esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Problem Solving and Algorithm Designing
The objective of parameter iteration is to minimize the mean square error of the Q-values calculated by the target network and the main network. This can be achieved by minimizing the loss function, which can be expressed as follows:  
\[J_{j}\big{(}\omega_{j,main}\big{)}=\mathbb{E}\bigg{[}\Big{(}\widehat{q}_{j} \big{(}S,A,\omega_{j,main}\big{)}-\Big{(}R_{j}+\gamma\underset{\mathbf{a}}{\text{ max}}\widehat{q}_{j}\big{(}S^{\prime},\mathbf{a},\omega_{j,target}\big{)}\Big{)} \bigg{)}^{2}\bigg{]}, \tag{25}\]  
where \(S^{\prime}\) and \(R_{j}\) represent the reached state and the acquired reward after taking action \(A\) in state \(S\), respectively. The gradient-descent algorithm is then adopted to minimize the objective function. The gradient of (25) can be calculated as follows:  
\[\
abla_{\omega_{j,main}}J_{j}\big{(}\omega_{j,main}\big{)}= \mathbb{E}\Big{[}\Big{(}R_{j}+\gamma\underset{\mathbf{a}}{\text{max}}\widehat{q} _{j}\big{(}S^{\prime},\mathbf{a},\omega_{j,target}\big{)}-\widehat{q}_{j}\big{(} S,A,\omega_{j,main}\big{)}\Big{)}\times \tag{26}\] \[\
abla_{\omega_{j,main}}\widehat{q}_{j}\big{(}S,A,\omega_{j,main} \big{)}\Big{]},\]  
where \(\
abla_{\omega_{j,main}}\widehat{q}_{j}\big{(}S,A,\omega_{j,main}\big{)}\) can be obtained through the gradient back propagation algorithm [35]. In each iteration, an experience batch \(D_{j}^{batch}\) is randomly sampled from the replay buffer \(D_{j}\) to train the value network. For each sample \(\left\{\left(\mathbf{s},\mathbf{a},r_{j},\mathbf{s}^{\prime}\right)\right\}\) in \(D_{j}^{batch}\), the parameter \(\omega_{j,main}\) of the main network is updated as follows:  
\[\omega_{j,main}\leftarrow\omega_{j,main}-\beta\
abla_{\omega_{j,main}}J_{j} \big{(}\omega_{j,main}\big{)}, \tag{27}\]  
where \(\beta\) is the learning rate. After \(\Delta\) iterations, the parameter \(\omega_{j,target}\) of the target network is updated as \(\omega_{j,main}\):  
\[\omega_{j,target}\leftarrow\omega_{j,main}. \tag{28}\]  
Algorithm 1 presents the matching algorithm based on multi-agent deep reinforcement learning, in which \(\epsilon\)-greedy is used to balance exploitation and exploration. The value network of each LEO satellite is centrally trained at the terrestrial traffic center based on the global states, global actions, and the local reward of each LEO satellite. Then, the trained value network is sent to the corresponding LEO satellite. At the \(j\)-th LEO satellite, its policy network can be trained in a decentralized manner based on its received value network with parameter \(\omega_{j,target}\) and its local observations. Afterwards, each LEO satellite develops its own optimal strategy based on its trained policy network to maximize the long-term return. Finally, each LEO satellite broadcasts the matching strategy to all HAP drones within its visible range.
###### Abstract  
Due to rapid changes in climatic conditions worldwide, environmental monitoring has become one of the greatest concerns in the last few years. With the advancement in wireless sensing technology, it is now possible to monitor and track fine-grained changes in harsh outdoor environments. Wireless sensor networks (WSN) provide very high quality and accurate analysis for monitoring of both spatial and temporal data, thus providing the opportunity to monitor harsh outdoor environments. However, to deploy and maintain a WSN in such harsh environments is a great challenge for researchers and scientists. Several routing protocols exist for data dissemination and power management but they suffer from various disadvantages. In our case study, there are very limited water resources in the Middle East, hence soil moisture measurements must be taken into account to manage irrigation and agricultural projects. In order to meet these challenges, a Smart Grid that supports a robust, reactive, event-based routing protocol is developed using Ad hoc On-Demand Multipath Distance Vector (AOMDV) as a starting point. A prototype WSN network of 5 nodes is built and a detailed simulation of 30 nodes is also developed to test the scalability of the new system.  
wireless sensor networks; reactivity; reliability; soil moisture; rainfall 1  
A Smart Sensor Grid to Enhance Irrigation
## 3 The Proposed Approach
### The Extraction and Recovery Phase
The extracted secret bits are_ \([0]\)_._
* _Get three bits from_ \(P^{\prime}=\{210,99,131,64,72,162,17,19,25,162,24,71,87,96,47\}\)_. Then, we can derive_ \((p_{x},p_{y},p_{z})=(17,19,25)\)_._ _The results of this step are_ \(S=\{0,0,0,1,1,0,\ldots\}\) _and_ \(P=\{210,99,131,65,72,162,17,19,25,\ldots\}\)_._
* _Step 4:_ 1. _Get the three bits from_ \(E^{\prime}=\{0,0,0,-1,0,0,1,1,-1,2,-1,0,1,2,0\}\)_:_ \((e^{\prime}_{x},e^{\prime}_{y},e^{\prime}_{z})=(1,1,-1)\)_. This is a Type-_\(B^{\prime}\) _case. The extracted secret bits are_ \([0]\)_._
* _Get three bits from_ \(P^{\prime}=\{210,99,131,64,72,162,17,19,25,162,24,71,87,96,47\}\)_. Then, we can derive_ \((p_{x},p_{y},p_{z})=(64+1,72,162)=(65,72,162)\)_._ _The results of this step are_ \(S=\{0,0,0,1,1,\ldots\}\) _and_ \(P=\{210,99,131,65,72,162\ldots\}\)_._
* _Step 3:_ 1. _Get the three bits from_ \(E^{\prime}=\{0,0,0,-1,0,0,1,1,-1,2,-1,0,1,2,0\}\)_:_ \((e^{\prime}_{x},e^{\prime}_{y},e^{\prime}_{z})=(1,1,-1)\)_. This is a Type-_\(B^{\prime}\) _case. The extracted secret bits are_ \([0]\)_._
* _Get three bits from_ \(P^{\prime}=\{210,99,131,64,72,162,17,19,25,162,24,71,87,96,47\}\)_. Then, we can derive_ \((p_{x},p_{y},p_{z})=(17,19,25)\)_._ _The results of this step are_ \(S=\{0,0,0,1,1,0,\ldots\}\) _and_ \(P=\{210,99,131,65,72,162,17,19,25,\ldots\}\)_._* _Step 4:_ 1. _Get the three bits from_ \(E^{{}^{\prime}}=\{0,0,0,-1,0,0,1,1,-1,\mathbf{2},\mathbf{-1},\mathbf{0},1,2,0\}\)_:_ \((e^{\prime}_{x},e^{\prime}_{y^{\prime}},e^{\prime}_{z})=(2,-1,0)\)_. This is a Type-_\(\mathcal{C}^{{}^{\prime}}\) _case. The extracted secret bits are_ \([1],[1],[1]\)_._ 2. _Get three bits from_ \(P^{{}^{\prime}}=\{210,99,131,64,72,162,17,19,25,\mathbf{162},\mathbf{24}, \mathbf{71},87,96,47\}\)_. Then, we can derive_ \((p_{x},p_{y},p_{z})=(162-1,24+1,71)=(161,25,71)\)_. The results of this step are_ \(S=\{0,0,0,1,1,0,1,1,\ldots\}\) _and_ \(P=\{210,99,131,65,72,162,17,19,25,161,25,71\ldots\}\)_._
* _Step 5:_ 1. _Get the three bits from_ \(E^{{}^{\prime}}=\{0,0,0,-1,0,0,1,1,-1,2,-1,0,\mathbf{1},\mathbf{2},\mathbf{0}\}\)_:_ \((e^{{}^{\prime}}_{x},e^{\prime}_{y^{\prime}},e^{\prime}_{z})=(1,2,0)\)_. This is a Type-_\(\mathcal{C}^{{}^{\prime}}\) _case. The extracted secret bits are_ \([1],[0]\)_._ 2. _Get three bits from_ \(P^{{}^{\prime}}=\{210,99,131,64,72,162,17,19,25,162,24,71,\mathbf{87},\mathbf{96 },\mathbf{47}\}\)_. Then, we can derive_ \((p_{x},p_{y},p_{z})=(87-1,96-1,47)=(86,95,47)\)_. The results of this step are_ \(S=\{0,0,0,1,1,0,1,1,1,0\}\) _and_ \(P=\{210,99,131,65,72,162,17,19,25,161,25,71,86,95,47\}\)_._
# Metaheuristic and Heuristic Algorithms-Based Identification Parameters of a Direct Current Motor
## 5 Discussion
However, the current signal estimated by the Jaya algorithm failed to reach the stabilization value; see Figure 15. The current signal obtained by GWO is the closest estimate to the real motor signal; see Figure 13.  
The average current values in the steady-state zone of each algorithm were as follows: 1.1821 A for the real signal, 1.1840 A for Steiglitz-McBride, 0.0827 A for Jaya, 1.1805 A for GWO, and 1.0968 A for GA. This means that GWO and Steiglitz-McBride were the algorithms that estimated the closest current response to the real one. The comparison of the overshoot values in the current signals were as follows: 7.8717 A for the real signal, occurring in 1.014 s; 7.6930 A for the Steiglitz-McBride algorithm, occurring in 1.016 s; 3.2511 A for Jaya, occurring in 1.004 s; 7.8738 A for GWO, occurring in 1.014 s; and, finally, 7.5736 A for GA, occurring in 1.018 s. The GWO algorithm proved to be the most accurate when discussing the maximum peaks, obtaining the same result as the real signal for time. For current, a 0.22% difference was displayed. In [3], the authors reported that a percentage error comparison cannot be reliable as a measure of the effectiveness of these algorithms. Since the algorithms do not show consistent solutions because they start from random solutions, performing the comparison by means of the standard deviation would be more suitable, which is a more stable metric for random data. Table 8 shows the difference in the standard deviation between the estimated parameters of each algorithm and the nominal values. The algorithms with the most significant deviation were Jaya and GA, with Jaya being the algorithm with the greatest variation in the estimates. On the other hand, Steiglitz-McBride and GWO had less variation in the estimates, where Steiglitz-McBride was the algorithm with the lowest deviation of 37.34% with respect to GWO. This may be due to Steiglitz-McBride having consistent estimated values, because it starts from a mathematical model. In contrast, metaheuristic algorithms start from random solutions which are adjusted to find the most suitable solution during iterations.
# An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images
## 3 Study Methods  
### Construction of Improved CASA Model  
Net primary production (NPP) refers to the total amount of organic matter accumulated by plants in the primary production stage per unit time and unit area. It is an essential indicator of plant-growth status [34]. The improved CASA model was used in this study to simulate the winter wheat NPP [49], and then the yield of the study area was estimated. After years of development, the parameters of the CASA model can be obtained by the inversion of remote sensing data and processing of meteorological data. This is done by using  
\[\text{NPP}(x,t)=\text{APAR}(x,t)\times\varepsilon(x,t), \tag{1}\]  
where \(t\) is time, \(x\) is the pixel number, \(\text{NPP}(x,t)\) [g C m\({}^{-2}\)] is the net primary production of pixel \(x\) at time \(t\), APAR(\(x\), \(t\)) [M] m\({}^{-2}\)] is the absorbed photosynthetically active radiation of pixel \(x\) at time \(t\), and \(\varepsilon(x,t)\) [g C M]\({}^{-1}\)] is the light use efficiency for pixel \(x\) at time \(t\).
## 2 Related Work  
### ML-Based Intrusion Detection  
For several decades, there has been a surge in research pertaining to Intrusion Detection Systems (IDSs), aiming to enhance intrusion detection performance across diverse security frameworks. Rule-based Deep Packet Inspection (DPI) has persisted as the dominant approach in network-based intrusion detection, particularly for proactively alerting and the identifying anomalies. Nonetheless, the situation exacerbates with the emergence of attack variants that exploit zero-day vulnerabilities, rendering the existing rule-sets for pattern matching increasingly ineffective and obsolete [7]. Moreover, the pervasive adoption of cryptographic protocols (such as HTTPS, SSH, and SSL) to establish end-to-end encryption has gained substantial traction, posing a formidable challenge to the comprehensive scrutiny of packet payloads [20; 21].  
Within this realm, there is a notable emphasis on leveraging machine learning techniques to improve the efficacy of intrusion detection processes. Numerous methodologies and models have emerged with the intent of bolstering the efficiency of IDSs, catering to the detection of an array of attack types. Notably, a significant focus lies on scrutinizing intrusion detection approaches that harness deep learning strategies. [PERSON] et al. [8] proposed the Multi-layer Feature Selection and Reduction IDS (MFSR-IDS) algorithm to provide a high level of protection against DoS and Probe attacks using an anomaly-based approach. [PERSON] et al. [11] developed an effective intrusion detector using both a deep learning model and a machine learning model. To obtain high-accuracy scores, a deep learning model was used in the process of extracting features, and a machine learning model was used to measure the intrusion detection performance of intrusion detection. [PERSON] et al. [12] proposed a 'deep adversarial learning response' to machine learning attacks targeting traffic from IoT devices using AdaBoost, DT, KNN, RF, and neural networks metrics. At this time, a new test bed was developed and tested, which collected the network traffic data set of IoT devices in general use situations such as media playback and then changed the statistics of outbound traffic using deep adversarial learning technology.  
In a study by [PERSON] et al. [9], a binary data set was converted to a binary code format, which was further converted into a file. Based on the hex code generated from the file, the acquired binary image was fed into a Convolutional Neural Network (CNN). The authors argued that while conventional models generally demonstrated improved performance, the CNN-based models exhibited even higher performance levels in terms of accuracy. [PERSON] et al. [10] constructed a large integrated data set by merging multiple data sets. They then proceeded with experiments using multi-classification based on types of attacks.  
We note that previous studies focused on improving intrusion detection performance through ML-based and DNN-based feature analysis and selection, whereas our research differentiates itself by focusing on evaluating domain similarity to leverage deep transfer learning.
## 2 Problem Formulation
### Motivation and Objective  
Although the CL and DT problems have been extensively studied, their combination still draws limited attention, especially when considering practical multi-robot operation conditions such as nonlinear models or limited sensing and communication capabilities. One answer to the above challenge was provided in [9] by implementing the EKF scheme and asynchronized measurement update. However, this only covered the CL task, and the uncooperative target was not considered. On the other hand, it is also well known that the computation of Jacobian matrices is required by EKF-based algorithms. This maycause difficulties during implementation. Moreover, the estimation performance may deteriorate if the assumption of local linearity is not valid (e.g., bearing sensors). An alternative approach to extending the algorithm while avoiding the aforementioned potential drawbacks is to use the UT.  
Motivated by the above observations, in this paper, a UT-based CLAT scheme (UT-CLAT) is proposed that can realize self-localization and target tracking simultaneously in practical multi-robot operation scenarios. The correlations are properly addressed by implementing split covariance methods, similar to the method in [9], and the covariance intersection method. The UT approach was adopted to approximate the statistics of random variables in nonlinear models. In the end, the effectiveness of the proposed UT-CLAT algorithm is illustrated using not only simulations with synthetic data but also experiments with a networked quadrotor system and off-the-shelf sensors (cameras and UWB transmitters).
## 3 Results and Discussion
### Fabric Physical Property
#### 3.2.3 Fabric Tensile Strength  
The measured values of fabric tensile strength in the warp and weft directions are presented in Figure 11a,b, respectively. As shown in Figure 11a, it can be seen that fabric samples set A and set E with the regulating layers possess a similar tensile strength in the warp direction to that of the fabric samples set B and set F without the regulating layer, respectively. It can be ascribed to the use of the same warp yams and warp density in all eight fabric samples with and without the regulating layers. For the fabric tensile strength in the weft direction, the fabric samples with the regulating layers exhibit much higher tensile strength compared to that of fabrics without the regulating layers. The possible reason is that the  
strength than that of fabric sample sets C, G, D, and H, respectively, with the average percentage increase of 33.45%. The main reason is that the weft yarns of 150 denier were used in the fabric sample sets A, E, B, and F while the weft yarn count of fabric sample sets C, G, D, and H is 75 denier. Lastly, to investigate the effect of face structure with or without the mixing component of black yarn on the fabric tensile strength, the fabric sample sets A, B, C, and D can be compared to their corresponding fabric sample sets E, F, G, and H, respectively. It can be observed that each comparison group does not show significant difference, which indicates that the introduction of black yarn for mixing into the fabric face layer does not have great influence on the fabric tensile strength.
# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 2 Spatial Adaptive Filtering Algorithm in Hadoop Environment
### Hadoop Distributed Framework  
With the development of Big Data, it has become difficult for traditional high-performance computers to meet the hardware requirements required for the current data processing, and for most organizations and people working in the Big Data field, high-performance computers are too costly and cannot be iteratively updated and maintained with the growing demand [40; 41; 42].  
Hadoop is an open-source distributed computing platform under the Apache Software Foundation. The emergence of Hadoop renders high-performance computers more attainable, and through the Hadoop framework, users can form a distributed computing and storage platform with high-performance computing and storage using inexpensive hardware. Hadoop users can use a clustered environment to solve the parallel computing, storage, and management problems in Big Data without understanding the underlying details of the distribution. Hadoop has evolved into a big data processing framework, with MapReduce, HDFS [43], and YARN as the main components, which are widely adopted and deployed in several fields of big data.  
Figure 2: Pixel identification information within the search window (\(12\times 13\)) obtained based on KS test.  
HDFS is a cluster-based distributed file system that runs on a cluster and has many similarities to the existing distributed file systems, but also has clear differences from other distributed file systems. The advantages of HDFS are its high fault tolerance, high reliability, suitability for batch processing, and intended deployment on low-cost hardware. In Figure 3, we provide a principle diagram for storing and reading data in HDFS. Users can read, upload, and save data at various stages of data processing and analysis to the HDFS for storage, which can greatly reduce the disk Input/Output (I/O) and achieve a performance improvement owing to the copy mechanism of the HDFS.  
To optimize the scheduling use of CPU, RAM, and I/O resources in a cluster, several conceptual aspects must be considered, such as data dependencies in the overall data processing flow, load imbalance, proper task partitioning, and the relevant scheduling strategies to be employed. YARN is a resource management and scheduling system that provides unified resource management and scheduling for upper-layer applications and can provide reasonable resource monitoring and scheduling management for clusters.  
MapReduce is a programming model for the parallel computing of large-scale datasets (1 TB and above), which performs split computation and merge operations on large-scale datasets through the \"Map\" and \"Reduce\" features. It can be used to run programs on distributed systems when the programmer does not know much about distributed parallel programming.
## 3 Results and Discussion
### Color  
The four formulations presented a heterogeneous aspect and characteristic color corresponding to the cactus pear residue variety used (Figure 2). Color is an important quality attribute that influences consumer choices and preferences [31].  
Figure 1: Scanning electron microscopy images of cactus pear (_Opuntia focus-indica_) residue tablets, 250\(\times\). (**A**) Green cactus pear powder (without excipient); (**B**) GM: green cactus pear residue with microcrystalline cellulose; (**C**) GL: green cactus pear residue with lactose; (**D**) Red cactus pear residue powder (without excipient); (**E**) RM: red cactus pear residue with microcrystalline cellulose; (**F**) RL: red cactus pear residue with lactose. a: Aggregates of microcrystalline cellulose; b: Aggregates of lactose.  
The color parameters are shown in Table 2. Green tablets had the highest values (\(p<0.05\)) for luminosity (\(L^{*}\)), having a brighter color than red tablets. For the green tablets, the \(a^{*}\) values were negative and \(b^{*}\) values were positive, which places them in the green-yellow quadrant, while the red tablets presented positive values for \(a^{*}\) and \(b^{*}\), corresponding to the red-yellow quadrant.  
The inclusion of binding agents within the same variety of cactus pear had no significant effect on \(a^{*}\) and \(b^{*}\) values, so the characteristic color of the fruits was preserved by the peel color itself. Both GM and GL tablets exhibited negative hue (h\({}^{\circ}\)) values; according to [PERSON], the addition of 180\({}^{\circ}\) to a negative value allows us to achieve a positive one [60], and with this adjustment it was possible to determine the yellowish tonalities for the GM tablets and reddish or pink ones for the RM and RL samples. The chroma (C*) value estimates the quantitative attribute of colorfulness, with higher chroma values indicating higher color intensity of samples as perceived by humans [31]. The green cactus pear formulations (GM and GL) presented higher C* values, followed by RL and RM, so red formulations had less intense tonalities. All samples preserved the color characteristic of the variety used, and attractive colors were observed (Figure 2); therefore, it is not necessary to add artificial colors to make them attractive to the consumer. Besides, cactus pear pigments have been widely used by the food industry as an economical and stable source of pigments [61].