esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Macro-Fracture Patterns  
Figure 11 illustrates the macroscopic failure morphology of granite specimens under confining pressures over a wide range of strain rates (10\({}^{-6}\) to 10\({}^{-2}\) s\({}^{-1}\)). The experimental results demonstrate three principal findings: (1) At the strain rate of 10\({}^{-6}\) s\({}^{-1}\), specimens under a 0 MPa confining pressure exhibit vertical fracture planes perpendicular to the end surfaces, representing typical splitting failure. When the confining pressure increases to 10 MPa, Y-shaped fracture patterns initiate from the specimen mid-height with subsequent bifurcation, maintaining geometric stability under elevated confining pressures, indicative of a shear failure mechanism. (2) Under uniaxial conditions (0 MPa) at a strain rate of 10\({}^{-2}\) s\({}^{-1}\), the rock specimen develops a single inclined shear plane, with fracture planes wider than those observed under static loading conditions (10\({}^{-6}\) s\({}^{-1}\)). Additionally, the quasi-static regime (10\({}^{-2}\) s\({}^{-1}\)) produces a higher volume of pulverized rock debris observed on the fractured surfaces compared to static loading, demonstrating fundamental morphological differences compared to the static loading condition (10\({}^{-6}\) s\({}^{-1}\)). This result aligns with existing research [6] showing strain rate-induced transition from axial splitting to shear-dominated macro-fracture pattern with increasing strain rate in granite. (3) At a 10\({}^{-6}\) s\({}^{-1}\) strain rate with confining pressures increasing from 10 MPa to 40 MPa, a progressive failure pattern evolution is observed: Y-shaped fractures persist at 10-20 MPa, multi-branching fractures emerge at 30 MPa, and characteristic X-conjugate shear bands form at 40 MPa. Systematic analysis reveals that within the triaxial quasi-static loading regime, the macroscopic failure modes of granite are governed by strain rate-confining pressure coupling effects, following a consistent transition rule: increasing strain rates and confining pressures promote gradual evolution from hybrid tensile-shear failure to shear-dominated failure mechanisms.
# Physics-Guided Neural Network-Based Feedforward Control for Seamless Pipe Manufacturing Process
###### Abstract
This setup inherently limited the system's ability to perform in real-time scenarios due to latency issues and the absence of hard real-time communication capabilities. The introduction of specialized hardware, such as Siemens' neural processing unit (NPU), represents a transformative step towards the seamless integration of AI into industrial automation systems. These processors, originally designed for applications like image recognition and classification [21; 22], have now been adapted for the execution of neural network-based control algorithms directly within the PLC environment. Compared to external GPU-based setups, like those described by [PERSON] et al. [23], where reinforcement learning methods were applied using GPU-accelerated industrial control hardware, the Siemens NPU offers a streamlined, cost-effective solution with lower power consumption and easier integration into existing PLC systems. Its ability to execute neural network algorithms directly within PLCs ensures minimal latency, enhanced responsiveness, and consistent high-quality performance. This paper is organized as follows. First, mathematical models of the hydraulic actuator and the combined dynamics of the actuator and external resistive forces are developed, and unknown parameters are identified. These models are then reduced to a form suitable for integration into the PGNN framework. The theoretical background of PGNNs tailored to specific application is presented, followed by a description of the experimental setup, which consists of a scaled industrial hydraulic system used for both training and validation. Next, the training results of the PGNN are presented alongside a benchmark neural network used for comparison. Finally, the proposed methods are validated through two sets of experiments: one focusing on constant force setpoint tracking with varying speeds and another on varying force tracking with speed profiles representative of the operational conditions in pipe manufacturing. Conclusions are then drawn to summarize the findings and highlight future directions.
## 3 Validation with Specim Experiments
### Experimental Results  
The structure is subject to three impact loads. The time-domain strain signal is recorded by the transducer for 10 consecutive impacts, as shown in Figure 3a. An inspection of a sequence of the strain signal reveals that the peak strain of the impact increases with an increase in the impactor angle. The increase in impactor angle results in a higher impact energy, which, in turn, causes an increase in strain. The peak strains and impact durations for all the experiments are listed in Table 1. The experiments with the impactor loading angles of 32\({}^{\circ}\), 47\({}^{\circ}\), and 65\({}^{\circ}\) show average peak strains of \(-\)79.25, \(-\)121.30, and 168.40 \(\mu\varepsilon\), respectively, and standard deviations of 3.63, 5.68, and 3.18 \(\mu\varepsilon\), respectively. All the strain responses returned to their original state after the peak strain. The impact duration is  
Figure 2: Experimental set-up. (**a**) Experimental set-up for measuring transducer and structural strain due to impact; (**b**) structure installed with impact load transducer and strain gauges (y-axis) for validation.  
determined by the duration of the high strain rate region before and after the peak strain. The three experiments had nearly identical average impact durations.
# Prediction-Based Energy Saving Mechanism in 3 GPP NB-IoT Networks
## 1 Introduction
On the other hand, the NB-IoT enables a piggyback small application packet into the non-access stratum (NAS) signaling message. This scheme improves the transmission efficiency by skipping the bearer setup. Despite these features, there has been no progress in the scheduling request procedure to reduce energy consumption. Given that the NB-IoT is developed based on LTE, auxiliary procedures are necessary for the uplink radio resource allocation that is performed only by a random-access procedure. Figure 1: Internet of things (IoT) services according to coverage and throughput. In this article, we proposed a novel mechanism to improve energy consumption based on the predictive resource allocation. Prediction-based algorithms have been studied to guarantee the bandwidth and minimize the latency in poor channel condition. In [7], the authors investigated the predictability of user behavior to minimize bandwidth to achieve certain quality of service. However, this work does not exploit the non-continuous traffic characteristics and power consumption problem. In [8; 9], the algorithm that predicts uplink radio resources by monitoring bandwidth measurement for the video stream was proposed. These papers analyzed real-time service which generates packet with short deterministic interval continuously. In contrast, our work is for IoT data traffic that only a few handshakes with small size packet exist. In [10], the authors made traffic model for machine-to-machine communication and proposed uplink packet prediction algorithm based on the idea that data traffic can be generated at similar time by machines in the same group with a high probability. But again, it does not exploit the correlation between uplink and downlink packet. The remainder of this article is organized as follows. We briefly introduce the NB-IoT technology in Section 2 and identify the energy consumption problem caused by scheduling request procedures in Section 3. Then, we propose the algorithms to alleviate this problem in Section 4. Section 5 presents the simulation results and evaluation, and we provide a conclusion in Section 6.
## 1 Introduction
### Exergy and Thermoeconomics  
According to its definition, exergy can be considered as a rational parameter to assign costs along an energy system. Engineers and architects work to solve design problems and to optimize buildings under energy, economic and environmental perspective. If exergy analysis is connected to economics, a cost allocation procedure can be developed which becomes a powerful tool for systematically analyzing thermal systems.  
The economic analysis in building thermal installations aims to calculate the cost of all flows and particularly of final flows (heating, cooling and DHW) from the prices of the fuels consumed and the investment and maintenance costs of equipment. However, this information is not enough to calculate the costs when the system has several products, as we need guidance for allocating these costs among the different products. Thermodynamic analysis allows calculation of the efficiency of each process, as well as location of the irreversibilities, but it cannot evaluate the effect of irreversibility throughout the production process in terms of costs [8].  
The knowledge area that gathers exergy analysis and economics is known as Thermoeconomics, although the most appropriate name is Exergoeconomics, as it was proposed in 1984 by [PERSON] [12]. Referring to that author, the so-called advanced exergy analysis methodology has been developed and, in recent years, some application in buildings systems emerged, such as Refs. [13; 14]. The work done in those references aim to solve the problems related to dynamic conditions in buildings.  
As mentioned, the physical entity linking thermodynamics and economics is irreversibility or exergy destruction. Irreversibility can be used to detect the degree of perfection of an energy process so all processes can be analyzed under the same base. Irreversibility is related to the consumption of natural resources and, therefore, to the cost of the products obtained. Despite its interesting meaning, exergy analysis application in building energy supply systems is relatively new and there are still many methodological aspects to solve; nevertheless, different applications of exergy have come up and some of them are found in references [15; 16; 17]. Furthermore, the name low-ex buildings is now used to designate low-energy consumption buildings, as state references [18; 19].  
Figure 1: Profile of annual energy and exergy flows in a heating and domestic hot water (DHW) facility.
## 4 Invariant Multi-Mode Vectors Pushover Analysis
### The Extended N2 Method  
The extended N2 method was developed by [PERSON] and [PERSON] at the University of Ljubljana [65]. The extended N2 method improves the estimation of seismic demand compared to the basic N2 method by considering higher-mode effects both in plan and in elevation [65]. The extended N2 method provides a more accurate distribution of seismic demand throughout the structure, particularly in the upper part of the building and at the flexible edges. This improvement is achieved by combining the results of basic pushover analysis and those of standard elastic modal analysis. The extended N2 method yields fair, conservative estimates of response and greatly improves the prediction of seismic demand in the specified areas. The extended N2 method involves the following detailed procedure:  
1. Perform the basic N2 analysis [42; 65], which includes a pushover analysis to determine the target displacement at the roof level. This analysis is done separately for each horizontal direction of the building.
2. Perform an elastic modal analysis of the building using a 3D mathematical model [66]. This analysis considers all relevant modes of vibration in each horizontal direction. The results of the modal analysis are normalized so that the roof displacement at the center of mass (CM) is equal to the target displacement obtained from the pushover analysis.
3. Determine the seismic demand by applying correction factors to the results of the pushover analysis. Two sets of correction factors are used: one for displacements in plan and the other for story drifts along the elevation. The correction factors are determined separately for each horizontal direction and depend on the location in plan.
## 3 Experimental Results and Analysis  
### Design of the LP Horn Antenna  
In this design, the LP cone horn antenna as shown in Figure 9 is adopted as the feed source of the reflectarray antenna. The proposed LP horn antenna operates within the frequency band of 22-36 GHz. Therefore, a WR34 standard rectangular waveguide is connected to the cone speaker, and then a 2.92 mm coaxial adapter is used to convert the WR34 standard rectangular waveguide to achieve the connection between the horn antenna and the final test equipment. In order to facilitate physical testing, a flange plate is designed for the assembly of the test. Its dimensions are L = 20 mm, W = 14 mm, and H = 17 mm.  
Figure 10a shows the reflection coefficient of the LP horn antenna. It can be seen that within the frequency band range of 22-36 GHz, the reflection coefficient is always less than \(-10\) dB. Figure 10b is the normalized radiation pattern of the speaker at a center frequency  
Figure 8: (**a**) Simulated amplitude and phase under vertical incidence and (**b**) under oblique incidence.  
Figure 9: Three-dimensional view of the LP horn antenna.  
of 26 GHz. It can be seen that its peak gain is 13.5 dBi and the cross-polarization is less than \(-\)50 dB. If the \(-\)10 dB lobe width of this speaker is 64\({}^{\circ}\), then the opposite half-angle is 32\({}^{\circ}\).
# Application of Tucker Decomposition in Temperature Distribution Reconstruction
## 3 Numerical Simulation and Analysis
### Single-Peak 3D Temperature Distribution Reconstruction  
Set the single-peak 3D temperature distribution model as follows:  
\[T_{1}(x,y)=600+\frac{a}{\left(x-2\right)^{2}+\left(y-1\right)^{2}+\left(z-2.5 \right)^{2}+1}, \tag{18}\]  
where \(T_{1}(x,y)\)--temperature at the points \((x,y,z)\) of the single-peak 3D temperature distribution (K); \(x\)--X coordinate(m); \(y\)--Y coordinate(m); \(z\)--Z coordinate(m) and \(a\)--initial boundary condition.  
In the reconstruction area, use Equation (19) to calculate the temperature value. In order to reduce the amount of calculation,  
3D temperature distribution model and the boundary conditions as shown in Appendix A Tables A1 and A2.  
According to the 3D temperature distribution reconstruction calculation process, the prior dataset is decomposed by the Tucker decomposition algorithm to obtain the reconstruction coefficient matrix. Select 16 coordinate points as temperature measuring points to reconstruct the 3D temperature profile.  
The temperature distribution of test condition 5 calculated is shown in Figure 2.  
The temperature field result of the test condition 5 obtained by the Tucker algorithm is as follows.  
The boundary condition of test condition 5 is outside the constraint conditions of the priori dataset. As shown in Figure 3, the proposed algorithm still realizes the effective reconstruction.  
Figure 3: Reconstruction result of single-peak temperature field model in cubic area.  
Figure 2: Calculation results of single-peak temperature field model in a cubic area.  
To analyze the adaptability of the 3D temperature distribution reconstruction algorithm under different working conditions, the temperature profile data of the five test working conditions are reconstructed, and the reconstruction error is shown in Figure 4 below.  
As shown in Figure 4, the temperature field reconstruction results under five test conditions are analyzed. It can be seen that in the construction of single-peak 3D temperature distribution, the three-dimensional temperature field reconstruction algorithm based on the Tucker decomposition can effectively reconstruct the temperature profile data under different conditions.
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 5 Model Fitting Results  
The single-slope log distance PL model (1) and (2) was fitted to training data. The training data were created using the PL distribution defined in the three examples. The model fitting results are presented and analyzed in Sections 5.1-5.3. In all these examples, for simplicity, the training data were created assuming omnidirectional antennas at both endsof the link and with a large sample size. Large sample size is needed to avoid uncertainty in the parameter estimates [5; 10]. Additionally, a uniform distribution of distances was used [11; 40].
# Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology
###### Abstract
Blockchain technology, when merged with the Internet of Things (IoT), delivers a wide range of advantages that have the potential to transform many industries and services. This powerful combination enhances data security, transparency, and efficiency in ways that did not exist before. By providing a secured ledger concept for IoT devices to record and share data, blockchain ensures the integrity of information, making it a game-changer in fields such as supply chain management, healthcare, and environmental monitoring. Also, the promising synergy between blockchain and IoT holds great potential for revolutionizing various industries and applications by providing data integrity and security, transparency, decentralization, scalability, energy efficiency, and sustainable systems. In the case of this research, like environmental monitoring, blockchain, when merged with IoT, can contribute to sustainable practices by ensuring data transparency and accuracy and promoting eco-friendly policies, which have many environmental benefits. To address this significant gap in our environmental awareness, a system has been devised. This system employs sensors for detecting carbon monoxide, carbon dioxide, temperature, and humidity. The Arduino Uno, with the microcontroller ATmega, a low-power and cost-effective processing solution, is the central hub for data collection and processing. It offers an ideal low-cost and low-power consumption platform. The Internet of Things (IoT) and cloud computing are two of the most transformative technologies in this context. The IoT introduces a paradigm where devices autonomously sense, identify, process, and communicate with each other without human intervention. The IoT cloud system affords a consolidated perspective for accessing IoT resources and their capacities through well-defined Application Programming Interfaces (APIs), which conduct their functions within the cloud infrastructure [3]. Information stored within the cloud can be accessed, making it easier to analyze and, ultimately, adding to more effective solutions for managing air pollution to a certain extent. This paper introduces a solution that provides a low power and relatively low cost way to meet the requirements and needs of many IoT stations covering industrial and crowded wide areas.
## 3 Results and Discussion
### Influence of Different Process Parameters on MLA
#### 3.1.2 Holding Temperature  
When the pressing process is over, it will immediately enter the holding pressure process (applying a constant pressure of 200 N to hold the pressure until the mold temperature drops to the specified holding pressure-temperature). The holding temperature is also one of the main factors affecting the quality of the lens. The lower holding temperature means the microlens array experiences a longer holding time. Therefore, the microlens array's return deformation reduces. With the molding temperature of 130 \({}^{\circ}\)C, molding pressure of 400 N, and holding temperature of 90 \({}^{\circ}\)C, PMMA is observed to have elastic recovery and shrinkage after removing the holding pressure during cooling and demolding.  
Figure 5a shows the lens forming profile at the holding temperature of 90 \({}^{\circ}\)C. As the holding temperature decreases, the elastic recovery of PMMA is eliminated, with the defects of the aspheric lens array reduced, which means the forming quality is improved. Figure 5b shows the copied curve of the lens profile at the holding temperature of 70 and  
Figure 4: (**a**) Aspheric microlens array image at a molding temperature of 100 \({}^{\circ}\)C, (**b**) the comparison of aspheric microlens profile and mold profile at a molding temperature of 100 \({}^{\circ}\)C, (**c**) aspheric microlens array image when the temperature is 130 \({}^{\circ}\)C, (**d**) the comparison of the aspheric microlens profile and mold profile when the molding temperature is 110 \({}^{\circ}\)C/130 \({}^{\circ}\)C, and (**e**) the surface of the aspheric microlens array with bubbles when the molding temperature is 150 \({}^{\circ}\)C.  
50 \({}^{\circ}\)C. When the holding temperature drops from 90 to 50 \({}^{\circ}\)C, the sample's surface quality is significantly improved. Simultaneously, by observing the aspheric microlens array with magnification, at the holding temperature of 90 \({}^{\circ}\)C, the PMMA interface's adhesion and the elastic recovery deformation of the material affect the morphology and imaging effect of the microlens. As the holding temperature decreases and the holding time increases, the microlens' surface morphology becomes better (See Figure 5c).
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 3 Scalable TCP for 5G MmWave Networks
### MmWave Scalable TCP
#### 3.3.1 Cwnd Increase Mechanism of mm-Scalable TCP  
By modifying the cwnd increase mechanism, mmS-TCP can improve TCP fairness. When 100 ACKs are received, the S-TCP increases the size of cwnd by 1 packet. The increase due to the constant value (1 packet) according to 100 ACKs reception provides a faster delivery rate than NewReno. However, a rapid increase in S-TCP may be detrimental to competing TCP flows using other congestion controls. To alleviate the aggression of S-TCP and prevent a decrease in throughput, we redesigned the increasing constant of cwnd as follows:  
1. For each ACK received: \[\text{cwnd}\leftarrow\text{cwnd}+\frac{1}{a}\] (3) where \(a\) increases to 125 by 1 each time ACK is received.  
Each time an ACK is received, 1/100 is added to the S-TCP, and when 100 ACKs are received, the cwnd increases by one packet. That is, the increase rate and aggression of S-TCP are determined by the value \(a\) of Equation (3). If the vale \(a\) is small, the S-TCP increases faster, and if the value \(a\) is large, the cwnd increases slowly.  
We changed the value of \(a\) to gradually increase from 1 to 125 through the counter to reduce the aggression of S-TCP but prevent throughput degradation. The value of \(a\) increases by one from 1 to 125 each time an ACK is received. The maximum value of \(a\) is set to 125, so that cwnd is increased more slowly than the S-TCP. That is, in the early stages of cwnd increase, the mmS-TCP rises faster than S-TCP, and in the second half of the cwnd increase, it rises more gently than S-TCP to increase fairness with competition flows.  
Figure 3 shows the cwnd increase curves of the S-TCP and the redesigned mmS-TCP. mmS-TCP initially increases the cwnd faster than does S-TCP, and then slowly increases from the time \(a=125\). Compared with the early stage of cwnd increase, the slowly increasing rate allows mmS-TCP to operate more stably.  
Figure 2: Throughput changes of S-TCP, HS-TCP, and CUBIC TCP.