esa-sceva/satcom-chunk-collection huggingface dataset:
# Approximate Sensory Data Collection: A Survey
## 2 Model-Based Approximate Data Collection Algorithms
### The Algorithms
In [13], a stochastic model, which is based on the conditional probability and priori distribution, is proposed to estimate the vehicle speed trajectories during data collection in vehicle sensor networks. On condition of maximizing the likelihood function, the optimal activity sequence was determined, and then a detailed vehicle speed trajectory would be reconstructed accordingly. Since much trajectory data is estimated instead of retrieving it from the sensor nodes, many energy and transmission costs are saved. Finally, the authors applied this algorithm to a large-scale vehicle dataset and verified the high performance. Such a model only serves for the regular trajectory prediction application, and not applicable for other applications such as abnormal event detection._etc._ The works in [14; 15] also have introduced the Traffic Estimation and Prediction Systems (TrEPS) in VANET, which generally provide the predictive information needed for proactive traffic control and traveler schedule. Such system enhance the performance of the tradition transportation systems, and their prediction models are also useful to reduce amount of sensory data during data collection in VANET, however, they has the similar problem with the one in [13]. An adaptive data collection problem is addressed by [16] in wireless body sensor networks (WBSNs). In traditional WBSNs, each biosensor node collects data and sends them to the coordination in a periodic manner. Therefore, a huge amount of data is collected, which brings a huge burden for transmission and processing. Due to such motivation, the authors proposed a model to reduce the amount of data during data collection on the condition that the integrity of the sensory data is guaranteed. According to their model, each sensory value is scored based on an early warning score system, and the newly coming sensory values won't be collected if it has the same score as the previous one. Based on such a model, the data fusion method is also proposed using a decision matrix and fuzzy set theory. The algorithm in [16] largely reduced the transmission costs during data collection. However, the proposed model seems a little simple so that such a data collection method is only suitable for emergence detection application. In [17], the authors surveyed the hierarchical data gathering protocols in WSNs, they have discussed energy and communication overhead for each category of routing protocol with respect to design trade-offs, and also points out the advantages and disadvantages of each technique as well. However, it mainly focused on the exact data gathering protocols and not the approximate ones.
## 3 Results and Analysis
### Shear Test of Unfilled Soil-Concrete Interface
Figure 10 shows the variations of the internal friction angle and cohesion with the moisture content. With the increase in the moisture content, the internal friction angle of the soil-concrete interface showed a decreasing trend. The friction angles for the silty sand and the medium-coarse sand interfaces were in the ranges of 29-32\({}^{\circ}\) and 35-38\({}^{\circ}\), respectively, and the friction angle was not greatly affected by the moisture content. For the clay and sandstone, the friction angles decreased rapidly with the increase in the moisture content, in the ranges of 14-35\({}^{\circ}\) and 8-34\({}^{\circ}\), respectively. Moreover, the friction angle was significantly affected by the moisture contents of the clay and sandstone. Furthermore, the cohesion of the different soil-concrete interfaces showed different variation trends with the moisture content. The cohesion of silty sand and medium-coarse sand decreased with the increase in the moisture content, although the magnitude of the change was only about 2 kPa. As the moisture content increased, the cohesion of the clayey soil and the sandstone first increased and then decreased, and the moisture content corresponding to the maximum cohesion was near the plastic limit of the soil. This was because the adsorption of water in the soil gradually increased the cohesion. When the moisture content reached the plastic limit, the water film thickness between the soil particles increased, and the effect of the water pressure became greater than the adsorption effect. Thus, the co  
Figure 8: Variation of interfacial shear strength with moisture content: (**a**) silty sand, (**b**) medium-coarse sand, (**c**) clay, and (**d**) sandstone.
# Underwater Object Detection Method Based on Improved Faster RCNN
## 3 Proposed Method
### Positive and Negative Sample Imbalance Improvement  
In the process of training the model by the Faster RCNN, a large number of the bounding boxes will be generated when the image feature information passes through the RPN network structure. In the Faster RCNN, the value of the IOU is used to determine the positive and negative attributes of the bounding box samples; generally the bounding box with the IOU value greater than 0.7 is a positive sample, while the bounding box with the IOU value less than 0.3 is a negative sample. In these large numbers of bounding boxes there is often a serious imbalance between the number of the positive and negative samples, and in general the number of negative samples is much higher than the positive samples. Among the samples of these bounding boxes, there are often some difficult negative samples, and the difficult negative samples are samples that are difficult to identify and distinguish with the model. Due to the imbalance of the positive and negative samples and the existence of the difficult samples, the detection accuracy of the training model is affected.  
The OHEM is a mining algorithm for hard-to-detect samples, which is often used in image recognition and image detection tasks. In the two-stage object detection model, the OHEM generally acts between the bounding box generation module and the bounding box classification and the regression module. The OHEM algorithm usually selects difficult negative samples with complexity and high loss for training. Under limited conditions, the input positive and negative samples are cyclically sampled and the initial samples are redistributed and combined to form new samples. In this process, it will help the model to pay attention to those difficult negative samples and improve the detection effect of the model. On the other hand, this operation will ignore those simple samples, thereby changing the distribution of the entire positive and negative sample input.
## 8 Query/Data Protection Mechanism
### Overhead of Symmetric Encryption/MAC  
To get an idea of the performance impact of performing symmetric encryption/MAC on every data packet, we conducted a simple experiment in which mote \(A\) (see Figure 7) sent 2000 packets over the air as rapidly as possible. We varied the packet size from 40 bytes to 100 bytes (near the upper limit of packet size for our platform) and measured the throughput of the system. As illustrated in Figure 8, using encryption and integrity checking does lower the throughput from 31-53 kbps to 8-14 kbps (depending on packet size). We also directly measured the time required to perform the MAC and encryption operations on a single 100-byte packet and we found that this consumes about 44 ms of CPU time. However, we feel that this is a reasonable price to pay for the added security. Moreover, the lower throughput is still sufficient for a practical deployment, as we explain later.  
Figure 7: Experimental setup with two motes and a PC.
## 2 Network Model and Threat Model
### Threat Model  
In this paper, we assume that malicious adversaries are intelligent and tend to launch attacks against some nodes with great value. Without loss of generality, we assume that there are multiple individual adversaries in the system, and all attacks are independent repeated trials. Therefore, the attack interval follows an exponential distribution \(t_{interval}\sim E(\lambda)\), where \(\lambda\) is the number of attacks in a unit of time.  
We also assume that the malicious code injected by the adversary does not exist in the memory of remote devices persistently. To avoid detection, the adversary may eliminate malicious codes after  
Figure 1: An example of the advanced metering infrastructure (AMI).  
Figure 2: An example of a wireless mesh network for smart grid communication.  
an attack is complete. Therefore, the lifetime of malicious codes on the remote device may not be long enough to be detected by attestation schemes. Furthermore, after the malicious device is detected, it requires some time to find the cause of faults or bugs and to update the hardware or software of compromised nodes. We also assume that the remote node has a high probability in which undiscovered bugs still exist and could be compromised again even after its software is patched.  
In addition, because our proposed scheme is an enhanced software-based attestation scheme based on SWATT [11], we have similar assumptions: (i) the verifier is well protected, and information stored in it will not be leaked to the adversary; (ii) the verifier has the knowledge of the hardware structure and software information on the remote devices, including the CPU clock speed, the memory architecture, the instruction set architecture (ISA) of the microcontroller and others; and (iii) the verifier has a local copy of the memory contents of remote devices, so that it can compute the correct checksum, as well.  
Because smart measurement devices are often connected through wireless networks, the adversary may launch cyber attacks and compromise measurement devices. For example, the adversary can manipulate the memory contents on compromised devices and launch false data injection attacks [8; 24; 25; 26]. We assume that the adversary will not be able to manipulate the hardware, because it is extremely difficult to replace devices while keeping the system running. Therefore, all assumptions made here are feasible and can be applied to real-world practice.
###### Abstract  
Accurate prediction of the thermal conductivity of ethylene glycol (EG) and aluminum oxide (Al\({}_{2}\)O\({}_{3}\)) nanofluids is crucial for improving the utilization rate of energy in industries such as electronics cooling, automotive, and renewable energy systems. However, current theoretical models and simulations face challenges in accurately predicting the thermal conductivity of EG-Al\({}_{2}\)O\({}_{3}\) nanofluids due to their complex and dynamic nature. To that end, this study develops several supervised ML models, including artificial neural network (ANN), decision tree (DT), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), multi-layer perceptron (MLP), and extreme gradient boosting (XGBoost) models, to predict the thermal conductivity of EG-Al\({}_{2}\)O\({}_{3}\) nanofluids. Three key parameters, particle size (D), temperature (T), and volume fraction (VF) of EG-Al\({}_{2}\)O\({}_{3}\) nanoparticles, are considered as input features for modeling. Furthermore, five indices combining with regression graphs and Taylor diagrams are used to evaluate model performance. The evaluation results indicate that the GBDT model achieved the highest performance among all models, with mean squared errors (MSE) of \(6.7735\times 10^{-6}\) and \(1.0859\times 10^{-5}\), root mean squared errors (RMSE) of \(0.0026\) and \(0.0033\), mean absolute errors (MAE) of \(0.0009\) and \(0.0028\), correlation coefficients (R\({}^{2}\)) of \(0.9974\) and \(0.9958\), and mean absolute percent errors (MAPE) of \(0.2764\%\) and \(0.9695\%\) in the training and testing phases, respectively. Furthermore, the results of sensitivity analysis conducted using Shapley additive explanations (SHAP) demonstrate that T is the most important feature for predicting the thermal conductivity of EG-Al\({}_{2}\)O\({}_{3}\) nanofluids. This study provides a novel calculation model based on artificial intelligence to realize an innovation beyond the traditional measurement of the thermal conductivity of EG-Al\({}_{2}\)O\({}_{3}\) nanofluids.  
EG-Al\({}_{2}\)O\({}_{3}\) nanofluids; thermal conductivity; prediction; machine learning; SHAP
# Handover Management for D2D Communication in 5G Networks
## 4 Proposed Approach
### Choosing Handover Target
Thus, with the joint consideration of stability of connection and avoidance of ping-pong effect, we would designate the eNB with the largest \(D_{counter}\) value and its strength holding for a period of TTT to be the new serving eNB. For the third subclass, as the target eNB is worse than the original serving eNB, we will retain the connection to the original serving eNB to minimize unnecessary handovers. For the third class, both users of the \(i\)-th D2D pair are connected to the target eNB. We can further classify this class into three subclasses: (1) both users can satisfy their minimum requirement of connection, (2) either one of them can satisfy the minimum requirement of connection but the other cannot, and (3) neither one of them can satisfy the minimum requirement of connection. For the first subclass and the second subclass, we would hand over the D2D pair to the target eNB due to the target eNB's advantage over the original serving eNB in satisfying both users. For the third subclass, the minimum requirement of connections of both users cannot be satisfied, no matter connecting to the target eNB or the original serving eNB. We can only choose the best eNB with which both users have better-received signal strength, as specified in the utility function (8). Thus if such eNB can keep its advantage for a period of TTT, it will be the new serving eNB for the D2D pair. The handover decision rules above are comprehensively shown in Algorithm 2, where the first class, second class, and third class are listed from Line 1 to Line 3, Line 5 to Line 20, and Line 21 to Line 37, respectively. Thus, with Algorithm 1 and Algorithm 2, for each D2D pair, we can obtain the best handover target by jointly considering the movements and the received signal conditions of both users. Finally, as a result of Algorithm 1 and Algorithm 2, we can achieve our goal of reducing unrequired handovers caused by a possible ping-pong effect while assigning both users of the same D2D pair to the same eNB.
# Applications of Mueller Matrix Polarimetry to Biological and Agricultural Diagnostics: A Review
The works devoted to the metrological aspects of MMP, in particular, the calibration of MM polarimeters and the quantification of MMP results to determine the properties of biological samples, are valuable for the practical utilization of MMP [49; 50; 51; 52; 53; 54; 55; 56]. It is important to note that MMP is no longer limited to only exploratory studies with unique laboratory instruments, and, at present, ready-to-use equipment for MMP is quite accessible and well described for customers. Commercial MMP systems supplied with a wide range of analysis software for specific applications provide easy-to-understand data for various MMP-related research and industries [57; 58]. The cited references demonstrate the widespread use of MMP methods. Our review describes the foundations of Mueller matrix polarimetry and the ways of integrating it into existing schemes of optical diagnostics with an explanation of the corresponding restrictions and also analyzes specific examples of and prospects for the application of MMP methods in biology and agriculture.
## 6 Experimental Results
### Tournament Selection Size  
Another interesting point that occurred during the development of the algorithm is the size of the tournament selection of the genetic algorithm as the population size increases. It is obvious that as the population size increases, a small tournament selection size results in small improvement of the quality of the solutions. Thus, it was decided to increase the tournament selection size as the population size increases. For this reason, we chose the tournament selection size to be a fraction of the population size. However, the bigger the size of the tournament selection, the more computational time that is required. A balance was obtained for \(pop/32\). In Tables 6 and 7, the average distance from the best reported solution in the literature for a constant tournament selection size (10) and for a fraction of population size (\(pop/32\)) is illustrated. The experiments were run five times for all thirteen datasets for three population sizes, with different seeds, but the same initial solutions, and the mutation probability was 0.3% and the crossover probability 75%. Furthermore, the steepest descent algorithm was excluded from these measurements, so that it could not impact the quality of the solutions. It is clearly seen that with a larger tournament selection size, the algorithm obtains better solutions in population sizes of 2048 and 16,384. For a population size of 256, the tournament selection size (10) is greater than the fraction of the population size (256/32 = 8). It is worth mentioning that in general, it is here illustrated that bigger population sizes give better solutions.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Dataset** & \(pop=256\) & \(pop=2048\) & \(pop=16,384\) \\ \hline car-f-92 & 77.73 & 66.69 & 63.97 \\ car-s-91 & 90.09 & 82.24 & 78.27 \\ ear-f-83 & 67.90 & 58.75 & 61.48 \\ hec-s-92 & 66.13 & 59.39 & 58.09 \\ kfu-s-93 & 52.36 & 46.04 & 45.46 \\ lse-f-91 & 67.17 & 64.63 & 63.47 \\ pur-s-93 & INF & INF & INF \\ rye-s-93 & 119.01 & 106.42 & 94.77 \\ sta-f-83 & 19.43 & 19.43 & 19.42 \\ tre-s-92 & 53.26 & 49.31 & 41.63 \\ uta-s-92 & 80.02 & 73.28 & 71.11 \\ ute-s-92 & 30.02 & 33.75 & 31.53 \\ yor-f-83 & 37.82 & 35.69 & 34.25 \\ \hline \end{tabular}
\end{table}
Table 6: Average difference (%) from the best reported solution in the bibliography with the genetic algorithm and a tournament selection size of \(10\).
# Communication Rate Increase in Drill Strings of Oil and Gas Wells Using Multiple Actuators
###### Abstract  
Wireless data communication and telemetry during drilling deep oil and gas wells are important enablers for safe and timely drilling operations. The transmission of information through drill strings and pipes using sound waves is a useful and practical approach. However, given the limited available bandwidth, transmission rates are typically smaller than what is needed. In this paper, a new method and system are proposed to increase the transmission rate over the same bandwidth, by deploying more than one actuator. Upon using multiple actuators, several data streams can be transmitted simultaneously. This increases the data rate without the need for additional bandwidth. The experimental results of a testbed with two actuators are presented, where the transmission rate is doubled with no bandwidth increase. A strain sensor receiver and accelerometer receivers are used to separate and demodulate the two data streams. It is demonstrated that it is possible to recover the data in the new faster system benefiting from two actuators, while having about the same bit error probability performance as a one-actuator system. Various combinations of strain and acceleration sensors are considered at the receive side. Due to some properties of strain channels (e.g., smaller delay spreads and their less-frequency-selective behavior) presented in this paper, it appears that a strain sensor receiver and an accelerometer receiver together can offer a good performance when separating and demodulating the two actuators' data in the testbed. Overall, the experimental results from the proposed system suggest that upon using more than one actuator, it is feasible to increase the data rate over the limited bandwidth of pipes and drill strings.  
oil and gas wells telemetry; oil and gas wells communication; measurement while drilling; drill strings; pipes; wireless telemetry; strain sensors; accelerometers; acceleration sensors; actuators +
Footnote †: journal: Sensors  
10 January 20191337 March 201917 March 201910 January 2019137 March 201910 January 2019137 March 2019137 March 201910 January 2019137 March 20 doubled by transmitting two data streams simultaneously without increasing the bandwidth. While two piezoelectric actuators were used here, one can use magnetostrictive actuators [4; 5; 6] as well.  
The rest of this paper is organized as follows. The experimental testbed is explained in Section 2. Channel measurements and communication test results are presented in Sections 3 and 4, respectively. Section 5 provides some concluding remarks.
# Development of Open-Source Tools for Event-Based Hydrological Modelling Using GIS and Python
## 2 Materials and Methods
### Integration with Hydrological Models
Figure 2 represents an idealised catchment and the processes' workflow we follow for the hydrological simulations. We present some examples using the outputs of the QGIS operations for running the following: (1) the Green and Ampt [36] infiltration model (see Equations (2) and (3)) to estimate the excess of runoff from a set of synthetic storm events; (2) the soil conservation service curve number (see Equations (3)-(5)); and (3) the flow routing as estimated by the kinematic wave model integrated with the Muskingum-Cunge routine [37] (see Equations (6) and (7)). We code the models in Python 3.7. Figure 2: Idealised catchment (**a**) and processes’ workflow (**b**) for the hydrological simulations. Following [PERSON] et al. [38], the Green-Ampt model provides the potential infiltration rate \(f(t)\) [L\(\cdot\)T\({}^{-1}\)] as given by Equations (4) and (5):  
\[f(t)\cong k_{s}\bigg{(}1+\frac{\tau_{f}\cdot\Delta\theta}{F(t)}\bigg{)}, \tag{4}\]  
\[F(t)=k_{s}\cdot t+\tau_{f}\cdot\Delta\theta\cdot Ln\bigg{(}1+\frac{F(t)}{\tau_{ f}\cdot\Delta\theta}\bigg{)} \tag{5}\]  
where \(k\)s [L\(\cdot\)T\({}^{-1}\)], \(\Delta\theta\) [L\({}^{3}\cdot\)L\({}^{-3}\)] and \(\tau_{f}\) [L] stand for the saturated hydraulic conductivity, the difference between the saturated and the initial soil water content and the wetting front suction head, respectively. This model requires a homogeneous soil with a uniform initial water content while the saturated wetting front is assumed to move downwards as a single piston-like displacement. Similarly, the excess of runoff (\(E\) [L]) generated by a \(P\) [L] volume rainstorm depends on the curve number (\(CN\), dimensionless) and the initial abstraction (\(la\) [L]) as follows (Equations (6)-(8)):  
\[S=25.4\;*\;\bigg{(}\frac{1000}{CN}-10\bigg{)}, \tag{6}\]  
\[I_{a}=0.2\;*\;S, \tag{7}\]  
Figure 1:0  
The model assumes the precipitation above a given threshold (Initial abstraction) is divided between infiltration and runoff. The kinematic wave method is given by Equations (9) and (10):  
Figure 1:1  
Figure 1:2  
where \(Q\) ([L\({}^{3}\cdot\)T\({}^{-1}\)], x [L] and \(t\) [T] stand for the discharging flow over time t and across x, c [L\(\cdot\)T\({}^{-1}\)] is the wave celerity, and \(S_{0}\) [L\(\cdot\)L\({}^{-1}\)] and \(S\)f [L\(\cdot\)L\({}^{-1}\)] are the channel slope and the energy gradient, respectively. The kinematic wave solution for the Saint-Venant equations implies the flow is uniform, and the friction slope is approximately equal to the slope of the channel. For the Muskingum-Cunge [37] method, we followed the solutions presented in [38], solving the equations using an explicit finite difference approach with different time steps, setting [PERSON]'s relationship between time and spatial steps to ensure convergence.