esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Flexural Formulation
### Flexural Analysis  
#### 2.3.1 Forces  
Compressive Forces  
Compressive concrete contribution:  
By integrating the Hognestad's profile [11] over the area from the neutral axis up to the maximum compressive concrete fiber strain, the concrete compressive force (\(C_{c}\)) is determined for the section, see Figure 5, based on the following equations:  
\[C_{c}=\ \int_{0}^{C}f_{c}\cdot b\ dy \tag{5}\]  
\[C_{c}=\ f_{c}^{\prime}b\cdot\frac{\varphi}{\epsilon_{c}^{\prime}}\bigg{(}c^{2 }-\frac{\varphi}{3}\frac{c^{3}}{\epsilon_{c}^{\prime}}\bigg{)} \tag{6}\]  
Compressive Steel Contribution:  
The compressive steel force (\(C_{s}\)) is a direct linear relationship with the corresponding steel strain (\(\epsilon_{s}\)\({}^{\prime}\)) in addition to subtracting the contribution of the concrete in compression occupied by the compression steel bars. This relationship was determined based on the assumption of the bilinear response of steel bars.  
\[C_{s}=A_{s}^{\prime}f_{s}^{\prime}-A_{s}^{\prime}f_{c}^{\prime}((2\frac{ \epsilon_{s}\prime}{\epsilon_{c}\prime}-\bigg{(}\frac{\epsilon_{s}\prime}{ \epsilon_{c}\prime}\bigg{)}^{2}) \tag{7}\]  
where \(f_{s}^{\prime}\) is computed from Equations (1) and (2) by substituting \(\epsilon_{s}\prime\) for \(\epsilon_{s}\).  
Tensile Forces  
Tensile Concrete contribution:  
Figure 5: Beam and cross section geometrical and material parameters.  
The concrete tensile contribution is divided into two main profiles, Figure 4. The first profile is a linear relationship up to the cracking strain (\(c_{c}\)), with a slope equal to the concrete modulus of elasticity (\(E_{c}\)). This profile leads to a tensile force (\(T_{c1}\)) equals to:  
\[T_{c1}=\ \int_{0}^{d_{z}}f_{t}\cdot b\ dy \tag{8}\]  
\[T_{c1}=\frac{bf_{r}^{2}}{2E_{c}\varphi} \tag{9}\]  
The second profile is a descending curve, which mainly contributes to the total concrete tensile force. This profile is a result of the concrete softening, concrete stiffening due to the steel bond as well as the residual stresses due to shrinkage effect. The tensile force due to this profile (\(T_{c2}\)) is calibrated against global experimental response to be a function of the concrete cracking strength (\(f_{r}\)) and the steel yielding strain (\(c_{y}\)). The tensile constitutive model and its calibration against experimental results are presented in Section 3 of this paper. The equation for (\(T_{c2}\)) is presented there.  
Tensile steel Contribution:  
The tensile steel force (\(T_{s}\)) follows the same bilinear behavior based on Equations (1) and (2):  
\[T_{s}=A_{s}E_{s}\varepsilon_{s}-A_{s}f_{ct}(\varepsilon_{s})\,\ when\ f_{s}\leq f_{y} \tag{10}\]  
\[T_{s}=A_{s}\big{(}f_{y}+E_{s}^{\prime}\big{(}\varepsilon_{s}-\varepsilon_{y} \big{)}\big{)}-A_{s}f_{ct}(\varepsilon_{s})\,\ when\ f_{s}>f_{y} \tag{11}\]  
where \(f_{ct}(\varepsilon_{s})\) is taken from the uncracked or post-cracked parts of the concrete tensile constitutive functions. Note that \(f_{ct}(\varepsilon_{s})=0\), _when_\(\varepsilon_{s}\geq\varepsilon_{tt}\).
# Rock Breaking Performance of TBM Disc Cutter Assisted by High-Pressure Water Jet
## 2 New Method of Rock Broken by a TBM with a High-Pressure Water Jet  
This paper presented a new method of rock breaking that is a TBM coupled with a high-pressure water jet. As shown in Figure 1, water jet nozzles are set up on the TBM cutter head, located at the front of the disc cutters and at the back of the scrapers. On the new cutter head, the disc cutter group (center cutter, panel cutter, and edge cutter) is still the main body for rock breaking, and both of the water jets and the scrapers play an auxiliary role. With the impacting of the water jet, a groove and numerous tiny cracks will appear on the dense rock mass, which can increase the degree of freedom for rock breaking. The scrapers can play a role of protecting the water jet nozzle to avoid hitting the slag stone broken by the front disc cutter. In the condition that there are few scrapers set on the TBM cutter head, it is necessary to equip a protecting component just like the scrapers.  
Figure 1: New tunnel boring machine (TBM) cutter head with a high-pressure water jet nozzle.  
Figure 2 shows the mechanism of rock broken by the disc cutter coupled with a high-pressure water jet. The rock breaking mechanism is different when the nozzle is located at the center section of the disc cutter or beside the center section of the disc cutter. As shown in Figure 1(a), the groove on the rock mass formed by the impacting of the high-pressure water jet is at the center section of the disc cutter. Therefore, the disc cutter can easily roll and break the rock mass with the groove. Figure 1(b) shows that the groove is beside the center section of the disc cutter. Although there is no rock broken by the water jet on the rolling path of the disc cutter, the force loaded by the rock mass will decrease and the width of the crushing area will increase in the process of the rock being broken by the disc cutter rolling because the rock between the groove and disc cutter is located at the stress concentration area. With the degree of freedom provided by the grooving, this part of the rock mass will be broken by the thrust force of the disc cutter in the form of brittle cracking.
## 5 Simulation  
### Latency Distribution  
By simulation we demonstrate the efficacy of the proposed scheme. We assume a complete binary tree as the data gathering tree. The number of nodes considered have been \(n=2^{7}-1=127\) and \(n=2^{9}-1=511\). The number of minislots were varied from either \(m=10\) or \(m=30\). We compare the performance of the proposed scheme with a baseline scheme defined as follows. The baseline scheme uses UNIFORM scheme for the medium access, and uses a simple in-network aggregation of combining available data without selective forwarding or overhearing. The latency has been measured in terms of the number of frames it takes for the extreme data arrives at the sink. Recall the latency is denoted by a RV \(T\), and for a given deadline \(d\) we would like to maximize \(\mathbb{P}(T\leq d)\). Thus we examine the CDF of the latency for comparison.  
Figure 7 shows the CDF of the latency when \(b=1\) for \(n\) = 127 and 511, each for \(m\) = 10 (left) and 30 (right). We see that the proposed scheme clearly outperforms the baseline scheme. For example when the probability of retrieval of the maximum data is given by 0.8, the proposed scheme achieves the latency that is roughly 19%-20% (resp. 15%-17%) lower than the baseline scheme when \(m=10\) (resp. \(m=30\)). The overall latency is reduced for larger \(m\) since the contention window has become wider. The relative performance of the proposed scheme is slightly better for smaller \(m\), which suggests that as the level of contention become higher, the proposed scheme becomes more effective.  
Figure 8: Comparison of CDF of latency for \(m=10\) and 30 when \(b=3\).  
Figure 7: Comparison of CDF of latency for \(m=10\) and 30 when \(b=1\).  
Figure 8 shows the CDF of the latency when \(b=3\) for \(n=127\) and 511, each for \(m\) = 10 (left) and 30 (right). The reduction in latency at the probability of retrieval of 0.8 is 32-33% (resp. 30-32%) when \(m=10\) (resp. \(m=30\)). Clearly the gains have increased compared to the case where \(b=1\). With larger \(b\) the network undergoes higher level of contention and temporary congestion, thus the gain in latency reduction due to the proposed scheme increases under high load on the network. The mean latency has been compared in Table 1. The relative reduction in mean latency by the proposed scheme ranges 15.1-18.0% when \(b=1\), and 31.1-34.3% when \(b=3\).
###### Abstract  
The quadratic electro-optic effect/Kerr coefficients were measured for the first time for metal nanoparticles. In particular, gold nanoparticles in glass were studied. Measurements were made using the field-induced birefringence method at a wavelength near the onset of the surface plasmon resonance. The magnitudes of the Kerr coefficients for different sizes of gold nanoparticles in glass were measured and compared with that of subnanometer size metallic particles in non-conjugated conductive polymers. The magnitude of the Kerr coefficient was found to increase rapidly (about d\({}^{-3}\)) when the diameter, d, of the nanoparticles was decreased. This is consistent with the existing theories and understanding of nonlinear optics in metal nanoparticles. The results imply a broad range of new applications of metal nanoparticles in electro-optic switching/modulation, low-cost Kerr cells and other uses in optoelectronics.  
quadratic electro-optic effect; metal nanoparticles; nonlinear optics; Kerr coefficient 1
## 3 Research Design and Methodology
### Analysis Method  
The VARM, established by [PERSON] [17], is a type of multivariate auto-regressive model that reflects time [55; 72]. It combines time series models of variables that have causal relationships into a vector form. Specifically, the VARM dynamically analyzes how past levels of a variable affect current values within a time-series dataset.  
The VARM offers a systematic yet flexible way of capturing complicated real-world behavior. It can capture time series data's intertwined dynamics. Such a model provides individuals with improved forecasting performance. VARM captures co-movements that are not detectable in univariate or bivariate models because they incorporate both the current and lagged values of multiple time series [73]. In addition, VARM has been assessed to have flexibility because it can adapt to a wide variety of scenarios, making it applicable in many different fields. The following are the disadvantages of Vector Autoregression (VAR) models. VARM assumes that the data are stationary. Non-stationary time-series must be differenced to achieve stationarity, which can complicate the analysis. Choosing the appropriate lag length can be subjective, significantly impacting the model's performance. Even though the disadvantage of VARM, it has been an accepted and widely used method for portraying these co-movements.  
This study utilizes the VARM to analyze the causal relationships between the GSCPI and the stock prices of global logistics companies. Technical analysts contend that stock prices already reflect all new information, including news and macroeconomic factors [74]. The major applications of the stock market include stock price and trend prediction, risk analysis and return forecasting, and index prediction [75]. Therefore, we think that stock prices are adequate to represent the conditions of global logistics companies. The VARM can be represented by Equation (1), where \(y_{1,t}\) is the variable, \(y_{1}\) is the first observation at time \(t\), \(y_{2,t}\) is the first observation of the variable \(y_{2}\), and \(t\) represents the second observation of the variable. Before conducting the VARM analysis, it is essential to determine how past values influence current ones. The parameter p indicates how many past periods are included in the VARM analysis, and the optimal lag length is usually determined by the lowest values of the Akaike information criterion (AIC) or Schwarz information criterion (SIC) [76; 77; 78]. In this study, the AIC was employed to select the optimal lag length. The empirical analysis using the VARM was conducted with EViews 12, a statistical package. Equation (1) is as follows:  
\[\begin{split} y_{1,t}&=\text{Bc}_{1}+\text{B} \varnothing_{11,1}y_{1,t-1}+\text{B}\varnothing_{12,1}y_{2,t-1}+\text{Be}_{1,t} \\ y_{2,t}&=\text{Bc}_{2}+\text{B}\varnothing_{21,1}y_ {1,t-1}+\text{B}\varnothing_{22,1}y_{2,t-1}+\text{Be}_{2,t}\end{split} \tag{1}\]
## 3 Results
### Results and Predictions on Test Pictures
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy (\%)**} & \multicolumn{2}{c}{**Precision (\%)**} & \multicolumn{2}{c}{**Recall (\%)**} & \multicolumn{2}{c}{**F\({}_{1}\) Score (\%)**} \\ \hline  & \multicolumn{2}{c}{**Deep**} & \multicolumn{2}{c}{**Deep**} & \multicolumn{2}{c}{**Deep**} & \multicolumn{2}{c}{**Deep**} & \multicolumn{2}{c}{**Deep**} \\  & **SVM 4** & **Learning** & **SVM 4** & **Learning** & **SVM 4** & **Learning** & **SVM 4** & **Learning** \\  & **13** & & & & **13** & **13** & **13** \\ \hline Mean & 83.09 & 85.05 & 90.54 & 88.35 & 81.12 & 86.28 & 83.11 & 86.27 \\ Best model (\%) & 64.29 & 35.71 & 85.71 & 14.29 & 46.43 & 53.57 & 64.29 & 35.71 \\ Best model Deep Learning & \multicolumn{2}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ @3\% (\%) 1  & 17.86 & 82.14 & 42.86 & 57.14 & 14.29 & 85.71 & 14.29 & 85.71 \\ Best model SVM @3\% (\%)2  & 28.14 & 17.86 & 92.86 & 7.14 & 64.29 & 35.71 & 82.14 & 17.86 \\ Improvement (\%)3  & 17.86 & 46.43 & 7.14 & 42.86 & 17.86 & 32.14 & 17.86 & 50.00 \\ \hline \hline \end{tabular}  
\end{table} 
Table 4: SVM and deep-learning results. Figure 7: Test picture segmentation example: (**a**) original image, (**b**) SVM segmentation, (**c**) SVM errors, (**d**) deep-learning segmentation, (**e**) deep-learning errors.
# Exploring the ViDiDetect Tool for Automated Defect Detection in Manufacturing with Machine Vision
## 3 Proposed Solution to the Problem
### The Tested Detail and the Machited Surface  
The tested detail is a car suspension knuckle, which is produced in one of the automotive industry plants located in Bielsko-Biala, Poland. The view of the tested element is shown in Figure 4.  
According to the collective information obtained regarding the replacement of cutting tools on machine tools, the cutter responsible for the machining of brake calipers (Figure 5) is replaced every 200 cycles.  
Figure 4: Car steering knuckle with machined surfaces for tool wear testing.  
Figure 5: Selected machined surface for testing—brake caliper (marked in red).  
In order to analyze the workpiece, attention should be paid to the characteristic features of the surface at two moments: before the tool change (Figure 6) and just after the tool change (Figure 7). The remaining chips and the uneven edge after machining are marked in red.  
The above figures show that, in the final phase of the tool life, the remaining chips after machining appear on the surface margin, which are not visible on the details made just after replacement.  
The task of the proposed machine vision system is evaluating the wear of the cutting tool based on the recognition of whether chips are detected on the edge of the machined surface. During the tests, photos of the details were linked to the tool change schedules at the plant.  
An alternative solution is testing the cutting tool surface directly, also known as tool surface inspection or tool wear measurement, can be a viable approach in certain manufacturing and machining scenarios. However, there are several challenges and limitations associated with direct tool surface inspection that may make inspecting the machined workpiece surface method more practical in some cases. Machine vision systems that inspect the machined workpiece surface offer advantages such as continuous monitoring, real-time feedback, non-intrusive inspection, and the ability to assess the impact of tool wear on workpiece quality. These systems can capture high-resolution images of the workpiece and analyze them using advanced image processing techniques and artificial intelligence algorithms to detect and quantify wear patterns, defects, and deviations.  
Figure 6: Detailed photos taken with a service life of 170-200 tool cycles.  
Figure 7: Detailed photos taken just after changing the cutting tool.  
Ultimately, the choice between direct tool surface inspection and workpiece surface inspection depends on the specific needs of the manufacturing process, the accessibility of the cutting tool, the desired level of automation, and the trade-offs between down-time, safety, and data accuracy. In many modern manufacturing environments, machine vision systems are preferred for their ability to provide comprehensive, real-time, and non-intrusive insights into tool wear and workpiece quality.
## 3 The Proposed Method  
### Construction of Two-Dimensional Feature Space  
The fault signal of the rolling bearing presents cyclostationary characteristics, so the fault source information can be highlighted by extracting its cyclostationary quantity. Therefore, based on the idea of the cyclic spectrum, this paper constructs a two-dimensional feature space to realize the feature enhancement of weak signals. As mentioned in Section 2.1,  
Figure 1: The fully connected layer.  
the calculation efficiency of high-order cyclostationary quantities is low, and the cyclic periodic spectrum is affected by the window function, which is not suitable for the pre-processing process of model data. However, compared with the Fourier transform, the wavelet function has stronger adaptability in some cases and performs well in fault diagnosis. To improve adaptability, wavelet transformation coefficients are proposed to replace the Fourier transform coefficients. As described in Equation (9):  
\[\begin{array}{rl}WT_{x}(a,b)&=\frac{1}{\sqrt{a}}\int_{-\infty}^{\infty}\int_{- \infty}^{\infty}x(t)\Psi^{*}(\frac{t-b}{a})e^{-j2\pi ft}dadt\\ &\quad=\int_{-\infty}^{\infty}\left\langle x(t),\Psi_{a,b}(t)\right\rangle e^{ -j2\pi ft}dt\end{array} \tag{9}\]  
where \(\Psi\) is the waveform basis function; \(\Psi^{*}\) is the conjugate of \(\Psi\); \(\Psi_{a,b}(t)\) is obtained by performing time direction translation and scale direction scaling on the basic wavelet function; a is the scale factor; and b is the time factor.  
Through the study of the fault mechanism of rolling bearings, as described in paper [26], the fault signal characteristics of rolling bearings are close to the Morlet wavelet. Therefore, Morlet is used as a wavelet basis for analysis when constructing two-dimensional feature space based on WSC.
# Achieving Low Latency Communications in Smart Industrial Networks with Programmable Data Planes
## 6 Results and Discussion
The rest of MQTT packets are MQTT control packets, related to the connection establishment between the IIoT device and the broker. A total of 12,165 TCP packets were sent to the cloud, during a period of about 638 s. A capture of packet length statistics has been taken, which shows a total of 870,071 bytes sent and a mean throughput of 1363.75 bytes/s in that period. These statistics can be examined in the packet length analysis provided by Wireshark, as Figure 12 demonstrates. In the case where MQTT packets are preprocessed at the SmartNIC and 5% of values are being generated above the threshold, only 71 MQTT packets have been sent to the edge broker. Fifty-one of those packets are publish messages with an \"alarm\" topic, while the rest are messages that maintain the connection (only for responses). Additionally, 135 TCP messages are related to the MQTT conversation, in a period of about 628 s. A total sum of 9459 bytes was sent, and the mean throughput in that period was 15.06 bytes/s. Figure 13a presents the total number of transmitted packets (TCP packets associated to MQTT conversations) and Figure 13b shows the equivalent throughput value. As shown in this figure, preprocessing at the SmartNIC allows reducing throughput by about 90%. This is due to the fact that, when packets are preprocessed at the SmartNIC, only those packets with values above threshold must be further transmitted to the edge broker. Additionally, the publication communications between the SmartNIC and the edge broker do not require all the TCP connection messages, which allows reducing the throughput even more in this case, since the majority of TCP control packets are avoided. On the contrary, if no preprocessing is performed at the SmartNIC, all the MQTT messages must be forwarded to the cloud so that they are processed there in order to check if the value they convey is above the threshold or not. These results show that the packet processing system to detect high values is beneficial not only for response times, but for bandwidth usage reduction too. This resource usage decrease is a crucial point, due to the massive amount of sensors that can be deployed and their corresponding communications saturating the network. Besides, edge data processing centers are usually less powerful than cloud centers due to optimization of economic resources, which could result in a link or CPU saturation by the transmission of full sensor communication of a factory to the aforementioned edge broker. Figure 12: Screenshot of Wireshark packet length analysis of the experiment 3 MQTT communication.
## 4 Discussion  
Our proposal addresses an implementable control architecture that does not require knowledge of the complex plant but only conservative bounds, yet it guarantees point-to-point regulation for a critical task. The numerical results show that, considering parameters and specifications of commercial actuators, LEO downlink is feasible; moreover, there arises few discussions that the advantages of our proposal.  
First, no previous scheme have included LEO downlinking with a GS platform to cover the worskspace envelope from east horizon to west horizon. The two DoF pedestal robot, usually considered for satellite downlink, or tracking, clearly fails to convert such workspace [28]. Secondly, in addition, proposing model-based schemes yields a closed-loop system prone to any uncertainty on regressor or parameter, including from approximation theory schemes that neglects approximation errors, such as neural networks and fuzzy logic, or adaptive optimal control or predictive control.  
Furthermore, the computational load of any scheme that realies on the model or approximation of inverse dynamics certainly leads to unfeasible implementation with COTS. Finally, our proposal is indeed a path tracking controller, it computes the desired trajectory online in accordance to the real state of the GS, thus, taking action at each instant with respect to its state. Since it may be cumbersome to compute, we compared also the performance when the motion planner is precomputed (offline), which suggests a marginal difference with respect to online implementation of the motion planner.  
This suggests that the slow LEO time varying georeferenced trajectories leads to few differences, i.e., there is enough time in between updating data points for the adaptive PID controller to converge in the vicinity of the desired pose; thus, a negligible difference arises between the offline vs. online motion planner, which has not been reported previously, neither with a pedestal two DoF or GS six DoF robot.  
Notice that the physical parameters of a realistic design have been used in the simulations, including for friction, structure dimensions, materials, actuators and sensors and for a realizable sampling. It results in control torques within acceptable point-to-point precision and within limits of actuators; thus, a feasible implementation can be assessed. Ongoing work is now near final integration at the Mechatronic Laboratory of CIATEQ for this same design, using a industrial PC real time computer and the aforementioned actuators from that provider.
# Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area
## 2 Materials and Methods  
The research methodology consisted of four sequential steps, as illustrated in Figure 1. Firstly, the study focused on the Franciacorta area in Lombardy, Northern Italy, defining the geographical context. Secondly, satellite imagery was acquired from Google Earth Pro, georeferenced with ArcGIS, and segmented using eCognition to create homogeneous image regions. In the third step, these segmented images were validated and classified by a plant expert using QGISto ensure accurate identification of land cover types such as grasslands and vineyards. Lastly, the fourth step involved training semantic segmentation models U-Net, SegNet, and DeepLabV3--on smaller image patches derived from the large original images, with and without pre-trained backbones. Models were assessed based on performance metrics such as accuracy and the Jaccard Index, thereby completing a thorough approach to land cover analysis and classification.