esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Analytics for Profiling Low-Voltage Customers with Smart Meter Readings
## 5 Example of Application in Smart Grid Planning and Operation
Obviously, the day ahead power/generation profile differs from real ones since forecast can never obtain no forecast error. Forecast errors on load consumption can be reduced if updated time series that properly represent the consumption and generation are used. Since in the real-time management of the network the goal is keeping voltage and power flows within the technical limits, the effectiveness of the approach can be furthermore validated by considering the resulting node voltage profiles. In the following, the analysis of the node voltages is focused on the feeder F_4. This feeder is the most critical one, for its length (it is about 400 m) and for the heavy load demand. For these reasons, such a feeder may suffer for excessive voltage drops in several hours of the day. Figures 19 and 20 show the three-phase voltage profiles of the feeder F_4 at two hours of the winter working day (at 11:00 a.m. and at 6:00 p.m., respectively). The comparison is made by considering the mean value of the phase node voltages of the \(\approx\)65 real winter working days that occur in the year. The load supplied by the three phases is not balanced, and, thus, the voltage profiles of the three phases are not perfectly coincident. By these graphs, it is evident that the blue profiles (i.e., the ones derived by applying the new typical LPs proposed in this paper) are closer to the mean real day profiles (i.e., green profiles) than the ones obtained by LF calculations performed starting by the typical LPs currently adopted by the DSO (i.e., orange profiles). The overestimation of the power (see Figure 18) causes voltage drops more severe than the real case. Indeed, such behavior not only exacerbates critical conditions (e.g., as at 6:00 p.m., when severe under voltages occur even in the real case Figure 20), but also creates excessive voltage drops when they simply did not exist (i.e., at 11:00 a.m Figure 19). Finally, as an example, Figure 21 shows the daily voltage profile of the node N_35 of Figure 17 which is at the farthest (i.e., about 400 m) from the sending end (i.e., the LV busbar of the MV/LV transformer). It is evident that the new typical load profiles better approximate the real behavior than the ones used by the DSO. Figure 19: Voltage profile of feeder F_4 in one hourly interval: 11:00 a.m. of the winter working day (mean of real day values in green, distribution systems operator (DSO)’s LPs in orange, and new LPs in blue; voltage limit in red). Figure 20: Voltage profile of feeder F_4 in one hourly interval: 6:00 p.m. of the winter working day (mean of real day values in green, DSO’s LPs in orange, and new LPs in blue; voltage limit in red).
# Two-Machine Job-Shop Scheduling Problem to Minimize the Makespan with Uncertain Job Durations
## Appendix A Proofs of the Statements
### Proof of Theorem 12  
We consider any fixed scenario \(p\in T\) and a pair of job permutations \((\pi^{\prime},\pi^{\prime\prime})=((\pi,\pi_{1},\pi_{2,1}^{*}),(\pi_{2,1}^{*},\pi_{2},\pi))\in S^{\prime}\), where \(\pi_{2,1}^{*}\in S_{2,1}\) is a [PERSON]'s permutation of the jobs from the set \(\mathcal{J}_{2,1}\) with vector \(p_{2,1}\) of the job durations (components of this vector are equal to the corresponding components of vector \(p\)). We next show that this pair of job permutations \((\pi^{\prime},\pi^{\prime\prime})\) is optimal for the individual problem \(J2|p,n_{i}\leq 2|C_{max}\) with scenario \(p\), i.e., \(C_{max}(\pi^{\prime},\pi^{\prime\prime})=C_{max}\). At time \(t=0\), machine \(M_{1}\) begins to process jobs from the permutation \(\pi\) without idle times. We denote \(t_{1}=\sum_{i=1}^{k+r+1}p_{i1}\). At time moment \(t_{1}\), job \(J_{k+r+1}\) is ready for processing on machine \(M_{2}\). From the condition of Inequality (13) with \(s=1\), it follows that, even if machine \(M_{2}\) has an idle time before processing job \(J_{k+r+1}\), machine \(M_{2}\) is available for processing this job at time \(t_{1}\). If in addition, the condition of Inequality (13) holds with \(s\in\{2,3,\ldots,r\}\), then machine \(M_{2}\) may also have idle times between processing jobs from the conflict set \(\{J_{k+1},J_{k+2},\ldots,J_{k+r}\}\). However, machine \(M_{2}\) is available for processing job \(J_{k+r+1}\) from the time moment \(t_{1}=\sum_{i=1}^{k+r+1}p_{i1}\). In permutation \(\pi\), jobs \(J_{k+r+1},\ldots,J_{m_{1,2}}\) are arranged in [PERSON]'s order. Therefore, if machine \(M_{2}\) has an idle time while processing these jobs, this idle time cannot be reduced. Thus, the value of \(c_{2}(\pi^{\prime\prime})\) cannot be reduced by changing the order of jobs from the conflict set. Note that an idle time for machine \(M_{1}\) is only possible between some jobs from the set \(\mathcal{J}_{2,1}\). Since the permutation \(\pi_{2,1}^{*}\) is a [PERSON]'s permutation of the jobs from set \(\mathcal{J}_{2,1}\) with scenario \(p_{2,1}\), the value of \(c_{1}(\pi^{\prime})\) cannot be reduced. Thus, we obtain \(C_{max}(\pi^{\prime},\pi^{\prime\prime})=\max\{c_{1}(\pi^{\prime}),c_{2}(\pi^ {\prime\prime})\}=C_{max}\) and the pair of permutations \((\pi^{\prime},\pi^{\prime\prime})=((\pi,\pi_{1},\pi_{2,1}^{*}),(\pi_{2,1}^{*},\pi_{2},\pi))\in S^{\prime}\) is optimal for the problem \(J2|p,n_{i}\leq 2|C_{max}\) with scenario \(p\in T\). As the vector \(p\) is an arbitrary vector in the set \(T\), set \(S^{\prime}\) contains an optimal pair of job permutations for each vector from the set \(T\). Due to Definition 4, set \(S^{\prime}\) is a dominant set of schedules for the problem \(J2|l_{ij}\leq p_{ij}\leq u_{ij},n_{i}\leq 2|C_{max}\) with job set \(\mathcal{J}\).
# Multi-Body Model of Agricultural Tractor for Vibration Transmission Investigation
## 1 Introduction
### Anatomy of Agricultural Tractor  
Most agricultural tractors have an unsuspended rear axle; the only damping elements are tyres. Tyres have, however, a damping capacity that is one order too low to control vibration [20]. A front axle is also unsuspended (see Figure 1) or equipped with suspension; in both cases, the axle can swing around the vehicle's longitudinal axis, which is also the axis of the power input. A suspended axle also allows vertical movement of the whole axle. The axle is mounted to a subframe, which is connected to a tractor body, allowing the subframe and the axle to tilt around the axis of the connection. The axis is oriented in the normal direction to the longitudinal plane of the vehicle.  
Mostly on tractors from the high-power category, independent front axle suspension can be found (see Figure 1). Front wheels then move in the vertical direction independently of each other. The main advantage of this design is improved vehicle handling thanks to better contact between the tyre and the road's surface. Both front axle suspension designs form the primary suspension. The main components of unsuspended and independently suspended front axles are described in Figure 1.
# Challenges for Teaching Wireless Communications Standards at the Graduate Level
## 3 Methodology for Teaching Wireless Standards
### Syllabus  
The author has taught Wireless Networks course since 2006 until now for the Master's degree in Telecommunications Engineering. During such a long period, the syllabus of the course has changed many times according to the evolution of wireless communications; for example, in the case of cellular systems, the first course analyzed the Global System for Mobile communications (GSM) standard, and now, it is only explained as a historical note. Currently, the syllabus covers the cellular systems, from UMTS, LTE, and LTE-Advanced, up to the new 5G system, as well as the main standards for the consumer market of WLAN and WPAN networks. The Wireless Networks course considers mainly the physical and MAC layers of each standard and also the use of new technologies, e.g., MIMO antenna systems to improve system capacity. For example, the physical and MAC layers for low power devices are shown to be compliant with the IEEE 802.15.4 standard, and also, the activities of ZigBee Alliance related to the upper layers are described such as security services, compliance testing, and marketing towards smart energy and home automation.  
The curriculum of the Telecommunications Engineering Master's degree considers a number of basic courses, which must be completed before attending the Wireless Networks course, as in the following:  
* Digital signal processing
* Digital communications techniques
* Information theory and coding
* Fundamentals of telecommunications networks  
These preparatory courses provide the main theoretical knowledge for understanding wireless communications, which are considered as well known consequently in the Wireless Networks course. For example, the teacher explains the adoption of OFDMA in the LTE downlink, its advantage with respect to WCDMA, and the motivation for this choice, but she does not teach the OFDM modulation again.
# Application of a Geotextile in the Treatment of Post-Subsidence in Karst Areas
## 4 Results and Discussion
### Effects of Buried Depth of Geotextile
#### 4.2.1 Soil Pressure  
Figure 13 presents the soil pressure beneath the geotextile for different buried depths \(H\) at the final state of subsidence. The trends of the curves of the soil pressure for different buried depths of the geotextile were similar. At the center of the subsidence area, the soil pressure was approximately zero and approached the greatest value at the boundary between the subsidence area and the stable area. The soil pressure could be effectively transferred from the subsidence area to the boundary close to the stable area by the geotextile. Similar results were found by [PERSON] et al. (2019), where the load was transmitted from the subsidence area to less-deformed surrounding area due to the soil arching effect. Greater soil pressure was transferred to the stable area if the buried depth of the geotextile was increased, indicating that the buried depth of the geotextile is one of the key factors controlling the development of the subsidence.  
Figure 11: Surface settlement for different anchorage ratios \(L/B\) at the final state of subsidence.  
Figure 12: Decrement ratio of surface settlement under different anchorage ratios \(L/B\).
## 2 Related Work
### Content-Based and Propagation Path-Based Methods  
Recent studies have increasingly focused on combining content-based and propagation path-based methods to enhance the accuracy and robustness of misinformation detection. For instance, the GCAN model proposed by [PERSON] and [PERSON] [33] integrates textual content with retweet sequences through a co-attention mechanism, achieving a 16% accuracy improvement while providing explainable predictions. Similarly, Heterogeneous Graph Attention Networks ([PERSON] et al.) [34] construct tweet-word-user graphs to capture global semantic relationships and structural propagation patterns, enabling early-stage rumor detection. GARD ([PERSON] et al.) [35] further emphasizes semantic evolvement during information diffusion by combining graph autoencoders with propagation structures, improving both robustness and early detection performance. Additionally, the GCNs-MT model ([PERSON] et al.) [36] employs memory-augmented Transformers and Graph Convolutional Networks to jointly model textual dependencies and propagation dynamics, demonstrating strong cross-dataset generalization capabilities. These approaches highlight the complementary strengths of content and propagation features, advancing detection accuracy, explainability, and timeliness.  
However, these methods overlook the feature representation of propagation nodes while focusing on the expression of propagation relations during the process. They also fail to consider the impact of using deep semantic features from the propagated text as initial node features for false information detection. Furthermore, during feature extraction, the original text features are underutilized, and the fusion of the original text features with other features is inadequate. To address these issues, this paper proposes a multi-source information fusion approach for false information detection based on graph convolutional networks. This method constructs a propagation graph by combining propagation users and structures, embedding both the source and propagated texts' semantic information as node features. Additionally, it fully leverages the source text information for multi-feature fusion, further improving detection performance.
## 2 Artificial Cobweb Routing Algorithm  
### Topology of Low-Voltage PLC  
For a three phase power distribution grid, the signal attenuation between phases on the secondary side of a transformer is very large. Without phase coupling, each phase can be regarded as parallel and relatively independent; thus, the topology of any one phase can be the focus of research. On the basis of the physical topology of a low-voltage distribution network proposed in reference [14], this paper establishes a single-phase power distribution network, as shown in Figure 1. The base station (BS) is responsible for the collection and coordination of the data from each terminal node in the network; the BS is connected with a wide area network (WAN) for external information exchange. In practice, the signal is attenuated as a result of the transmission distance [15]; therefore, only the terminal nodes with a short physical distance from the BS are able to ensure reliable communication. Over long transmission distances, traditional polling methods cannot guarantee the direct communication between the BS and the nodes far away from the BS, so the success rate of communication may be very low [14]. To solve this problem, a novel method called the artificial cobweb routing algorithm is proposed. In this method, a communication route is first established between the BS and some of the nodes. The nodes are then regarded as relays to expand the communication distance, and gradually, all the nodes are connected to the communication system. Finally, the route between the BS and all the nodes is completed. Consequently, it is necessary to select the relay nodes and establish, maintain and reconstruct the route.
## 2 Fracture Mechanics-Based Fatigue Assessment of Homogeneous Materials
### Determination of the FCG Law  
Equation (2) illustrates that a FCG law is a precondition to carry out a fatigue assessment. At present, there are mainly two ways to obtain the FCG law of fatigue cracks. One is to establish a functional relationship between fatigue crack growth rate and some mechanical parameters, e.g., the stress intensity factor, through experimental data fitting, such as the famous Paris formula and its amendments [18],  
\[\frac{da}{dN}=C\cdot(\Delta K)^{m} \tag{3}\]  
where \(\Delta K\) represents the stress intensity factor, \(C\) and \(m\) are material-related parameters. The key to this method is to determine the mechanical parameters in the formula, for example, the stress intensity factor in the Paris law. The value of the stress intensity factor is extremely sensitive to the geometry of components and the initial crack location. For a component with a certain geometry, the stress intensity factor can be calculated analytically or numerically. However, at the structural design stage, it is always difficult to accurately establish an analytical expression of the stress intensity factor, since the geometry of the test piece or structure is unknown, and the crack position is random.  
Another method is to propose a physical model of crack propagation based on microscopic observations, and then derive the theoretical formula of a FCG law. Such as the plastic passivation model [19], limit value model [20], re-nucleation model [21] and so on. In addition, the FCG law in high-cycle fatigue is usually different from that in low cycle fatigue. In this work, only FCG laws in high-cycle fatigue are selected to carry out the fatigue assessment.  
Among numerous theoretical models of crack propagation [22], [PERSON] and [PERSON] [23] built a \"universal\" growth curve (illustrated in Figure 2) based on their propagation rate equation, containing the monotonic tensile properties. A good agreement was acquired between prediction  
Figure 1: (**a**) Fatigue loads with constant stress amplitudes; (**b**) Simplified model of the fatigue failure process of defective homogeneous materials.  
and experimentation. Such a theoretical model is undoubtedly accurate and efficient for structural designers. Since it completely reflects the effects of fatigue loading, crack length and material properties on the rate of crack growth, while requires only the static properties of the material which is basically constant at room temperature and relatively easy to obtain. Therefore, this work uses this model for the following fatigue design and assessment.  
The FCG law proposed by [PERSON] and [PERSON] can be expressed as,  
\[\frac{da}{dN}=\frac{2\sigma_{max}^{4}a^{2}}{\left(\sigma_{y}+\sigma_{u}\right) \epsilon_{u}\sigma_{u}^{2}E} \tag{4}\]  
where \(a\) represents the half of a center crack, \(\sigma_{max}\) represents the upper limit of a fatigue stress (as illustrated in Figure 1), \(\epsilon_{u}\) is the elongation after fracture, \(\sigma_{y}\) is the yield strength, \(\sigma_{u}\) is the ultimate tensile strength and \(E\) is the Yong's Modulus.
## 3 The Optimization Model
### Constraint Conditions
#### 3.3.2 A Uniformity Constraint on the Cable Force  
With regard to all of the stay cables in cable-stayed bridges, employing only Formula (7) as the constraint for the cable forces may lead to unreasonable differences in the forces between adjacent stay cables. Therefore, it is necessary to incorporate a uniformity constraint on the cable force. This is achieved by introducing a measure of the non-uniformity in the cable forces between adjacent stay cables [32] and constraining this variation to ensure a more uniform distribution. In order to analyze the distribution characteristics of the cable forces within the entire cable-stayed bridge, we introduce the \"cable force distribution parameter\". The numerical value of this parameter is directly related to the uniformity of the cable force distribution. If the cable forces of the adjacent three stay cables are denoted as \(T_{\rm i-1}\), \(T_{\rm i}\), and \(T_{\rm i+1}\), then the cable force distribution parameter can be defined as  
\[P_{\rm i}=\frac{T_{\rm i-1}+T_{\rm i+1}}{2}-T_{\rm i}\;(i=2,\cdots,n-1) \tag{8}\]  
And written in matrix form as  
\[\mathbf{P}=\mathbf{D}\mathbf{T} \tag{9}\]  
where \(\mathbf{D}\) is an \((n-2)\cdot n\)-order matrix:  
\[\mathbf{D}=\left[\begin{matrix}0.5&-1&0.5&0&\cdots&0\\ 0&0.5&-1&0.5&\cdots&0\\ \vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ 0&0&0&0.5&-1&0.5\end{matrix}\right]_{(n-2,n)}\]  
Then, the uniformity constraint on cable forces can be expressed as  
\[\mathbf{P}_{\rm L}\leq\mathbf{P}\leq\mathbf{P}_{\rm U} \tag{10}\]  
where \(\mathbf{P}_{\rm U}\) and \(\mathbf{P}_{\rm L}\) represent the specified upper and lower limit values of the cable force distribution parameter, a reasonable value of which can be determined through trial and error.
## 6 Evaluation and Performance  
The performance of the proposed scheme was evaluated in detail to illustrate its efficiency in diverse environments, from resource-constrained IoT networks to distributed applications such as blockchain. The analysis focused on computational complexity, efficiency in constrained environments, and comparisons with other cryptographic schemes.
## 2 Materials and Methods
### Characterization  
Material characterization was supported by the Center for Advanced Materials Analysis (The University of Suwon). Scanning electron microscopy (SEM; APREO, FEI, Hillsboro, OR, USA) was used to observe the size of the GO and L-GO flakes, and the thickness of the GO and L-GO films. Chemical compositions of GO and L-GOs film were determined by Fourier-transform infrared spectroscopy (FTIR; FT-IR spectrum Two, Parkin Elmer, Waltham, MA, USA), X-ray photoelectron spectroscopy (XPS; K-alpha Plus, Thermo Fisher Scientific, Waltham, MA, USA), confocal Raman microscopy (FEX, Nost, Gyeonggi, Korea), and UV-Vis-NIR spectrophotometry (Lambda 750, Perkin Elmer, Waltham, MA, USA). X-ray diffraction (XRD; ARL EQUINOX 3000, Thermo Fisher Scientific, Waltham, MA, USA) was used to detect d-spacing. Polarizing microscopy (ECLIPSE LV100 ND POL, Nikon, Tokyo, Japan) was used to observe the liquid crystal behaviors of GO and L-GO, and their shear viscosity was measured by a rheometer (MCR 300, Paar Physica, Graz, Austria). Before the measurement, pre-shear of 500/s was applied for 60 s to ensure homogeneous dispersion of suspension. Between shear rates ranging from 20 to 760/s, the viscosity of each GO suspension was measured in a stepwise manner for 40 min. Tensile strength was investigated using a microforce testing machine (MicroTester 8848, Instron, Norwood, MA, USA), and tensile tests were on rectangular samples with a 16 mm gauge length at a rate of 2.5 mm/min. The sheet resistance of films was measured with a 4-point probe system (CMT-SR1000N, Advanced instrument technology, Cumming, GA, USA). The cross-sectional area of the film was precisely evaluated from the cross-sectional SEM image using an image analysis program (Image J, NIH, Bethesda, MD, USA, 2020). The electrical conductivity of the films was obtained by dividing the sheet resistance into the cross-sectional area.
# A Design of a Lightweight In-Vehicle Edge Gateway for the Self-Diagnosis of an Autonomous Vehicle
## 2 Related Works
### On-Board Diagnostics
One of the key issues enabling future solutions is achieving an effective integration between mobile apps and vehicles. Such integration can be efficiently achieved on all existing vehicles by relying on the OBD-II interface. This allows obtaining critical information such as speed, fuel consumption, gas emissions and system failures [14]. [PERSON], [PERSON] et al. [15] proposed an on-board safe driving assist system using vehicle dynamics and real-time traffic information. It immediately detects harsh driving behavior and alerts the driver. The in-vehicle dynamics are collected from OBD-II, an accelerometer and a gyroscope. The real-time traffic information is collected from a camera with image processing. The proposed estimator exploits low-cost inertial measurement unit (IMU) and OBD-II of the vehicle to achieve navigation data without any aid of GPS. The presented estimator is based on extended Kalman filter and linear Kalman filter for vehicle attitude and 3-D velocity estimations, respectively. For accurate estimations in GPS-free situations, the extended and linear Kalman filters receive the inertial data and the vehicle speed from the IMU and the OBD-II, respectively. The proposed estimator tracks the vehicle trajectory velocity in GPS-free environments [16]. The remote diagnostic system in a vehicle includes a LoRa module, an Arduino (Uno), and an OBD-II bridge. The OBD-II bridge can read some vehicle information to detect whether any is abnormal (e.g., coolant temperature is too high) and then transmits the abnormal vehicle information from OBD-II to Arduino by UART. When abnormal vehicle information is detected, the abnormal events will immediately be sent to LoRa gateway via Arduino and LoRa module. Furthermore, LoRa gateway transmits abnormal vehicle information to cloud platform for recording the abnormal events via Ethernet. Moreover, the recorded abnormal events can be provided to vehicle maintenance plant [17].