esa-sceva/satcom-chunk-collection huggingface dataset:
# Data-Driven Network Latency Processing for Auxiliary Services in Virtual Power Plant
## 2 Related Work
### The Practice of VPP  
Looking around the world, various countries and regions have actively explored the research and practice of VPPs. Denmark is building a DER interactive VPP system based on biogas and biomass power generation and wind power in Bornholm Island [5]. AutoGrid is committed to the development of a VPP platform supporting energy storage systems [6]. The German VPP demonstration project RegModHarz, in which unified standards are used for photovoltaic, wind power, biogas, biomass, and energy storage, has a variety of DER interconnections, through interaction with the regional household users of the energy management system to regulate electricity time and electricity consumption according to price [7]. At the same time, with the improvement of Chinese policies, various provinces also responded to carry out VPP practice demonstrations [8; 9; 10]. State Grid Zhejiang Electric Power Company has brought together residential loads, energy storage facilities, and commercial buildings to implement a demand response, which includes 4 million customers' participation [11]. State Grid Shanghai Electric Power Company has built a VPP dispatching operation management system, which mainly regulates the air-conditioning system in large commercial buildings [12].  
In addition to renting the wireless public network transmission of telecom operators, the Chinese VPP network also adopts a variety of hybrid networking methods, such as power fiber private network and power wireless private network, while western VPP projects mainly rely on the link of telecom operators [13]. However, due to the actual situation of VPP implementation, there are many challenges, such as the VPP communication network is difficult to integrate and connect with heterogeneous networks, service requirements are vaguely defined and poorly matched with network performance indicators, and the lack of flexibility of a multimode business computing network integrated a cloud edge collaboration framework.
## 1 Introduction
To achieve high data rate communication using guided elastic waves, a much wider frequency band must be used. We test the proposed TR-PPM modulation scheme under the data rates of \(10\) kbps, \(20\) kbps, \(50\) kbps and up to \(100\) kbps. The obtained average bit error rates are \(0\), \(5.75\times 10^{-4}\), \(1.09\times 10^{-2}\) and \(5.01\times 10^{-2}\), respectively, out of a total of \(40,000\) transmitted binary bits (_i.e._, \({}^{\prime}0\)'s and \({}^{\prime}1\)'s) when transmitting at the center frequency of \(250\) kHz and a \(500\) kHz bandwidth on steel pipe specimens. We note that no complex processing schemes, such as adaptive channel equalization and error correction coding, are employed at the receiver for the purpose of emphasizing the advantage of time reversal. The remainder of this paper is organized as follows. Section 2 describes the background theory. Section 3 presents the communication results by experiments. The conclusion is presented in Section 4.
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 4 Discussion and Insights
### Importance of Spatiotemporal Information  
One of the most significant observations from our experiments is the notable performance improvement achieved by incorporating spatiotemporal information into the segmentation model. As evidenced by the results on both the TZMD_NUC and SemanticKITTI datasets, the proposed method outperforms traditional methods that do not leverage spatiotemporal context, such as PointNet++ and Cylinder3D. This highlights the critical role of temporal cues in enhancing feature representation, especially in dynamic outdoor environments where object and terrain characteristics evolve over time. The spatiotemporal clue-gated encoder is particularly effective in extracting relevant information from historical frames, helping the model better understand long-term dependencies and contextual changes, which ultimately leads to more accurate segmentation.
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
## 2 Model Establishment and Simulation Test Design
### Establishment of Finite Element Model
#### 2.1.3 Load Handling  
For a fully load-bearing hydrogen fuel cell city bus, there are four typical working conditions during its daily operation. These conditions include horizontal bending, ultimate torsion, emergency braking, and emergency cornering [32].  
The overall structure of the hydrogen fuel cell city bus studied in this paper is relatively complex. To better simulate the real-life load distribution of the bus, other additional loads attached to the bus are applied to the corresponding assemblies and positions through concentrated loads. The load on the hydrogen fuel cell city bus body frame mainly includes the hydrogen fuel cell system, hydrogen storage system, power battery, seats, air conditioning, passengers, etc. The bus is calculated to have 20 seats and 39 passengers, where each seat is 11 kg and each passenger averages 65 kg. The weight distribution of the main load is shown in Table 2.  
After the weight distribution is completed, the final simplified finite element model of the bus body frame is shown in Figure 2.
## 2 Materials and Methods
### Normalized Difference Vegetation Index Data  
The METER Spectral Reflectance Sensor was used to measure Normalized Difference Vegetation Index (NDVI) (METER Groups Inc., Pullman, WA, USA). The sensor has two versions: (1) cosine-corrected sensor (SRS-Ni Hemispherical Sensor) for incident radiation and (2) a field stop lens sensor (SRS-Nr Field Stop Sensor) for reflected radiation from the canopy. METER ZL6 data logger (METER Groups Inc., Pullman, WA, USA) was used to record the data on a 30 min basis. The SRS-NDVI was mounted 1.6 m above the ground surface.
## 2 Materials and Methods
### Gyroscope and Accelerometer Recordings  
Recent advancements in technology have facilitated the installment of gyroscopes and accelerometers in wristwatches (ASUS ZenWatch 3, WI503Q, ASUSteK Computer Inc., Taipei, Taiwan) for recording purposes. A smart watch (Figure 1) that measures six axes of movement (three for the gyroscope and three for the accelerator) was used. We used our own application program (app) written in Java for the recording. The app recorded five data points per second in the three axes of the gyroscope and accelerometer by using a low-pass filter of 2.5 Hz. The gyroscope was used to measure angular velocity--Gx, Gy, and Gz (Figure 1)--in degrees per second, and the detected value was displayed as 16-bit digits. The accelerometer measured acceleration in the axial direction--Ax, Ay, and Az (Figure 1)--in units of g, and the detected value was displayed as a 16-bit digit. Due to limitations of the skeletal structure of the human hand, different hand actions presented different movements and rotations in different spatial directions. Therefore, the gyroscope and accelerometer values along the \(x\)-, \(y\)-, and \(z\)-axes contain different information regarding rotation movements. Therefore, to compare and determine the most discriminative direction, the gyroscope and accelerometer values were quantified separately along the \(x\)-, \(y\)-, and \(z\)-axes.  
Each data point included Gx, Gy, Gz, Ax, Ay, Az, and the time values. During preprocessing, to reduce some activities that may have affected our analytic results, such as going to the restroom, we  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ADHD** & **Control** & \(p\)**Value** \\ \hline Age & 6 years 11 months \(\pm\) 1 year 0 months & 6 years 9 months \(\pm\) 1 years 1 months & 0.2765 \\ Sex (M/F) & 11/4 & 11/4 & NA \\ SNAP (teacher) & 43.38 \(\pm\) 14.03 & 6.67 \(\pm\) 5.38 & \textless{}0.0001 \\ SNAP (parents) & 44.73 \(\pm\) 14.68 & 14.87 \(\pm\) 10.29 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic data of the patients with attention-deficit hyperactivity disorder (ADHD) and controls.  
Figure 1: Six-axis recordings of the gyroscope and accelerometer.  
manually removed some data points according to teachers' recordings. The children with ADHD and the controls wore the watches on their non-dominant wrist simultaneously during class. The recording period was between 1:00 p.m. and 5:00 p.m., and the time point was the same for 3 consecutive days. One child with ADHD and one control underwent recordings for 2 h per day. Normally, these children took language or mathematics classes during recording. The classroom had 20-30 children and 2-3 teachers. The teacher in the class recorded participants' activities during the study period.
# Improving Response Time for Crowd Management in Hajj
###### Abstract  
Flying Adhoc Network (FANET) is a particular type of Mobile Adhoc Network (MANET) that consists of flying drones or unmanned aerial vehicles (UAVs). MANETs are especially useful in rural and remote areas, where the lack of public networks necessitates data delivery through mobile nodes. Additionally, FANETs provide better coverage where there is a lack of roads. Generally, the goal of FANETs is to provide multimedia data to applications such as search and rescue operations, forest fire detection, surveillance and patrol, environmental monitoring, and traffic and urban monitoring. The above applications' performance and efficiency depend on the quality and timely delivery of these essential data from an area of interest to control centers. This paper presents a Priority-based Routing Framework for Flying Adhoc Networks (PRoFFAN) for the expedited delivery of essential multimedia data to control centers. PRoFFAN reduces the FANET application's response time by prioritizing the sending and forwarding of critical image data from the UAV to the control center. Our motivation application is crowd management; we believe that having important image features as early as possible will save lives and enhance the crowd's safety and flow. We integrated PRoFFAN over the RPL routing layer of Contiki-NG's IPv6 network stack. We used simulations in Cooja to demonstrate the benefit of PRoFFAN over conventional ZigBee.  
FANET; Priority Routing; image transmission; simulations; drones Article Improving Response Time for Crowd Management in Hajj  
1 Computer Engineering Department, Umm Al-Qura University, Makkah 24231, Saudi Arabia 1  
2 Science and Technology Unit, Umm Al-Qura University, Makkah 24231, Saudi Arabia; [EMAIL_ADDRESS]  
*Correspondence: [EMAIL_ADDRESS] (E.F.); [EMAIL_ADDRESS] (A.N.)
### Literature Review  
In recent years, various machine learning algorithms have been proposed and evaluated for the detection of voice disorders. It is worth noting that some previous works [9; 10; 11] tested their algorithms using the Massachusetts Eye and Ear Infirmary voice and speech lab (MEEI) database, which are not discussed in this paper because the MEEI database is commercialized and not publicly available. Further, it is inconsistent and features varying recording conditions for pathological and healthy subjects.  
Instead, this paper focuses on two publicly available databases: the Saarbruechen Voice Database (SVD) [12; 13; 14; 15; 16] and Voice ICar fEDerico II (VOICED) [17; 18; 16]. The following is a summary of existing approaches applied to the SVD. A hybrid support vector machine (SVM) and Gaussian mixture models (GMM) were proposed and evaluated in [12]. This method achieved an accuracy, sensitivity, and specificity of 0.965, 0.94, and 0.99, respectively. Six methods, including a sequential minimum optimization (SMO)-based SVM, decision tree, Bayesian classification, logistic model tree, k-nearest neighbor, and entropy-based method, were evaluated in [13]. The SMO based SVM yielded the best performance in accuracy (0.858), sensitivity (0.876), and specificity (0.839). [PERSON] et al. [14] proposed two approaches, long short-term memory (LSTM) and convolutional neural network (CNN), for differentiation between healthy and dysphonic candidates, healthy and laryngitic candidates, and healthy and paralyzed candidates. The achieved precision values were 0.66, 0.67, and 0.78, respectively. SVM was applied with zero frequency filtering and quasi-closed phase glottal inverse filtering methods for the detection of voice disorders in [15]. Based on the performance evaluation, the applied method achieved an accuracy, sensitivity, and specificity of 0.76, 0.72, and 0.78, respectively. A threshold-based detection method with a newly defined dysphonia detection index was proposed in [16]. The accuracy, sensitivity, and specificity were 0.798, 0.706, and 0.902, respectively.  
The work in [16] also applied VOICED, showing degraded performance in the accuracy, sensitivity, and specificity (of 0.5, 0.458, and 0.643, respectively). Researchers adopted K nearest neighbor (KNN) as a model for the detection of voice disorders [17]. This method achieved an accuracy of 0.933 and outperformed the other algorithms (random forest (0.874) and extra trees (0.863)). In [18], boosted tree, SVM, decision tree, naive based classifier, and KNN were evaluated; the pairs of sensitivity and specificity were (0.829, 0.862), (0.79, 0.279), (0.779,0.844), (0.857,0.644), and (0.774,0.334), respectively. As the VOICED database was published in 2018 (compared with SVR, which was published in 1997), the research publications that consider the VOICED dataset are fewer than those using SVR.
## 2 Materials and Methods
### Experimental Design and Treatments  
The experiments were conducted in winter at the CREA-CI of Caserta (lat. 41\({}^{\circ}\)04\({}^{\prime}\)26.1\({}^{\prime\prime}\) N; long. 14\({}^{\circ}\)19\({}^{\prime}\)05.5\({}^{\prime\prime}\) W). Two lettuce cultivars, Imperiale and Verde d'Inverno, belonging to the butterhead and romaine types, respectively, from Topseed s.r.l., Sarno (SA), Italy, were grown in a floating system. The experiment was carried out in an unheated, polycarbonate-covered greenhouse under natural daylight conditions.  
Seeds were sown on 12 January 2021 in 336-cell polystyrene trays filled with the 6 compost-growing media (C1-60, C1-30, C2-60, C2-30, C3-60, C3-30) prepared by mixing the three composts (C1, C2, and C3) with commercial peat (carbon 51% d.m. and nitrogen 1.2% d.m.) Plantobul substrate, Plantafilor Humans Verkaufs-GmbH from Vechta, Germany) at two rates (60% and 30% on a volume basis), after sieving to 1.5 cm. A commercial peat-based substrate (Neuhaus Humin-substrate n\({}^{\circ}\)17, Klasmann-Deilmann, Geeste, Germany) was used as a control (TC).  
The experiment was arranged according to a randomized block experimental design with two cultivars and seven growing media (Table 2) with three replications and a plant density of 1926 plants per square meter.  
Three weeks after sowing, the water in the tanks was substituted with 0.5 g L\({}^{-1}\) Hydrofert (Biochim Spa, Medicina, Italy) nutrient solution containing 100, 43.6, and 83.0 mg L\({}^{-1}\) of N, P, and K, respectively.  
During the crop cycle, air temperatures and relative humidity were registered. All the measurements were collected on a data logger Testo Mod. 175-H1 (Testo SE & Co. KGaA, Titisee-Neustadt, Germany).  
In Figure 1, daily maximum, mean, and minimum values of air temperature (a) and relative humidity (b) recorded inside the greenhouse during the growing period of lettuce were reported. The minimum temperature did not fall below 0 \({}^{\circ}\)C, while the maximum reached 30 \({}^{\circ}\)C degrees in the middle and at the end of the growing cycle.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Growing Medium** & **Coding** \\ \hline Peat-based substrate & TC \\
60\% Composd 1 + 40\% Peat & C1-60 \\
30\% Composd 1 + 70\% Peat & C1-30 \\
60\% Composd 2 + 40\% Peat & C2-60 \\
30\% Composd 2 + 70\% Peat & C2-30 \\
60\% Composd 3 + 70\% Peat & C3-60 \\
30\% Composd 3 + 40\% Peat & C3-30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of the Growing Media utilized.
## 4 Results
### Voltage Values
#### 4.1.1 Base Case  
Voltage profiles for each node are presented in Figure 5, where L1, L2, and L3 represent phases of three-phase DN. This nomenclature will be used further in the paper. Figure 5 is partitioned into three distinct subfigures, each illustrating the voltage profiles for a particular phase with different levels of PV integration. PVs are connected to the node where the load has been previously integrated. As can be seen from the figure, phase 1 contains the highest number of loads, while phase 3 is the least loaded. This results in a high voltage rise in phase 3, as well as exceeding the upper node voltage permissible value. Figure 5 shows an indicative representation, the node whose voltages will be displayed is red marked shown in details in the next figure.  
According to voltage profiles from Figure 5, the highest difference between PV production and loads is in phase 3 at node 336 with 1.13 p.u. Therefore, the voltages at this node will be presented and compared with the voltages at the same node obtained through the optimization procedure. Figure 6 represents the voltage profile of node 336 over 24 h. It can be observed that in phase 3 the voltage exceeds the permissible limit at 10 a.m. and it continues to about 4 p.m. In this period, the highest voltage value occurs. In phase 2, the voltage limit is exceeded from 1 and 2 p.m.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case Studies** & **Without Control** & **OLTC** & **PV RPC** \\ \hline Base case & ✓ & \(\times\) & \(\times\) \\ Case study 1 & \(\times\) & ✓ & \(\times\) \\ Case study 2 & \(\times\) & \(\times\) & ✓ \\ Case study 3 & \(\times\) & ✓ & ✓ \\ \hline ✓ include, \(\times\) exclude. & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Case studies description.
# Incremental Formula-Based Fix Localization
## 3 Proposed Method
### Overview  
AgxFaults takes _a buggy program_ and _a failing test case_ that demonstrate a program failure as an input. It outputs a set of pairs, each pair consisting of _a minimal set of suspicious statements_ with an _angelic execution_ that explain how the failure can be removed by replacing these suspicious statements with angelic values. The fault localization process of AgxFaults is iterative and incremental on-demand. Figure 4 provides a high-level view of AgxFaults and its main components. Below, we first briefly describe the main components and then explain the overall fault localization process of AgxFaults.  
* **Angelic DCFG (Dynamic Control Flow Graph)** : The Angelic DCFG is the dynamic control flow graph of an angelic program [24]. This angelic program acts as an abstraction of the input buggy program such that only program parts relevant to the error are represented precisely, while irrelevant parts are represented abstractedly as angelic non-determinisms (i.e., executions that can produce non-deterministic values such that the program execution succeeds).
* **Error Trace Formula**: The error trace formula is essentially formula \(1N\wedge TF_{agx}\wedge AS\), where \(1N\) represents the test input, \(AS\) represents the assertions of the given failing test case, and \(TF_{agx}\) is semantically equal to the current version of the Angelic Program. The error trace formula encodes the fault localization problem of the current angelic program with the given failing test case. Each MCS of this angelic formula corresponds to an angelic execution in the angelic program.
* **Angelic Execution** The angelic execution is a correct execution of the angelic program. This execution is obtained by diverting the original error trace such that the output of specific statements is dynamically replaced with proper values, i.e., angelic values, to make the test execution success.
* **On-demand Program Explorer and Encoder** component is responsible for refining the angelic program and the error trace formula in an on-demand manner.  
Figure 3: Error trace formula.  
Figure 2: Static single assignment (SSA) representation of method foo.  
* **Incremental formula solver** is responsible for computing the minimal correction subset (MCS) of the angelic formula incrementally.
* **MCS analyzer** analyzes the obtained MCS of the angelic formula to determine possible faults in the program. In addition, it determines which abstract parts of the angelic program need more refinement to provide a more precise result.  
The core idea of the AgxFaults is to work with an _angelic program_ incrementally instead of a program formula encoding all semantics of the original buggy program, which may lead to very complex and expensive computation. In the beginning, only statement instances that executed in the original failing trace are presented precisely in the angelic program. The angelic program is expanded dynamically in an on-demand manner, after each iteration, to provide more precise results.
## 5 Numerical Approach and Procedure for Fluid-Structure Coupling  
The calculations are performed numerically. The detail process on numerical procedure of fluid-structure interaction is shown in Figure 3. At the beginning, based on the initial flow field described by Equation (4) and the boundary conditions Equations (5)-(8), the lift force exerted on the cylinder can be obtained at \(t>t_{1}\) by Equation (15). Then, the displacement and velocity of cylinder can be obtained from the motion Equation (17). Subsequently, the flow field is advanced to the next time step by the integration of Equations (2) and (3) according to the boundary conditions calculated for update, as does the control process with Lorentz force at \(t>t_{2}\). Therefore, the flow field, the hydrodynamic force, the cylinder motion and so on can be obtained in the whole fluid-structure interaction process with this method.  
Figure 3: The detailed process of the numerical procedure of fluid–structure interaction.  
The exponential mapping in the radial direction allows us to work in a very large physical domain, so that we can avoid the well-known blockage effect (i.e., the effect of the size of apparatus or computational box). On the other hand, the grids near the cylinder are fine enough in a very small physical domain, so that the flow field can be described accurately. In the numerical calculations presented in this paper, the physical circular domain in polar coordinates is transformed into the rectangular \((\xi,\eta)\) domain through the coordinate transformation \((r,\theta)\rightarrow(\xi,\eta)\). The computations presented in this paper are run on a mesh consisting of \(N_{\xi}\times N_{\eta}=400\times 256\) grid points.  
The Alternative-Direction Implicit (ADI) algorithm was used to solve the equation of vorticity transport. A Fast Fourier Transform (FFT) algorithm was used to integrate the equation of stream function. Solving Equation (16) using the Runge-Kutta method allows calculating the cylinder motion. For more details on the numerical method and validation of the code, refer to [39; 40; 41; 42]. The concrete procedure for simulating the coupling of fluid and structure is the same as [42]. Moreover, the above numerical methods have the accuracy of second order in space and first order in time [42]. The computational step sizes are \(\Delta\xi=0.004\) and \(\Delta\eta=0.004\) for all simulations appearing in the paper. The input parameters are as follows: density of fluid \(\rho=1.0\times 10^{3}\) kg/m\({}^{3}\), kinematic viscosity \(\
u=1.0\times 10^{-6}\) m\({}^{2}\)/s, free-stream velocity \(u_{\infty}=7.5\times 10^{-3}\) m/s, cylinder radius \(a=1.0\times 10^{-2}\) m, (so that \(\mathrm{Re}=\frac{2u_{\infty}a}{v}=150\), where the flow about the cylinder is fully laminar), density of cylinder \(\rho_{cyl}=2.6\pi\times 10^{3}\) kg/m\({}^{3}\), and \(f=f_{n}=0.0675\)/s (so that \(\rho_{cyl}/\rho=2.6\pi\)). For VIVs in dense fluid, the structural damping is so small that it is negligible [44]. In order to accentuate the effect of viscous damping force (\(\frac{4}{\mathrm{Re}}\frac{dl}{dt}\)), it may even be assumed zero (i.e., \(\xi=0\)).