esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data-Acquisition Set Up
It avoids potential scalability problems and improves the speed of querying and writing to the database. These data volumes are temporarily stored in the control server itself. Figure 1: Outline of the design of the IoT solution. This schematic diagram shows the operation of the IoT solution. Note that only read operations are performed, so the process will never alter machine operation. Twice a day, the information is sent to the Fiware Internet of Things (FIoT) data-processing platform through a communication interface with REST software architecture. The data volumes included in this work that were collected on the FIoT platform exceeded 1,500,000 datums grouped by category, over 3 months of machining center operations, although the specific study described in the following sections only analyzed a very limited part of this information. In this process, another data filtering operation was performed: eliminating possible captures of erroneous measures and adding extra information for later use. This captured information is tagged with machine identifiers, company, location, date, and origin of the data. JSON annotation was also used for the correct management of this information.
## 1 Introduction  
Fatigue is one of criteria in asphalt concrete pavement design, and the fatigue failure of asphalt concrete has a correlation with the magnitude of tensile strain at the bottom of the asphalt layer [1; 2; 3]. Therefore, it is very important to monitor the strain response of asphalt layers in service under vehicle loads [4; 5]. However, the existing methods are very limited in measuring the strain changes for the low modulus of asphalt concrete, which is approximately 1 GPa. Traditional electronic strain gauges have difficulties to work when the insulation and electromagnetism are concerned. Optical Fiber Bragg Gratings (OFBG) sensors have been widely used in civil infrastructures in recent years [6; 7; 8; 9], so some researchers have turned to use optical fiber sensors due to their vibration resistance and electromagnetism immunity [10; 11; 12]. However, the modulus of the traditional optical fiber sensor is so high that it is very hard to obtain the real strain changes of the asphalt pavement [13; 14; 15].  
In this research, a novel polypropylene based OFBG strain sensor with low modulus and large strain sensing scale was designed and fabricated. Because it has almost the same modulus and constitutive relationship of strain and stress with asphalt, PP (polyppropylene) can be regarded as a proper matrix in packaging bare OFBG to fabricate strain sensors to monitor the strain response of asphalt concrete pavement. In this study, the physical and mechanical performance of the PP resin matrix was studied; the appropriate fabrication techniques were developed and the sensing properties of this kind of sensor were calibrated.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 4 Discussions and Conclusions
### Enhancing Control Through Reinforcement Learning and Adaptive Strategies  
Integrating RL techniques with thrust deviation rewards optimizes thrust control. RL enables adaptive adjustment based on real-time engine operating conditions, thus significantly improving pressure regulation effectiveness compared to traditional methods. The introduction of a thrust deviation reward in RL reduces errors in divert and attitude thrust, showcasing the potential of RL in handling under-constrained optimization problems inherent in thrust distribution.
## 4 Discussion
As submitted by [PERSON] and [PERSON] [68], given that IR algorithms aim at reducing noise while preserving the fidelity of fine details, this necessitates a conservative approach to noise reduction for voxels near edges, and therefore, noise is not reduced as much near edges or structures (i.e., \"edge\" effect). The nonuniformity findings of this study suggest that, for CT imaging, a noise evaluation only in homogeneous phantoms could be incomplete and/or partly misleading when IR algorithms are employed. While pure ASiR-V/ASiR offers the maximum noise reduction capability in reconstructed CT images, a blending level of 100% can affect the spatial uniformity of noise mostly at the boundary between different structures. Therefore, for specific clinical applications of CT imaging with ASiR-V and ASiR, the use of a maximum blending level of reconstruction might be not optimal [27; 40; 43; 44; 71; 72]. In particular, the revealed \"edge\" effect might partly limit the potential of IR algorithms in studying small structures (such as metastases, vessels, pulmonary nodules, or brain ischemic lesions), especially when they present reduced contrast. Analysis of noise map histograms (Table 4) showed that, for both ASiR- and ASiR-V-reconstructed CT images, kurtosis and interquartile range/median values monotonically increased and decreased, respectively, with increasing blending level of reconstruction. Moreover, for all the blending levels of reconstruction, noise map histograms of ASiR-reconstructed CT images were characterized by higher or equal kurtosis and interquartile range values compared with that of ASiR-V-reconstructed CT images. Overall, these results might reflect the inherent nonlinear and nonstationary properties of iterative reconstruction algorithms in CT imaging.
## 3 Experiments and Results
### Case 3: Various Walking Uphill Experiment
#### 3.4.2 Results  
The control performance of the proposed method is displayed. Figure 5a,b shows the tracking trajectories of the leg joints. In the first 20 s, the slope angle was approximately 3\({}^{\circ}\) and the gait cycle was approximately 2 s. The slope angle was then increased to 5\({}^{\circ}\) for 20 s. Finally, the slope angle was maintained at 7\({}^{\circ}\). Figure 5c shows the tracking errors of the leg joints. With an increase in the slope angle, the load for the joints increased and the tracking errors of the joints changed simultaneously. Figure 5d shows the change in the control torque of the leg joints. Figure 5e shows the change in \(\hat{Y}\). Figure 5f shows the change in \(\hat{\tau}_{e}\).  
In Table 4, the tracking errors are statistically calculated for the two subjects. The mean errors (MEANs) of the tracking performance for Subject 1 and 2 are 2.4\({}^{\circ}\), 2.9\({}^{\circ}\), 3.4\({}^{\circ}\), and 2.2\({}^{\circ}\), 2.8\({}^{\circ}\), 3.1\({}^{\circ}\), respectively. The minimized mean square errors (MSsE) of the tracking performance for Subject 1 and 2 were 1.6\({}^{\circ}\), 2.3\({}^{\circ}\), 2.6\({}^{\circ}\), and 1.3\({}^{\circ}\), 1.7\({}^{\circ}\), 2.2\({}^{\circ}\), respectively.  
Figure 5: Tracking performance of various walking uphill tests. (**a**) Tracking performance of the knee joint. (**b**) Tracking performance of the ankle joint. (**c**) Tracking errors of the joints. (**d**) Control torque of joints. (**e**) Estimation of the Y of joints. (**f**) estimation of \(\tau_{e}\) of joints.
## 5 Experimental Study and Results
### In-RCS Results and Evaluation  
To validate the real-time usability of the learning-based models in the RCS, we utilized the Jetson AGX Orin Development Kit (NVIDIA, Santa Clara, CA, USA) for the slave robot operation, and evaluated the model's performance under uniform and varying input commands using the control block diagram presented in Figure 14. For a typical master's displacement (\(M_{p}\)), velocity (\(M_{v}\)), and the velocity ratio (\(V_{r}\)), the RNN model makes an appropriate prediction (\(RNN_{p}\)) and performs an error check. In addition, the slave robot receives a compensated displacement value (that incorporates the difference between the model's prediction and the input command). Afterwards, the slave robot actuates for the given step and the difference between the master and slave position (\(S_{p}\)) is computed as the final error (motion lag) for that step. In Figure 15, we present the experimental setup for the evaluation of the above control approach within the RCS using the neural-based controllers.  
The average performance of the GRU model for varying displacement commands (3-5 mm) and a traveling distance of 120 mm by the slave robot is presented in Figure 16.  
Figure 14: Control block with error compensation for the slave robot.  
Figure 13: The original position error of the slave robotic device is shown in the lower panel compared with (**a**) GRU-based and (**b**) LSTM-based predictions for a distance of 150 mm with a translational step of 5 mm.  
Figure 15: The hardware setup for experimental validation of the neural-based models within the RCS using Jetson Orin as the slave device’s processor.  
The choice of 3-5 mm aligns with the experimental observation of an increasing motion gap between the master and slave robot under uniform translational motion as presented in Figure 7A,B. The figure shows the positive impact of the real-time compensating values for input displacement such as 5 mm, thus maintaining the slave robot's smooth translational motion with a minimum and maximum error of 0.014 and 0.04 mm, respectively, as shown in Figure 16. This infers the neural-based model's suitability for prediction and compensation tasks within the slave robot during catheterization and the realization of an equivalent master-slave position trajectory for displacement steps above 1 mm.
# Review on Optimization Techniques of PV/Inverter Ratio for Grid-Tie PV Systems
## 4 Conclusions  
Many studies have discussed the optimization of the PV inverter sizing issue for grid-connected PV systems. The frequently employed inverter-to-PV array formula uses power as a design factor of scaling ratios, and the majority of the studies concentrate on the best uses of c-Si PV module technology. Most studies indicated that the optimal sizing ratio relies on the geographic location characteristics, the PV inverter, and the module material composition, and the recommended size ratio took into consideration all power losses that would affect the inverter's power generation and conversion efficiency when in use.  
The most relative references that mainly discussed the optimization of DC/AC ratio, cost, and tilt angle to maximize annual energy yield for grid-connected PV systems are [18, 27, 28, 29, 30]. These studies were either based on iterative algorithms or trial-based methods that consume a very long time to approach the optimization value of the DC/AC ratio and/or cost. In order to close this gap, this paper empirically analyzed and summarized the literature on inverter sizing ratios according to the various PV module technology types in use worldwide. It also introduced a novel inverter sizing strategy using the Deep Learning network technique that can provide the best value for the sizing ratio. The obtained results demonstrated that under specified climate conditions and component constraints, there is a global minimal point for the cost of a particular PV inverter system. Furthermore, cost minimization was conducted to obtain the corresponding optimal DC/AC ratio and the required PV capacity.  
Conceptualization, H.I.H. and A.H.S.; methodology, H.I.H.; software, H.I.H.; validation, K.A.B., A.H.S. and A.J.H.; formal analysis, C.K.G.; investigation, C.K.G.; resources, C.K.G.; data curation, K.A.B.; writing--original draft preparation, A.J.H.; writing--review and editing, A.H.S.; visualization, C.K.G.; supervision, K.A.B.; project administration, H.I.H.; funding acquisition, A.J.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Figure 8: The relationship between the system cost with respect to PV capacity and the DC/AC ratio for the studied system.
## 3 Methods
### Mid-Surface Projection  
Generation of the mid-surface projection was a fundamental part of the proposed method. In order to calculate it, the scans were divided into 2 mm thick horizontal slices separately for different segments. A mid-surface plane was defined as perpendicular to the transverse plane (XY). It was further aligned to the best-fit line of the flattened slice surface points using the Root Mean Square algorithm. Utilized formulas for slope (1) and offset (2) calculations are presented below:  
\[a=\frac{\sum_{i-1}^{n}(x_{i}-\overline{x})(y_{i}-\overline{y})}{\sum_{i-1}^{n }(x_{i}-\overline{x})^{2}}, \tag{1}\]  
\[b=\overline{y}-a\overline{x}, \tag{2}\]  
Figure 5: Comparison of raw (**a**) and smoothed (**b**) scans.  
Figure 6: Three-dimensional (3D) body segmented scan colored according to the body parts (head, arms, upper/lower torso, hips, and legs.  
where \(a\) is the slope; \(b\) is the offset; \(n\) is the quantity of points in the slice; \(i\) is the number of currently considered points in the slice; and \(x_{i},y_{i}\), \(z_{i}\) are the coordinates of the \(i\)th point in slice.  
The algorithm depended on the proportion of the slice and could output one of the two possible slope coefficients, as shown in Figure 7. For unification, if the angle \(\alpha\) between sagittal plane (YZ, marked in yellow) and the calculated mid-surface best-fit alignment line was less than \(45^{\circ}\) or greater than \(135^{\circ}\), the surface was rotated by \(90^{\circ}\). When the plane was consistently oriented, segment points were projected perpendicularly onto it. After performing the process for each slice separately, the mid-surface projection was obtained.
# An Efficient Method for Document Correction Based on Checkerboard Calibration Pattern
## 3 Proposed Method  
It has been established that 3D page-shape reconstruction methods produce high-quality results when recovering the shape of the document regardless of the content of the document image. However, specialized and expensive hardware is required. Furthermore, prior knowledge parameters from the camera and scanner are needed. In contrast, 2D image-processing techniques do not require any parameters or hardware. Unfortunately, in the case of complex layouts and bad illumination, the shape is often inaccurately recovered. The proposed method combines the advantages of 3D and 2D methods by recovering documents with high quality regardless of their content and without prior knowledge parameters. To obtain the world and image points, the proposed method utilizes checkerboard calibration patterns. Regardless of the content of the document image, camera calibration parameters are produced depending on the computed world and image points. Finally, the radial distortion algorithm is used to correct the warping problems based on the extracted camera-calibration parameters. The block diagram of our method is illustrated in Figure 2.  
Figure 2: General block diagram of our method.  
There are five main steps in the proposed technique. The first step includes removing bad illumination and shading from the document image. A well-defined checkerboard calibration pattern is used in the second step to match the warped shape of the document. Checkerboard corner detection is used in the third step to extract the world points. Camera calibration parameters are calculated in the fourth step. The calculated camera calibration parameters and the world points are used to de-warp the document image in the fifth step. The following subsections go through each stage in depth.
## 3 Background
### General CubeSat Specifications  
The CubeSat design specification states the requirements and regulations a designed CubeSat must meet before consent to fly is granted. The document is separated into general, mechanical, operational, and electrical design segments. The mechanical segment largely details the CubeSat structure and that of the P-POD, particularly the P-POD rails upon which the CubeSat is mounted throughout the launch and slides along upon release. The waiver process for design alteration is presented for deviations from the specified baseline [1]. The design guide, along with the GEVS documentation state the following tests are required before launch acceptance [24]:  
* Random Vibration;
* Thermal Vacuum Bake-out;
* Visual Inspection Qualification;
* Acoustic Test;
* Sine Vibration;
* Mechanical Shock;
* Mechanical Functional Analysis and Test.
# Integrating IP Mobility Management Protocols and MANET: A Survey
## 3 MANET Routing Protocols
### Reactive MANET Routing  
Protocols under this category work on demand. Routes from sources to destinations are only discovered when needed. When a node wants to send a packet to a destination, it checks its routing table. If no route to the destination was found, the source node starts the process of route discovery. Ad hoc On-Demand Distance Vector (AODV) [26], Dynamic MANET On-Demand Routing (DYMO) [27], Dynamic Source Routing (DSR) [28], and Temporally-Ordered Routing Algorithm (TORA) [29] are the typical routing protocols for reactive MANET.  
_Ad hoc On-Demand Distance Vector (AODV)_: This is a reactive routing protocol that does not use source routing. Source routing is used by some MANET protocols where the source node includes the complete route to a destination. AODV instead stores the next hop address for each route entry in the routing table. In order to discover a route to a destination, AODV broadcasts an RREQ message that includes source address, destination address, recent known sequence number of the destination, and unique ID. Such information is used to guarantee loop-free operation. If an intermediate node that knows the address to the required destination or the destination itself receives the RREQ, then a RREP will be generated and sent in a unicast mode to the initiator. Mobile nodes on the route of the RREP will use the RREP to update the sequence number of the required destination.  
AODV uses a hello message to discover neighbor connectivity, and thus to maintain active routes. A node expects a frequent hello message from its neighbors. If the hello message stops arriving, a link failure is assumed. The node will broadcast RERR to distribute a topology change. If the node still requires the route to a destination that uses the failing neighbor, the node must start new route discovery procedure.
# Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs
## 3 FLMPC: Proposed Protocols
### Layers Division  
In FLMPC-One and FLMPC-Two, we split the network into unequal sized layers, because noise intensity is relatively high near the noise source and in the shallow water, while, smoother in deep water. Based upon this fact, the layer size near the surface is smaller in contrast to deep water. This division increases the reliability and reduces the packet drop of FLMPC-One and FLMPC-Two. The expression for unequal size division is given by [22]:  
\[C(x,y)=\left(\frac{k_{1}x_{2}+k_{2}x_{1}}{k_{1}+k_{2}},\frac{k_{1}y_{2}+k_{2}y _{1}}{k_{1}+k_{2}}\right) \tag{13}\]  
where \(x_{1},y_{1},\)\(x_{2}\) and \(y_{2}\) are the coordinates of end points and \(C(x,y)\) is the point of division as shown in Figure 5. Point \(C\) divides \(AB\) in the ratio \(k_{1}\):\(k_{2}\) which depends upon the noise signal's strength.  
Cross nodes reside near the layers as shown in Figure 2. When sensor nodes are randomly deployed, they often leave layers vacant. To make sure every data packet passes through cross node, the deployment of cross nodes is uniform random.
# Reinforcement Learning for Mean-Field Game
## 4 Convergence Result
### Conditions of Lemma 1 Are Met for Any Optimal Oblivious Strategy  
In this section, we show that the conditions of Lemma 1 are met for any optimal oblivious strategy. In the mean-field equilibrium, each agent plays according to the strategy \(\pi\in\mathcal{P}(\alpha)\). If the average population action distribution is \(\alpha\), and each agent takes an oblivious strategy, hence, we must have the evolution of the state space such that the oblivious strategy on those states leads to an average action distribution of \(\alpha\). Since we assume a large number of agents, including agent \(i\)'s own state will not change the population state distribution. So, we let the average population state distribution be \(f_{j}\) at time index \(j\) as,  
\[f_{j}(s)=\frac{1}{n}\sum_{m\in[n]}1_{s_{mj}=s} \tag{20}\]where \(s_{m,j}\) is the state of the agent \(m\) at time index \(j\). Similarly, we also include agent \(i\)'s action as well in the population action distribution \(\alpha\). Then, we have,  
\[\mathbf{E}[f_{j}(s)] =\mathbf{E}\Bigg{[}\frac{1}{n}\sum_{m\in[n]}\mathbb{1}_{s_{m,j}=s} \Bigg{]} \tag{21}\] \[=\frac{1}{n}\sum_{m\in[n]}\mathbf{E}\Big{[}\mathbb{1}_{s_{m,j}=s} \Big{]}\] (22) \[=\frac{1}{n}\sum_{m\in[n]}P\big{(}\{s_{m,j}=s\}\big{)}\] (23) \[=\frac{1}{n}\sum_{m\in[n]}\sum_{s^{\prime}\in\mathcal{S}}P\big{(} \{s_{m,j-1}=s\}\big{)}P\big{(}s|^{\prime},\pi(s^{\prime}),\alpha_{j-1}\big{)}\] (24) \[=\sum_{s^{\prime}\in\mathcal{S}}\Bigg{(}\frac{1}{n}\sum_{m\in[n] }P\big{(}\{s_{m,j-1}=s\}\big{)}\Bigg{)}P\big{(}s|^{\prime},\pi(s^{\prime}), \alpha_{j-1}\big{)}\] (25) \[=\sum_{s^{\prime}\in\mathcal{S}}\mathbf{E}\big{[}f_{j-1}(s^{ \prime})\big{]}P\big{(}s|s^{\prime},\pi(s^{\prime}),\alpha_{j-1}\big{)} \tag{26}\]  
where \(P(\{s_{m,j}=s\})\) is probability of agent \(m\) being in state \(s\) at time index \(j\). Here, Equation (24) follows from the transition probability matrix. Recursively replacing \(f_{j-1}\) in Equation (26) using Equation (21) for all \(j\in[\tau]\) gives the required result of \(f\in\mathcal{D}(\pi,\alpha)\).  
The above statement also implies that \(\alpha\) must satisfy  
\[\alpha_{j}(a)=\sum_{\pi^{-1}(a,j)}f_{j}(s) \tag{27}\]  
where \(\pi^{-1}(a,j)\) represents the set of states \(s\) for which \(a\in\pi(s,j)\). This is equivalent to saying that if all the agents follow the optimal oblivious strategy \(\pi\in\mathcal{P}(\alpha)\), then the population state distribution \(f\) and the population action distribution \(\alpha\) satisfy \(f\in\mathcal{D}(\pi,\alpha)\) and \(\alpha\in\widehat{\mathcal{D}}(\pi,f)\).
## 2 Materials and Methods
### Experimental Design
#### 2.1.1 Scaling Charge Mass  
In this study, Hopkinson-Cranz (H-C) scaling [25; 26] was used to compare the test results at different length scales. For near-identical charge shapes and ambient conditions, this scaling law states that two explosions are expected to produce identical blast waves at distances that are proportional to the cube root of the respective energy release. Since the energy within the explosive is directly proportional to its mass, denoted as W, the concept of scaled distance Z establishes a relationship between blast wave parameters in a free-field environment [10]. This relationship is illustrated in Equation (1), where R represents the stand-off distance:  
\[Z=\frac{R}{\sqrt[3]{W}} \tag{1}\]  
According to the H-C scaling law, pressures and velocities remain unchanged across scales since similar blast waves are generated. However, arrival times, durations, and impulses are scaled by the length scale factor K, as shown in Equation (2):  
\[Z=\frac{R}{\sqrt[3]{W}}=\frac{KR}{\sqrt[3]{K^{3}W}} \tag{2}\]  
In order to facilitate a comparison with the 400 g series of field tests conducted by [11], this study used two reduced-scale PE-4 charges of W\({}_{100\%}\) and W\({}_{75\%}\), amounting to 21.3 g and 9.0 g of PE-4, respectively (equivalent to 25.6 g TNT and 10.8 g TNT, respectively). This calculation was performed using Equation (2) while assuming a TNT equivalence factor of 1.2 for PE-4 [27]. The charge masses were shaped into a cuboid with an approximate ratio of width:heightlength = 1:1:2. For consistency, a 3D-printed mould was used to shape the charge, and its cross-section dimensions were maintained when the detonator was inserted. The explosive was positioned in such a manner that the long side faced the front face of the wall. Specifically, its longitudinal axis was parallel to the wall, as depicted in Figure 1.