{(\tau)}\big{(}\alpha_{1}^{0},\alpha_{2},t\big{)}=u_{3}^{(\tau)}\big{(} \alpha_{1}^{1},\alpha_{2},t\big{)}\end{array}  \tag{51}\]  
On the other hand, if the closing edge is identified by \(\alpha_{2}=\alpha_{2}^{0}\) and \(\alpha_{2}=\alpha_{2}^{1}\), the compatibility conditions in terms of stress resultants become  
\[ \begin{array}{ll}N_{21}^{(\tau)}\big{(}\alpha_{1},\alpha_{2}^{0},t\big{)}= N_{21}^{(\tau)}\big{(}\alpha_{1},\alpha_{2}^{1},t\big{)}\\ N_{2}^{(\tau)}\big{(}\alpha_{1},\alpha_{2}^{0},t\big{)}=N_{2}^{(\tau)}\big{(} \alpha_{1},\alpha_{2}^{1},t\big{)}\\ T_{2}^{(\tau)}\big{(}\alpha_{1},\alpha_{2}^{0},t\big{)}=T_{2}^{(\tau)}\big{(} \alpha_{1},\alpha_{2}^{1},t\big{)}\end{array}  \tag{52}\]  
whereas the kinematic conditions assume the aspect written below  
\[ \begin{array}{ll}u_{1}^{(\tau)}\big{(}\alpha_{1},\alpha_{2}^{0},t\big{)}= u_{1}^{(\tau)}\big{(}\alpha_{1},\alpha_{2}^{1},t\big{)}\\ u_{2}^{(\tau)}\big{(}\alpha_{1},\alpha_{2}^{0},t\big{)}=u_{2}^{(\tau)}\big{(} \alpha_{1},\alpha_{2}^{1},t\big{)}\\ u_{3}^{(\tau)}\big{(}\alpha_{1},\alpha_{2}^{0},t\big{)}=u_{3}^{(\tau)}\big{(} \alpha_{1},\alpha_{2}^{1},t\big{)}\end{array}  \tag{53}\]  
Finally, it should be specified that the presented theoretical approach is employed to analyze the mechanical behavior of thick and moderately thick shells that are defined by the following limitations  
\[0.01\leq\max\biggl{(}\frac{h}{R_{\min}},\frac{h}{L_{\min}}\biggr{)}\leq 0.2 \tag{54}\]  
where \(R_{\min}\) and \(L_{\min}\) stand for the minimum value of the radii of curvature and the minimum size, respectively.
# Wireless Multi-Node uRLLc B5G/6G Networks for Critical Services in Electrical Power Systems
## 2 The OFDM/FHMA System Based on Mini-Slot Frame Architecture
### OFDM/FHMA Integrating with Mini-Slot Structure  
In the frame structure of 5G New Radio (NR), where TDD is assumed in this paper, the interval of a radio frame is equal to 10 ms, where each frame is evenly divided into 10 sub-frames. Each sub-frame is divided into several slots, where each slot consists of a fixed number of OFDM symbols (14 OFDM symbols). The exact time interval of one slot depends on the OFDM numerology (i.e., configurable subcarrier spacing (SCS)). For  
Figure 2: The diagram of reliable OFDM/FHMA system: (**a**) transmitter and (**b**) receiver.  
example, for OFDM with an SCS of 30 kHz, the length of a 5G NR slot is equal to half of a frame length. Therefore, a larger SCS occupies a shorter slot interval and supports lower-latency transmission. The frame structure of 5G NR is shown in Figure 3.  
In order to support more latency-critical applications, a new concept of the frame structure, i.e., mini-slot, was first proposed in B5G NR. Different from the traditional slot structure with 14 OFDM symbols, one mini-slot can flexibly support arbitrary \(L=\{1,2,\cdots,13\}\) OFDM symbols, which is more applicable to the wireless scenarios with short/bursty packets. In addition, the downlink-to-uplink switch point can be initiated at the end of mini-slot, rather than at the end of the subframe. Therefore, in the future B5G/6G NR, the mini-slot frame structure will still be adopted as a basic scheduling unit, enabling the low-latency (re)transmission.  
In this paper, the OFDM/FH system based on the mini-slot frame structure is first proposed from the perspective of the signal processing in the PHY and the scheduling mechanism in MAC. OFDM's subcarriers and mini-slots (i.e., time-frequency domains) of the proposed system are depicted in Figure 4. Without loss of generality, we take the \(k\)-th node as the example to explain this figure. OFDM has \(N_{f}\) sub-channels, where the \(l\)-th subcarriers at the \(i\)-th symbol interval are hopped randomly according to the assigned FH sequence \(\left\{c_{l,i}^{(k)}\mid c_{l,i}^{(k)}\in[0,1,\ldots,N_{h}-1]\right\}\). The number of OFDM symbols in each mini-slot can be flexibly configured according to the data traffic size. When the transmission reliability within the mini-slot is reduced (that is, the error rate is larger than a given threshold), so as not to meet the target of the service requirement, the ARQ and the retransmission will be carried out quickly in the next mini-slot, without waiting until the next subframe [8].  
Figure 4: The time–frequency domains of the OFDM/FH system based on the mini-slot structure.  
Figure 3: The diagram of the frame structure of 5G/B5G NR for different OFDM numerologies [8].
## 3 Results
### Validation against ECA&D Ground Measurements
In spite of the general pattern, it can be noticed that from 2004 until 2011, CLARA-A2 is slightly underestimating ECA&D-derived TCC. In addition, since approximately 1999 a considerably improved agreement between CLARA-A2 and ECA&D can be discerned, having bias values mostly less than 2% in absolute terms. That might be explained by the fact that more satellites were included in CLARA-A2, leading to a better representation of daily averages. However, apart from the aforementioned difficulties imposed by the orbital drifts and the satellite changes in the evaluation of the satellite-derived TCC  
Figure 6: Same as in Figure 5, but for the slope of the linear regressions applied to the scatter plots, having the satellite-based TCC on the y axis and the ground-based TCC on the x axis. t trends, the TCC time series from ground-based observations should also be treated with caution due to potential changes in the observation method (e.g., from human observers to ceilometers) that might induce artifacts in the trends [57]. Regarding the seasonal variability of mean TCC over the stations (Figure 8b), we found that, as expected, all three data records have their maximum values during winter and the minimum values in summer. The overall pattern of the variability of ECA&D is similar to the ones of CLARA-A2 and ISCCP-H (see also Figure 3b). However, the CLARA-A2 TCC is much closer to ECA&D than ISCCP-H. For the summer months CLARA-A2 just shows slightly lower TCC than ECA&D whereas for the rest of the months CLARA-A2 overestimates the ground measurements of TCC. On the other hand, ISCCP-H TCC is constantly higher in magnitude than ECA&D. Although from November to February the overestimation of ISCCP-H is lower than that of CLARA-A2 with respect to ECA&D, during the rest of the months ISCCP-H overestimates considerably (by up to 9%) more than CLARA-A2, which has values much closer to ECA&D (biases less than 3.5%). Thus, the seasonal cycle of ECA&D matches better with the one of CLARA-A2 than ISCCP-H, which is largely due to the fact that ISCCP-H exhibits a flattened curve in winter months. Note however, that this is within the range of interannual variability of the ECA&D winter cloudiness. This can be clearly seen in Figure 8c which depicts the month-to-month variability of the bias value computed between the mean grid cell of the two satellite data records and the equivalent average of the ground stations. An additional feature of the seasonality that is more evident in Figure 8c is that there is also an obvious opposite seasonality of the biases between CLARA-A2, ISCCP-H and ECA&D. CLARA-A2 is more biased during winter and less during the summer months, whereas ISCCP-H (which is positively biased throughout the year) has increased biases during summer and is less biased for the winter months. Figure 7: Same as in Figure 5, but for the TCC bias value (in percent).
## 3 Results and Discussion  
### Total Potential Environmental Impact (PEI)  
Figure 2 shows the results corresponding to the total PEI generated and total PEI output per kilogram of product and per hour for all cases. We can observe that the total PEIs generated for each case were negative (\(-7.35\times 10^{2}\), \(-7.12\times 10^{2}\), \(-7.10\times 10^{2}\), \(-6.87\times 10^{2}\) PEI/h), which indicates that the process is environmentally friendly. It can be observed that for all cases, the total PEI output per kilogram of product is an insignificant value while in the base case (case 1), the total PEI output value per hour is lower than the other cases. Thus, it is evident that the energy and the products contribute to a small increase in the total PEI output. On the other hand, for the case in which the product and energy are included (case 2) and (case 3), the PEI output values are equal. These results indicate that the inclusion of the product stream or energy generate the same impacts and contributes to an increase in the total PEI output.
# How Soft Polymers Cope with Cracks and Notches
## 2 Experimental Tests on Macroscopically Flawed Thin Plates
### Plates Containing Internal Crack-Like Flaws  
A second set of experiments deals with plates containing a centered crack of length \(2a\) (Figure 2a with \(b=\rho=0\)). The samples are made of a commercial silicone rubber (TSE3478T by Momentive). The desired crack is obtained by manually cutting the samples with a sharp blade. From tensile tests on sound specimens, the initial Young modulus of the material is found approximately equal to \(E=1.12\) MPa and the Poisson ratio to \(\
u=0.42\). Four series of specimens containing a centered crack are here considered, characterized by different values of the relative crack length \(a/W\), whereas the plate aspect ratio is kept constant at \(L/W\approx 2\). The geometric characteristics of the specimens are reported in Table 2. The plates are subjected to tensile loading along the \(y\)-axis up to failure, applied at a constant strain rate of \(\dot{\epsilon}_{0}=5.8\cdot 10^{-3}\) s\({}^{-1}\). The response during the experimental tests is monitored by means of the DIC technique.  
Figure 5 shows the deformed patterns of the specimens, for three different stages of the applied loading: at the beginning of the test (undeformed, left column), at an intermediate stage (remote stretch \(\lambda_{0}=1.29\), central column), and at the final stage before failure (ultimate remote stretch, right column). The strain patterns obtained from the DIC analyses are also shown, specifically, the Green-Lagrange strain \(E_{yy}\) (parallel to the loading direction) at the intermediate and the ultimate stages. It can be noticed that the initial crack-like shape tends to blunt under loading and the applied ultimate stretch generally decreases for an increasing relative crack size \(a/W\). At the intermediate stage, the DIC maps clearly show a strain concentration typical of an elliptical notch, where the maximum strain values occur in the locations corresponding to the original crack tips. At incipient failure, the strain maps exhibit a complex distribution due to the failure mechanisms developing in the vicinity of the notch roots. It is worth noticing that out-of-plane displacements occur in two limited regions close to the crack edges (the blue regions in Figure 5), because of the contraction effect arising in the direction normal to the applied load, i.e., in the \(x\)-direction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Specimen ID** & \(W\) **(mm)** & \(a\) **(mm)** & \(t\) **(mm)** & \(a/W\) & \(\Lambda u\) \\ \hline CC1 & 56 & 10 & 2.75 & 0.179 & 1.75 \\ \hline CC2 & 56 & 15 & 3.00 & 0.268 & 1.61 \\ \hline CC3 & 56 & 20 & 2.75 & 0.357 & 1.55 \\ \hline CC4 & 56 & 25 & 2.85 & 0.446 & 1.32 \\ \hline \end{tabular}
\end{table}
Table 2: Geometric characteristics of the specimens containing a centered crack. The rightmost column contains the ultimate remote stretch before failure.  
The distribution of the Green-Lagrange strain \(E_{yy}\) along the \(x\)-axis is shown in Figure 6.