esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In the recent past, both industrial and academic researchers have driven the usually called fourth industrial revolution (I4.0) to respond to market demand for personalized and customized products [1]. This movement is empowered in Europe by the German Electrical and Electronic Manufacturers' Association (ZVEI). Concepts such as interoperability, cyber-physical systems, cloud computing, or blockchain are now usual when we are talking about Information and Communication Technologies (ICT) applied to manufacturing, both when we talk about the factory shop floor or the supply chains and even when the focus is the after-sales support. These concepts trigger new business models targeting decentralized manufacturing, augmenting manufacturing efficiency, and upholding operational excellence within environmental care.  
This work aims to present a software architecture capable of providing a fully integrated cloud-based ecosystem, merging the best practices and lessons learned from the past, and linking existing technologies of existing tools with emergent technologies such as blockchain, artificial intelligence, and big data analytics, among others. As part of these efforts, a blockchain architecture will also be developed to support decentralization and increase trust among the different partners providing traceability over operational boundaries and seamless interoperability regardless of the interfaces or tools used.  
This work is focused and oriented to industrial use cases, trying to cover as much as possible of the European industrial force.  
Besides the architecture, this work presents an integrated cloud platform to demonstrate the concept, from end to end, starting with the client creating a purchase order passing through the Enterprise Eesource Planning (ERP), to the Manufacturing Execution System (MES), until the delivery of the purchased product. The data models used for this integrated cloud platform will be described to demonstrate the data flow, synchronization, and interoperability between all software parts and systems and the hardware used on the shop floor. The integration of blockchain technology to validate some of the transactions between partners and to assure authenticity will also be depicted in this work.
## 7 Performance Evaluation of Multicast R-GOOSE over Public WAN
### Time Synchronization
This message is transmitted over the public WAN and received by the Linux PC in which the Wireshark application records the received R-GOOSE messages and the time they were recorded. The recorded time is also based on GPS time since the Wireshark application gets its time value from the Linux PC system time, which has been synchronized to the GPS clock via the Network Time Server. Thus, in each R-GOOSE message, communication latency can be measured by calculating the time difference between the arrival time (the recorded time in Wireshark) and the sending time, i.e., the timestamp value (t) of the message. The sending and arrival times, in Wireshark, are shown in Figure 21.  
Figure 20: GPS time synchronization of sender (Raspberry Pi) and receiver (Linux PC).
# An Efficient Neural Network for Shape from Focus with Weight Passing Method
## 3 Neural Network for SFF
### Proposed Model
The derivative can be implemented as  
\[\frac{\partial I\left(x+j,y+k,z_{(x,y)}^{(n)}\right)}{\partial z_{(x,y)}^{(n )}}=\frac{I_{+}-I_{-}}{2h} \tag{13}\]  
where h is a step size, \(I_{+}=I\left(x+j,y+k,z_{(x,y)}^{(n)}+h\right)\), and \(I_{-}=I\left(x+j,y+k,z_{(x,y)}^{(n)}-h\right)\). The remaining term \(\left(\partial z_{(x,y)}^{(n)}/\partial\mathbf{W}^{(n)}\right)\), which is surface gradient, can be acquired using backpropagation algorithm [9]. Here, the weight \(\mathbf{W}^{(n)}\) includes \(\mathbf{u}^{(n)}\), \(\mathbf{v}^{(n)}\), and \(w_{b}^{(n)}\), and their derivatives are expressed as  
\[\begin{array}{c}\frac{\partial z_{(x,y)}^{(n)}}{\partial\mathbf{u}^{(n)}}=z _{(x,y)}^{(n)}\cdot\left(1-z_{(x,y)}^{(n)}\right)\cdot\mathbf{v}^{(n)}\\ \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad \quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quadNote that \(G^{(n)}\) is pre-calculable value before network learning. Essentially, it is dependent on global indexes \((x,y)\) and the network output \(z^{(n)}_{(x,y)}\), and thus it is dependent on the network learning iteration \((n)\). However, a possible set of \(G^{(n)}\) can be calculated in advance of network learning, because the number of elements of the possible set is same to the total frame number \(I_{D}\) of image sequence. Therefore, it gives additional time efficiency. After updating \(\textbf{W}^{(n+1)}\) as Equations (17)-(19), the present iteration \((n)\) is ended, and it follows the next iteration \((n+1)\). In this manner, all \(z^{(n)}_{(x,y)}\) at each iteration are updated and the final depth map for \((x,y)\) can be acquired as  
\[d(x,y)=z^{(N)}_{(x,y)} \tag{21}\]  
And the final depth map of NN.Planar at all points is described as  
\[F_{NN.Planar}=\{d(x,y)|x\in[1,\ldots,I_{W}],y\in[1,\ldots,I_{H}]\} \tag{22}\]  
The proposed NN.Planar looks an instance of the NN.FIS as proposed in [10]. Consider a particular instance of NN.FIS by restricting the input vector \(\textbf{i}=\left[j\ k\ 1\right]^{T}\) with \(j=0\), \(k=0\). In this case, it needs to update not only weights connected from the bias 1 but also weights connected from \(j\) and \(k\). Weights update, related to \(j\) and \(k\), for focus measure over a planar surface is redundant in NN.FIS. Therefore, the proposed NN.Planar is not an instance of the NN.FIS. Moreover, in the NN.FIS, neighboring points are taken into account to compute the complex shape of FIS. Whereas the proposed NN.Planar is based on the assumption of planar surface. In other words, the surface within the local window is assumed to be a planar surface. It may seem to be similar to the piece-wise constant approximation. However, it is not constant as it is modelled through the functions (7) and (9) to provide accurate depth.
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 5 Experiments
### Analysis of Properties in SCSA  
**SCSA with and without RICA:** In our SCSA, the RICA with whitening played a foundation and optimization role in the experiments. Therefore, we conducted additional experiments to evaluate its optimizing ability. Table 4 shows the results for the SCSA with and without RICA for all three datasets. From the results, it can be observed that the proposed SCSA with RICA outperformed SCSA without RICA for all three datasets, indicating that the RICA can pre-process all the image datasets and make the input less redundant, which is obviously helpful for more transferable and discriminative feature learning. With less redundant input data, the cross-domain and invariant feature representations can improve performance in domain adaptation.  
**Computational Cost:** The time complexity of a stacked sparse auto-encoder is \(\text{O}(h_{1}+h_{2})\), where \(h_{1}\) and \(h_{2}\) are the hidden unit numbers of two layers, respectively. For our method, we took the labeled information into consideration, given \(c\) as the number of classes; the time complexity is \(\text{O}(h_{1}+h_{2}+h_{2}\bullet c)=\text{O}(h_{1}+h_{2}\bullet(1+c))=\text{O }(h_{1}+h_{2})\). For the convolution and pooling kernels, the time complexity is \(\text{O}(m\times m\times p\times d)=\text{O}\big{(}m^{2}\times p^{2}\times d \big{)}\), where \(m\times m\) and \(p\times p\times d\) represent the size of the input and the patches, respectively. The time complexity of our SCSA is \(\text{O}\big{(}m^{2}\times p^{2}\times d+h_{1}+h_{2}\big{)}\).
## 2 Materials and Methods  
### Data Selection Criterion of NmF2/hmF2 Observed by FY3C/GNOS and COSMIC  
A geomagnetic storm is a global disturbance of the earth's magnetic field, and the geomagnetic index Kp is usually used to characterize the magnitude of the geomagnetic storm, which can be obtained every 3 h from mean value of K-indexes of 13 geomagnetic stations in the global network [52]. Generally speaking, Kp less than 4 and Kp greater than 5 corresponds to the quiet period and storm period, respectively. From the Kp indexes obtained from ISGI ([[http://isgi.unistra.fr/](http://isgi.unistra.fr/)]([http://isgi.unistra.fr/](http://isgi.unistra.fr/))) in Figure 1, we can see that the quiet period accounted for 95.67%, thus we discarded 4.33% of the NmF2/hmF2 in the storm period and only chose the data in quiet period for further use. The IRO EDP data of FY3C/GNOS provided by National Satellite Meteorological Center (NSMC) can be downloaded from [[http://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx](http://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx)]([http://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx](http://satellite.nsmc.org.cn/PortalSite/Data/Satellite.aspx)) and the EDP data of COSMIC processed by COSMIC Data Analysis and Archive Center (CDAAC) can be accessed from [[https://cdaac-www.cosmic.ucar.edu/cdaac/products.html](https://cdaac-www.cosmic.ucar.edu/cdaac/products.html)]([https://cdaac-www.cosmic.ucar.edu/cdaac/products.html](https://cdaac-www.cosmic.ucar.edu/cdaac/products.html)).  
**In order to evaluate the effect of scintillation caused by ionospheric irregularities on EDP, [PERSON] et al. [53]** defined the mean deviation (MD) value:  
\[MD=\sum_{i}\frac{\left|\frac{n_{ei}-\overline{n_{ei}}}{N\overline{n_{ei}}}\right|}{ \tag{1}\]  
where \(N\) is the number of electron density sampling points in an EDP, \(n_{ei}\) is the electron density value at the \(i\)-th sampling point while \(\overline{n_{ei}}\) is the \(n_{ei}\) processed by 9-point moving average filtering method, namely the electron density background value at the \(i\)-th sampling point. The statistics show that the \(MD\)s are mainly concentrated between 0 and 0.5. A negative \(MD\) indicates that the EDP is questionable because at least one of the electron density background values is negative. Therefore, when \(MD\) is less than 0 or  
Figure 1: Kp variations during 2016.035–2017.035. The red line represents Kp = 4, namely the critical Kp value of a magnetic storm.  
greater than 0.5, the corresponding EDP is marked as unqualified data and abandoned. In addition, [PERSON] [54] analyzed the hmF2 of COSMIC in low latitudes from 2008 to 2011 and found that the hmF2 ranged from 200 to 400 km. Considering that the electron density profiles used in this work are spread all over the world, we set the hmF2 interval to 200-450 km and discarded the data out of range.
## 4 Results  
### Evaluation Experiments Using Testbed  
The system proposed in this research can be applied to a wide range of industrial machines used in manufacturing sites of SMEs by flexibly changing the configuration of the sensor devices. Here, evaluation experiments are carried out on the looseness detection of bolts fixing the support side of ball screws, which are used in industrial machines to transport products and for precise positioning.  
The testbed used in the experiment is shown in Figure 7.  
The testbed consists of four elements: ball screw, servo motor, servo amplifier and stage. A 1 [kg] weight is placed on the stage on top of a plastic case, thus reproducing the product to be transported on the production floor. The testbed was set to move in a reciprocating motion between the left and right ends. This reciprocating motion consists of four movements: starting from the leftmost position of the stage, uniform motion from left to right, stopping at the rightmost position, uniform motion from right to left and stopping at the leftmost position. Here, the stopping time at the left end is longer than the stopping time at the right end, so the stopping time between reciprocating motions is longer. The testbed repeats this reciprocating motion at intervals of approximately 5.5 [s] per round trip. In the following, this one round trip is treated as one cycle in the manufacturing process.  
The flow of this evaluation experiment is shown next.  
1. Data collection using SD card
2. Cutting out to cyclic waveforms
3. Determination of edge processing
4. Wireless transmission of features using edge processing  
First, data are collected using SD cards to measure the data required to determine the edge processing to be performed on the sensor devices of the proposed system. At this stage, before the edge processing is determined, the appropriate sampling rate for measuring the data for edge processing is unknown. Therefore, the data are measured at the maximum sampling rate that can be measured by the sensor device. Next, the data recorded on the SD card are cut into cycle waveforms. The cycle waveforms are then analysed between different bolt tightening torque conditions to derive features that show differences. Finally, the cycle extraction and feature calculation processes obtained in the experiments up to this point are implemented in the sensor device as edge processing, and the feature values collected using LPWA communication are visualised in a web application.  
Here, the experimental conditions of this evaluation experiment are described. In this evaluation experiment, the bolts on the support side of the ball screw are tightened appropriately to 11 [N] and the bolts are loosened to 1 [N]. Data measurements were carried out at two different tightening torques. A torque spanner is used to adjust to the respective tightening torques.
## 4 Results and Discussions
### Parameter Importance Analysis  
The effects of various structural and working parameters on the collection efficiency of hydrodynamic collectors are analyzed in Sections 4.2 and 4.3. The parameters that have a greater impact on the performance need to be prioritized in the design. In this study, the Variable Importance in the Projection (VIP) index [31] is utilized to quantitatively evaluate the extent to which each structural and working parameter affected the collection efficiency. After normalizing the numerical simulation results in Sections 4.2 and 4.3, the calculated VIP index was determined, as shown in Figure 26. Figure 26 clearly shows that the collection height \(H\) had the greatest effect on the collection efficiency, and it was followed by the slant angle of the acquisition nozzle \(\beta\). The remaining parameters, such as the center distance between the acquisition confluence tubes \(L\), acquisition flow rate \(Q_{a}\) and transmission flow rate \(Q_{t}\), had relatively less influence. Therefore, when designing and developing a hydraulic collector, the focus needs to be on the \(H\) and \(\beta\). In the hydraulic collector in this study, the terrain follower shown in Figure 3 was adopted for dynamic adaptive adjustment so as to ensure that the collection height could be kept near the optimal value during the walking collection process.
## 5 Problem Formulation  
In this study, an optimization problem in D2D networks is investigated, specifically concentrating on joint mode selection, channel assignment, and power allocation optimization issues. In our proposed system, D2D users can choose among the available three D2D modes, including DM, RM, or LM based on maximum SINR. Moreover, a network that has been completely loaded is regarded as having no dedicated channels for D2D pairs to utilize. Moreover, the optimum power level can be obtained from the range \(P_{min}\) to \(P_{max}\). This paper aims to optimize the sum data rate, SE, and EE of the proposed D2 Dcommunications scheme while guaranteeing the QoS demands for both D2D pairs and CUs. The following is the formulation of the optimization problem:  
\[\underset{\mathcal{A},II,P}{max}\sum_{n=1}^{N}\sum_{k=1}^{K}R_{n,k}(t),SE_{n,k}(t ),\text{ and }EE_{n,k}(t), \tag{11}\]  
s.t.  
\[\sum_{n=1}^{N}\sum_{k=1}^{K}R_{n,k}(t)\geq\ R_{min}^{th}(t),\hskip 28.452756 pt \forall k\in 1\ldots K, \tag{12a}\] \[\sum_{n}\mathcal{M}=1\hskip 56.905512 ptn\ \in N, \tag{12b}\]  
\[\sum_{n=1}^{N}\sum_{k=1}^{K}\Pi_{n,k}^{1},\hskip 56.905512 pt\Pi_{n,k}^{2}, \hskip 56.905512 pt\text{or}\hskip 56.905512 pt\Pi_{n,k}^{3}\quad\leq\quad K \hskip 56.905512 pt\forall k\in 1\ldots K, \tag{12c}\] \[\sum_{m}\Pi_{m}=1\hskip 56.905512 ptm\ \in UEs, \tag{12d}\]  
\[\sum_{n=1}^{N}\Pi_{k,n}\leq\ 3,\hskip 56.905512 pt\forall n\in N, \tag{12e}\] \[P_{min}\leq P_{n}\ \leq P_{max}\,\hskip 28.452756 pt\forall N,\quad RU, \tag{12f}\]  
\[P_{C},\ P_{Z}=P_{max}, \tag{12g}\]  
Constraint (12a) specifies the minimal data rate for the \(nth\) D2D pair in shared sub-channel \(k\). Constraint (12b) denotes that each D2D pair \(n\) chooses one mode among the D2D modes including DM, RM, or LM. Constraint (12c) indicates that the binary mode sub-channel indicator matrix for each D2D including DM, RM, and LM is equivalent to or less than the total number of sub-channels \(k\). Moreover, the constraint (12d) indicates that every cellular user \(m\) utilizes a distinct sub-channel \(k\). The constraint (12e) indicates that each sub-channel may be utilized a maximum of three times. The constraints (12f) and (12g) denote that \(T_{n}\), CUs, and gNB utilize specific transmission power.  
To sufficiently address the optimization problem expressed in (11), it should be divided into two sub-issues: joint mode selection and channel assignment, as well as power management. Since it is an MINLP problem, the optimization problem is NP-hard and involves computational difficulties.
## 4 CSAMT Experiment in the Jinding Lead-Zinc Deposit
### CSAMT Inversion  
Because the direction of the survey lines is perpendicular to the trend of the ore body, we can use two-dimensional continuous medium inversion software to invert CSAMT data. The principle of continuous medium inversion is based on the assumption of a continuous variation in electrical properties in the subsurface. It discretizes the subsurface space using numerical methods and adjusts the resistivity values of each unit to achieve the best fit between the forward electromagnetic field data and the observed data. The method is closer to the real subsurface conditions and is suitable for exploration in complex geological settings, providing higher-precision information on the subsurface electrical structure [30,31].  
Based on the statistics of the work area, the frequency of measurement points entering the near zone is around 10 Hz, and the surface resistivity ranges between 100 and 200 ohm per meter. According to the skin depth formula calculation, the exploration depth is approximately 1.5 km, which meets the exploration requirements. Therefore, we selected a total of 40 frequency points in the 8192-10 Hz frequency band for inversion. The initial inversion model is the result of one-dimensional Bostick inversion. After 34 iterations of inversion, the data misfit is 2.7.
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 4 Results
### Divisioning of Dataset  
Since this paper employs supervised learning, the training data must be labeled so that they can be used to train the algorithms. The set of non-overlapping photos used as testing data is used to gauge how reliable the classifier's predictions are. This batch of data has never been seen before. It is divided into three different scenarios. The findings in Table 4 demonstrate that there are only minor performance variations among all ratios of data splitting between training and testing, achieving the highest accuracy. The batch size and learning rate are both fixed at 16, while all other hyperparameters are set to 0.001.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Optimizers & Adam, SGD \\ Pooling Layers & Average Pooling \\ Batch Size & 16 \\ Activation & ReLu \\ Learning rates & 0.01 and 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameters for the ship detection model.  
Figure 6: Comparison between Adam and SGD optimizer of ship detection.
## 3 Results
### Centrality Indices
#### 3.2.2 Local Centrality Results  
As shown in Figure 10, the hot spots and cold spots of local centrality were identified by Getis-Ord Gi* statistics [32]. These revealed the following:  
1. The hot spots of local reach (CR60, CR120) spread from the central plain to the north and south edge of the county border. The area around Xizhuang Village, which is along Baoding-Anxin Provincial Highway, is always a hot spot, while Liulizhuang Town is always a cold spot.
2. The high values of local betweenness (CB60, CB120) mostly belonged to semi-waterside villages in 1964. In 1996, however, the CB values of semi-waterside villages decreased. Furthermore, in 2008, in the western region, the territory of Tongkou Town and Lao-hetou Town boasted high CB values, and they underwent a shift from a low value concentration of local intermediary centrality in 1964 to a high value concentration in 2008. This was triggered by the construction of Baoding-Anxin Provincial Highway and Gaoyang-Baoding Provincial Highway. The phenomena reflected by CB and CR have something in common, as both reflect the negative effect of water on the improvement of land transportation conditions. Villages near the water in Liulizhuang Town and Zhaili Town are always cold spots, with less accessible range and intermediary. In contrast, major highways encourage transportation to nearby villages. The area around Xizhuang Village along the Baoding-Anxin Provincial Highway has increasingly high values for both accessibility and intermediacy.
3. Almost all of the villages with high local closeness (CC60, CC120) are in Liulizhuang Town, where the villages form a natural group with large external connection impedance and strong internal connection due to the presence of water bodies.
4. High values of local gravity (CG60, CG120) have a clear correlation with water, as the presence of water bodies leads to a local agglomeration of nearby villages, unlike  
Figure 8: Global gravity centrality (GG) of villages in (a) 1964, (b) 1996, and (c) 2008.  
Figure 9: Global straightness centrality (GS) of villages in (a) 1964, (b) 1996, and (c) 2008.  
the dispersed distribution on the plains. These villages exert strong interaction forces on each other because of the short distances between them. This implies that semiburaterside villages that are close to a water body have the potential to affect the development of surrounding villages through their strong interaction force.
* Local straightness centrality (CS) is most strongly related to geographical location. The results are similar across different years.  
Figure 10: Cont.