esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Strain Transformation and Instrument Coupling
Substituting Equation (4) into Equation (3) yields  
\[g_{i}e_{i}=(C-V\frac{-\
u}{1-\
u})\varepsilon_{a}+Dcos2\varphi_{i}\varepsilon_ {d}+Hsin2\varphi_{i}\varepsilon_{s} \tag{5}\]  
\(\widetilde{C}\) is an apparent areal strain, which present the comprehensive result of areal and vertical strain, and is defined as the following:  
\[\widetilde{C}=\ \left[C-V\frac{-\
u}{1-\
u}\right] \tag{6}\]As Equation (6) denotes, areal strain will be reduced by vertical coupling effects, apparent areal strain may vanish or become negative when \(V>(\
u/(1-\
u))C\) in some location with a large vertical coupling effect [40]. If we assume the mechanical properties of rock are homogeneous and isotropic rock, \(H\) could be equal to \(D\). Equation (6) could be reformed:  
\[g_{i}e_{i}=\widetilde{C}\varepsilon_{a}+Dcos2\varphi_{i}\varepsilon_{d}+Dsin2 \varphi_{i}\varepsilon_{s} \tag{7}\]  
Additionally, Equation (7) could be easily generalized as the following:  
\[e_{i}=a_{i1}\varepsilon_{a}+a_{i2}\varepsilon_{d}+a_{i3}\varepsilon_{s} \tag{8}\]  
Equation (8) describes the general situation of coupling. In most cases, the linear strain \(e_{i}\) measured by the \(i\)th channel of strainmeter can be expressed with Equation (9):  
\[\left[ \begin{array}{c}e_{1}\\ e_{2}\\ \vdots\\ e_{i}\end{array} \right]=\left[ \begin{array}{ccc}a_{11}&a_{12}&a_{13}\\ a_{21}&a_{22}&a_{23}\\ \vdots&\vdots&\vdots\\ a_{i1}&a_{i2}&a_{i3}\end{array} \right]\left[ \begin{array}{c}\varepsilon_{a} \\ \varepsilon_{d}\\ \varepsilon_{s}\end{array} \right]=\left[A\right]_{3\times i}\left[ \begin{array} []{c}\varepsilon_{a}\\ \varepsilon_{d}\\ \varepsilon_{s}\end{array} \right] \tag{9}\]  
\([A]_{3\times i}\) is a coupling matrix that contains instrumental, angular, formation coupling and topographic effects comprehensively. The inverse matrix \([A]_{3\times i}^{-1}\) is the calibration matrix, which can transfer to remote strain from observed data channels. In the different constrain, the method to obtain the coupling matrix will be different. Since the significant responses of rainfall, air pressure, and river loading contribute in can be concluded as some kind of vertical strain, we treat the data calibration as in the isotropic rather than vertical condition in this study. We had a short discussion of difference between isotropic and vertical coupling in Section 6.2.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 4 Conclusions  
In this article, it was stated that an IRS receives a signal from another location and scatters it in a controllable way. It acts like a (semi-)passive large array. It might be considered as an advantage considering the capacity/data rate and power optimization, although channel estimation is a concern. In contrast, it was described that the LIS was first utilized as a large active antenna array, being similar to a massive MIMO but much bigger and beyond mMIMO. A LIS generates, transmits, and receives signals. The arrays are not only of high dimensions, but they are also made up of very small parts. These characteristics allow them to control electromagnetic waves over the surface almost continuously. In addition to the existing advantages of the IRS, the channel estimation issue has improved. In contrast, RS are a practical implementation of cell-free mMIMO, in which a network's user equipment shares the same time-frequency resources as a collection of multi-antenna APs.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 7 Experimental Results
### Simulation Environment  
To perform the experiments, we implemented environment entities, such as edge nodes, VNFs, SFCs, links, packets, users, and data sources, as described in the model presented in Section 3.1. Similar to [41; 42], we implemented our own simulation tool since other simulation frameworks do not support the granularity level (dynamic packet size) and do not offer support for new environment features, such as VNF sharing required for our experiments. Simulations were executed in a server equipped with an Intel(R) Xeon(R) Silver 2.20 GHz 48 cores processor and 500 GB of RAM. We used Python as a programming language to code our simulation, along with the simulation framework SimPy. The same programming stack was also used in the 5G simulation framework called 5 GPy [43].  
To define the edge environment entities' configuration, we considered a virtual scenario compatible with the 5G use case called _best effort_. In this 5G scenario, the resource requirements are more flexible and less restrictive in comparison with the other 5G 3 GPP scenarios [44]. This represents mixed applications with a variety of QoS requirements. Besides that, in the experiments, we consider that the edge nodes are arranged in a full mesh network topology, i.e., each edge node is directly connected to all the others.  
All the experiments were conducted using the parameters presented in Table 5. The CPU resource at the edge node and VNF type description is defined in terms of instructions per second (IPS). Each experiment was executed 10 times to increase the confidence interval, we  
Figure 3: Architecture components of our approach.  
adopted a 5% error tolerance. We analyzed the number of SLA violations in terms of packet delay, the number of accepted SFC requests, as well as the energy and resource consumption.  
Since the VNF placement problem is NP-hard, we created an ILP solver that implements the objective function defined in Equation (11) able to find feasible solutions only for small-scale edge systems. As the ILP solver presents poor scalability in terms of the number of nodes and SFC requests, we initially compare our proposed heuristic-based algorithm with the optimal solution in smaller scenarios.  
Besides that, we compared our approach with two other algorithms widely used as benchmarks in the literature, namely, greedy and random [17; 18; 28], which are described below. The energy parameters for edge nodes consumption follow the specifications defined in [45].  
* The **Greedy placement** first selected entities (edge nodes and links) with the higher resources available to compose the placement plan. This strategy tends to avoid selecting overloaded nodes and links.
* The **Random placement** randomly chooses a node among available nodes to create the new VNF instances.
###### Abstract  
This paper proposes a Duty-Cycle (DC) control method in order to improve the Packet Delivery Ratio (PDR) for IEEE 802.15.4-compliant heterogeneous Wireless Sensor Networks (WSNs). The proposed method controls the DC so that the buffer occupancy of sensor nodes is less than 1 and assigns DC to each sub-network (sub-network means a network consisting of a router node and its subordinate nodes). In order to use the appropriate DC of each sub-network to obtain the high PDR, this paper gives analytical expressions of the buffer occupancy. The simulation results show that the proposed method achieves a reasonable delay and energy consumption while maintaining high PDR.  
IEEE 802.15.4; duty-cycle; active period control; heterogeneous sensor network; tree topology +
Footnote †: journal: Applied Sciences  
**Citation**: [PERSON]; [PERSON].  
Duty Cycle Control Method Considering Buffer Occupancy for IEEE 802.15.4-Compliant Heterogeneous Wireless Sensor Network. _Appl. Sci._**2021**, _11_, 1362. https://doi/10.3090/app11041362  
https://doi/10.3390/app11041362
## 2 Materials and Methods
### Three \"High at Both Ends, Low in the Middle\" Functions  
Based on the problem definition and analysis, three functions that satisfy the \"high at both ends, low in the middle\" property and are symmetric around \(x=0.5\) are proposed. The first function is the tangent function, transformed by shifting and taking the absolute value (referred to as the tan function hereafter). Its functional expression is  
\[|\tan((x-0.5)\pi)| \tag{6}\]  
The graph of this function is shown in Figure 1.  
The second function is the secant function, shifted (referred to as the sec function hereafter). Its functional expression is  
\[\sec((x-0.5)\pi) \tag{7}\]  
The graph of this function is shown in Figure 2.  
The third function is a shifted rational function (referred to as the fractional function hereafter). Its functional expression is  
\[\frac{1}{x(1-x)} \tag{8}\]  
The graph of this function is shown in Figure 3.  
Figure 1: Graph of the function \(|\tan((x-0.5)\pi)|\).  
Figure 2: Graph of the function \(\sec((x-0.5)\pi)\).  
As shown, all three functions assign higher weights to base learners whose predicted scores are closer to 0 or 1, while those with predicted scores closer to 0.5 are given smaller weights. Compared to the other two functions, the tan function has a weight of 0 at \(x=0.5\) and carries a risk of division by zero. However, on the domain \(x\in[0,1]\), the derivative of this function is relatively large at every point, which leads to more pronounced differentiation of the outputs from different base learners. The sec function and the fractional function, on the other hand, have derivatives approaching 0 on the interval \(x\in[0.2,0.8]\), resulting in a smaller and more consistent weight for base learners whose predicted scores fall within this range. For \(x\in[0,0.2)\cup(0.8,1]\), the derivatives approach infinity, giving base learners in these regions a larger and more distinct weight.  
Each of the three functions has its advantages and disadvantages, but none of them are universally applicable to all datasets. Therefore, in the next section, a neural network-based weight generation function is proposed. By cleverly constructing the neural network and designing an appropriate loss function, this function can learn, in a data-driven manner, a weight generation function that is suitable for a specific dataset.
## 5 Case Study  
The computational experiments utilized TensorFlow v2.18.0 (Google LLC, Mountain View, CA, USA) under Python 3.8 on hardware comprising an Intel Core i7-10700F CPU (Intel Corporation, Santa Clara, CA, USA) and AMD Radeon R5 430 GPU (Advanced Micro Devices, Inc., Santa Clara, CA, USA). The current implementation uses a minimalist hardware setup and can support the real-time control of a 100-MW wind farm cluster with latency well below the operational threshold. Higher-end hardware will allow for larger batch processing.  
The data in this paper comes from the actual historical data of a 100-MW wind farm cluster, which is taken from the operation data of the first half of 2018 for 33 days, with a total of 3168 assessment periods, a sampling period of 2 s, and a total of 43,200 sampling points for the whole day. Among them, a total of 2208 assessment periods in 23 days are used as training sets, and a total of 960 assessment periods in 10 days are used as verification sets. The data used for AROCS and the energy storage parameters are referred to in the literature [11], and the coefficients of PI control are set dynamically according to the control deviation.
# Summary of Lunar Constellation Navigation and Orbit Determination Technology
## 5 Outlook on Key Technologies for Lunar Constellation Navigation and Orbit Determination
### Multi-Source Navigation Information Fusion and System Optimization  
In future exploration missions, in order to meet navigation needs, establishing a lunar navigation constellation is the most reliable way for lunar users to obtain location services. Studying the integrated navigation performance including pulsars, star sensors, lunar surface beacons and other information sources is also the focus of the next step of research. For example, the high-precision information obtained by the star sensor is used to calibrate the navigation measurement information in some phases, and the distance and speed information provided by radio navigation are combined to improve the accuracy of the rendezvous and docking section. To combine multiple navigation methods, it is critical to design efficient fusion algorithms for multi-source information based on rich navigation measurement information.  
In view of engineering constraints such as the nonlinear and highly time-varying orbital dynamic environment of lunar navigation satellites and unknown factors, the optimization selection criteria for inter-satellite measurement observations of lunar navigation constellations are proposed, and a navigation constellation optimization plan is proposed. By combining satellite ground measurement, astronomical measurement and other information, research is conducted on the quality evaluation and failure monitoring methods of combined autonomous navigation information, the unified method of spatiotemporal reference for multi-source navigation information and the fusion processing method of multi-source information. The optimal combined autonomous navigation scheme is constructed to satisfy the high-precision, real-time and highly reliable autonomous navigation requirements of lunar navigation satellites.
# EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks
## 2 Related Work
In [33], the authors discussed the drawback of this method: this security bit assumption is unrealistic. This technique cannot exactly determine the blackhole nodes. In [34], a DAPV mechanism is introduced to find single and multiple malicious nodes and also the nodes that do not behave normally. DAPV relies on two primary steps, including the log information of peers and the Merkle Hash Tree to check these logs without disclosing the privacy of the checked nodes. The authors of [35] developed a Delay Tolerant Network (DTN) to deal effectively with connectivity and latency issues in wireless networks. DTN is prone to attacks such as blackhole and greyhole due to the limited connectivity it has. In these attacks, malevolent nodes purposely drop packets. Statistical-based Detection of Blackhole and Greyhole attackers (SDBG) was suggested to address these types of attackers. Nodes can evaluate forwarding behaviors of other nodes based on the encounter record histories exchanged between them. This solution can work with various dropping probabilities and different number of attackers but still is not very efficient, as it consumes a lot of energy to exchange encounter histories and evaluate attackers. On the other hand, path selection is a challenging aspect in heavy-load networks, where data traffic congestion occurs frequently. Various algorithms were proposed to use the channels' capacity to alleviate the number of data packets going through nodes to avoid having congestion. In [36], the authors suggested a method to guarantee a fair power distribution in wireless network channels for real-time applications. Some other methods considered network resources such as RTT and the bandwidth to control the network traffic transmission over links, such as in [37; 38; 39]. In [39], the authors have developed a routing protocol that selects the route based on residual bandwidth at the intermediate nodes and the destination. The main concern of this protocol is its extra overhead incurred. Some other techniques proposed using neural networks [40] and fuzzy control [41]. These approaches are mainly time- and energy-consuming methods. The authors of [42] have proposed a protocol to pass the data through reliable links with less traffic. This method is based on node connectivity, where it won't guarantee high performance when node speed increases. There are several factors to evaluate a security routing protocol against the blackhole threat detection, particularly in MANET nodes that use battery-based power. It is necessary to make a compromise between these factors to propose an efficient protocol. These factors include throughput, end-to-end delay, energy consumption, and routing overhead. In this regard, we propose our algorithm, which outperforms the performance of other protocols.
## 3 OTFS Channel Estimation
### Proposed P-IPIC Algorithm
After that, during the _refinementphase_ the paths are refined as in the DDIPIC algorithm with the only difference in using the cost function in Equation (19) with a Tikhonov regularization, also known as ridge-regression, and in performing channel gain refinement as in line 15. As discussed above, during this phase some paths may be discarded. Therefore, the refinement procedure provides a number of estimated paths equal to \(\hat{P}\leq\hat{P}\). The number of paths detected during the _search phase_ cannot be determined a priori but it is reasonable to assume that, if the IPI is not too large and the channel is made of a sufficiently high number of propagation paths, \(\hat{P}\approx\hat{P}\). * _Regularization (line 15)_: Another difference from the original version of the IPI cancellation algorithm is that the ordinary least squares (OLS) estimate of channel gains in Equation (18) is replaced by a regularized least squares (RLS) estimate to make the matrix inversion more stable. This is due to the fact that, due to residual IPI, some paths are detected multiple times, and as a result, the CDDPM matrix may have some almost collinear columns. Therefore, the RLS estimate of the channel gains vector is given by \[\hat{\mathbf{g}}=\left(\mathbf{R}^{H}(\hat{\tau},\hat{\
u})\mathbf{R}(\hat{ \tau},\hat{\
u})+N_{0}\mathbf{I}\right)^{-1}\mathbf{R}^{H}(\hat{\tau},\hat{\
u })\mathbf{y}\] (22) where \(\mathbf{I}\) is the identity matrix.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 5 Discussion
### Discriminating Capability of ASTER and OLI  
In order to discriminate alteration mineral associations, the Landsat OLI sensor has two SWIR bands [105; 106; 107; 108; 109]. Clay, sulfate, and carbonate types cannot be adequately distinguished by OLI SWIR bands [110]. In contrast, the ASTER instrument has five temperature bands and six SWIR bands, which can enhance the extraction of lithologic and mineral characteristics. The application of RS techniques for lithologic mapping and mineral prospecting is now the subject of many publications. The aforementioned classification study indicates that ASTER data are a more useful tool for lithologic mapping than OLI. The relative strength of OLI and ASTER data for lithological information extraction shows that the total accuracy of the mapping results obtained from the ASTER dataset in this investigation is higher than the values obtained from the OLI image (Tables 1-4). This is because ASTER has a higher spatial resolution in the SWIR range than OLI, so the classification accuracy of lithological units obtained from the ASTER dataset is higher than that for the OLI dataset. Furthermore, because of the significant SWIR absorption properties of silicate minerals in ASTER bandpasses, ASTER is superior to OLI in the classification of different lithological units [111].
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 4 Results  
### Datesets and Settings  
#### 4.1.1 Datasets  
In the past few years, many MOT datasets have been proposed and applied. MOT Challenge is the most commonly used and most convincing benchmark for MOT, and MOT16 [25] includes 14 challenge video sequences (7 train, 7 test) from pedestrian videos captured with moving or stationary cameras. MOT17 Det is based on MOT16 video sequences, using new and more accurate ground truth. MOT20 [26] is the Pedestrian Detection Challenge proposed by [PERSON] and his team in 2020, including eight challenging video sequences (4 train, 4 test). Compared to previous challenges, MOT20 has more crowded pedestrians and is more difficult to track. This paper evaluates more commonly used datasets such as MOT17 Det [25] and MOT20 [26].
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 5 Experiments
### Results
#### 5.3.3 Results for Each Case
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Parameter** & **Value** \\ \hline GP & Filter type & Standardized training data \\ (WEKA 3) & Level of Gaussian noise & \(1.0\) \\  & Decimal places & \(2\) \\ \hline  & Activation function & Approximate sigmoid \\  & Loss function & Square error \\ MLP & Decimal places & \(2\) \\ (WEKA 3) & Number of hidden units & \(1\) \\  & Ridge & \(0.01\) \\  & Tolerance & \(1.0\times 10^{-6}\) \\ \hline  & Decimal places & \(2\) \\ RBFN & Number of basis functions & \(2\) \\ (WEKA 3) & Ridge & \(2\) \\  & Scale optimization & Use scale per unit \\  & Tolerance & \(1.0\times 10^{-6}\) \\ \hline  & Complexity constant & \(3.0\) \\ SVR & Filter type & Standardized training data \\ (WEKA 3) & Kernel & Polynomial kernel \\  & Decimal places & \(2\) \\ \hline  & Loss function & Mean square error \\ DNN & Activation function & ReLU \\ (PyTorch) & Number of epochs & \(1500\) \\  & Number of hidden layers & \(8\) \\  & Number of units per a layer & \(256\) \\ \hline   RNN & Loss function & Mean square error \\ (PyTorch) & Number of hidden units & \(256\) \\ (PyTorch) & Number of epochs & \(500\) \\ \hline  & & & \\ \end{tabular}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Evaluation**} & \multicolumn{4}{c}{**Case**} \\ \cline{3-5}  & & **1** & **5** & **6** & **7** \\ \hline DE & MAE & 0.0743 & 0.0286 & 0.0906 & 0.0417 \\  & NCLS & 0.9308 & 0.7377 & 0.7769 & 0.9172 \\  & MAE & 0.1238 & 0.0318 & 0.1050 & 0.0758 \\ PSO & NCLS & 0.8724 & 0.7183 & 0.7557 & 0.8375 \\  & MAE & 0.0743 & 0.0286 & 0.0909 & 0.0418 \\  & NCLS & 0.9308 & 0.7378 & 0.7763 & 0.9170 \\  & MAE & 0.0945 & 0.0290 & 0.0974 & 0.0360 \\  & NCLS & 0.9113 & 0.7431 & 0.7583 & 0.9282 \\ MLP & MAE & 0.0957 & 0.0285 & 0.1012 & 0.0320 \\  & NCLS & 0.9030 & 0.7541 & 0.7474 & 0.9353 \\  & MAE & 0.1064 & 0.0278 & 0.0986 & 0.0297 \\  & NCLS & 0.8992 & 0.7626 & 0.7508 & 0.9399 \\  & MAE & 0.0700 & 0.0301 & 0.0939 & 0.0394 \\ SVR & NCLS & 0.9355 & 0.7402 & 0.7711 & 0.9204 \\  & MAE & 0.1557 & 0.0192 & 0.1305 & 0.0571 \\ DNN & NCLS & 0.8493 & 0.8383 & 0.6879 & 0.8848 \\  & MAE & 0.1369 & 0.0270 & 0.1335 & 0.0494 \\ RNN & NCLS & 0.8555 & 0.7699 & 0.6809 & 0.8910 \\  & MAE & 0.0744 & 0.0295 & 0.1153 & 0.0301 \\ LSTM & NCLS & 0.9201 & 0.7413 & 0.7075 & 0.9408 \\  & MAE & 0.1352 & 0.1238 & 0.0656 & 0.0434 \\ MOHID & NCLS & 0.8633 & 0.7134 & 0.8480 & 0.9229 \\ \hline \hline \end{tabular} The lower the MAE values, the better. The higher the NCLS values, the better.  
\end{table} 
Table 5: Parameter values for machine learning (ML) methods.