esa-sceva/satcom-chunk-collection huggingface dataset:
# Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2
## 4 Discussion
### DTW Dissimilarity and Classification Accuracies  
We used the DTW dissimilarity values to understand class confusions and see what parcels had temporal patterns different to those we had as reference patterns. Furthermore, the DTW dissimilarity can be a valuable source in understanding spatio-temporal autocorrelation within time series of satellite images. Some possible applications are related to refinement of classification or segmentation results by finding outliers, clustering of seasonal crops or as a feature to be used in other machine learning classifiers. More research is needed on this topic in order to understand the implications of DTW in measuring spatio-temporal autocorrelation.  
A variety of errors are encountered when dealing with land use/land cover classification [86; 87], especially for global land cover mapping [88; 89]. The majority of our UAs and PAs for both test areas were higher than those obtained by CropScape at the state-level [42; 70], which make our DTW approach suitable to be applied at larger scales for land use/land cover mapping. Furthermore, we investigated the error propagations and combined the errors from the samples obtained from CropScape with our final DTW classification results. Considering the complexity of our study areas, we see the combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas as very good. [PERSON] et al. [84] obtained a similar error of 23.2% when using Sentinel-2 bands for mapping 7 crops in Southern Germany, with vegetables and sunflower classes having the lowest UAs and PAs. [PERSON] and [PERSON] [26] had a classification error of 21.9% when analyzing a similar area as we did in California using a time-weighted DTW. [PERSON] et al. [90] mapped crop types at smallholder level with 20% classification errors and stated that red edge and near infrared bands of Sentinel 2 were the most important features in the classification.
## 5 Network Costs
### Maintenance Costs  
The maintenance costs are defined here as the costs of replacing hardware, enclosures, and power (spare batteries). The hardware includes antennas, data acquisition boards, and motes. The antennas are the most vulnerable part of the hardware because they are exposed to the environment. Most of the damage to the antennas is due to animals (e.g., chewing damage by squirrels and chipmunks) and falling tree limbs (e.g., under windy conditions).  
The enclosures suffer from mechanical stresses due to the need of frequent access to the mote batteries (e.g., opening and closing of the lid) and thermal stresses due to changes in temperature (e.g., freezing and thawing of water along seems and joints). The enclosures attached to trees (_i.e._, for sap-flow sensing nodes) undergo additional mechanical stresses caused by the natural growth of the tree and, in some cases, the enclosure is fractured leaving the electronic equipment inside vulnerable to water intrusion.  
Water is a leading cause of malfunctions in node hardware. It short-circuits electronics causing batteries to discharge rapidly and potentially damage sensitive circuitry of the mote or data acquisition board. Water also rusts metallic components, deteriorating batteries, wires, and motes. The quality of rechargeable batteries used to power the motes also deteriorates over time.  
Table 9 shows that the cumulative cost of maintenance of the WSN is about $5000 and the average cost per visit is about $32.
# Assessment of Road Noise Pollution in Urban Residential Areas--A Case Study in Pitesti, Romania
## 3 Results
### Noise Level Measurement of and Frequency Analysis  
The measurements were taken at the Maior Sontu traffic light intersection in Pitesti, on a working day around 6.30 p.m., when there is the evening traffic peak and there are also traffic measurements made during in the same time [35], useful for further analysis of the relationships between road traffic and road noise in the area.  
The obtained diagrams were then downloaded to a Word document and analyzed in the Noise and Vibration Laboratory, resulting in a series of observations and conclusions, presented below:  
* It was highlighted that the road noise at the traffic light intersection has a cyclical character, as well as the flows of vehicles that are formed-with decelerations and accelerations that contribute to a substantial increase in noise level);* The noise level (acoustic pressure) reaches high values of 73.5 dB, which is explained by the braking and acceleration processes and less by the heavy vehicles presence in traffic;
* Noise signal peaks are captured, which are, in this case, determined by the horn use (unlawful). They will have to be eliminated, being aberrant values (the program allows this) and then the noise level will be recalculated;
* The recording also shows the presence of a truck, which, over a relatively long period of time, contributes to an increase in noise.  
The resulting general conclusion is that there are possibilities for a fairly accurate assessment of road noise, so that the change in noise level for various road traffic management solutions at the intersection can be highlighted.
# Secured Real-Time Machine Communication Protocol
## 3 Methodology-SRMCP Protocol Design
### Phase and Communication Procedure
#### 3.1.1 \"Discovery\" Phase  
The first phase, \"Discovery\", is a variation of the standard TLS handshake. The server will close the connection immediately if the client certificate is not from a trusted certificate authority. Thus, the server remains silent for any untrusted client to prevent any probing.
## 4 Application to Etna
### 16 and 23 February 2021  
We focused our analysis on two episodes in the middle of February 2021. The first was related to February 16 (Figure 13), where we observed that the TC-EW1 preceded the WR and TC detections. In fact, the IS-EW1 and IS-EW2 signals have anticipated of about 20 min the WR-EW1 signal. It is interesting to note that the IP shows a peak of 310 more than one hour after the maximum value of \(7\times 10^{5}\) kg/s reached by \(Q_{M}\). On the contrary, from the TC images, it was not possible to estimate significant \(v_{ex}\) values because of a partial occlusion of the TC field of view, due to the accumulation of volcanic ash around the camera.  
Figure 14 shows the signals for the lava fountain of 23 February 2021. The \(Q_{M}\), \(v_{ex}\), and IP values reached values of 2 \(\times\) 10\({}^{6}\) kg/s, 220 m/s, and about 200, respectively, between 23:30 and 00:00 UTC. It is interesting to note the good overlapping, confirming the possible and reliable integration of these systems in the definition of the automated paroxysmal early warning. The TC-W1 generally anticipated the EW levels of the other two sensors, while the WR-EW levels were mainly close together in time as the \(Q_{M}\) increased.  
Figure 12: Time series of \(Q_{M}\) (kg/s), IP (-), and \(v_{ex}\) (m/s) are shown as blue, red, and magenta lines, respectively, for the 5 December 2015 Etna lava fountain. The EW level time steps are plotted as vertical dashed lines with different colors, identifying the associated sensors.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 3 Formulation
### Iterative MLFMA-MADBT Method
#### 3.4.2 Disturbance from the BT Region to the MLFMA Region  
For the interaction coupled back from the BT region to the MLFMA region, the induced PO near field \(\mathbf{E}_{near}^{PO}\) affecting the MLFMA region [23] can be taken into account by adding the additional exciting voltages to the voltage vector of the MLFMA. Figure 7 shows two triangles associated with the \(i\)th common edge in SWG basis functions.  
The additional exciting voltages \(\Delta\mathbf{V}\) can be calculated by the center point approximation method given as follows:  
\[\Delta\mathbf{V}_{i}=\frac{l_{i}}{2}(\mathbf{E}_{i}^{inc,c+}\cdot\rho_{i}^{c+ }+\mathbf{E}_{i}^{inc,c-}\cdot\rho_{i}^{c-}) \tag{7}\]  
where \(\Delta\mathbf{V}_{i}\) is the additional excitation voltage on the \(i\)th common edge, \(l_{i}\) is the length of the \(i\)th common edge, \(\mathbf{E}_{i}^{inc,c+}\) and \(\mathbf{E}_{i}^{inc,c-}\) are the induced near field \(\mathbf{E}_{near}^{PO}\) from the BT region at each triangle center which is the superposition of PO near field from all the small hit beams in the BT region, \(\rho_{i}^{c+}\) and \(\rho_{i}^{c-}\) are the vectors in the triangular pairs between the center point and the vertex opposite to the common edge on each triangle.  
Solving the matrix Equation (9) excited by the modified voltages in (8), we can get the new current \(\mathbf{I}^{MLFMA}\).  
\[\mathbf{V}_{i}=\mathbf{V}+\Delta\mathbf{V}_{i} \tag{8}\]  
\[\mathbf{Z}^{MLFMA}\mathbf{I}^{MLFMA}=\mathbf{V} \tag{9}\]  
Figure 7: The division of the MLFMA and BT regions for the whole structure.
## 4 The IEEE 802.15.5 WPAN Mesh Standard
Among these functionalities, our main interest concentrateson appropriately managing power consumption in the devices so as to save energy, since it is one of the most critical issues in WSNs. In this context, the IEEE 802.15.5 standard provides two solutions denoted as _Synchronous Energy Saving_ (SES) and _ASynchronous Energy Saving_ (ASES), respectively. SES and ASES are energy saving mechanisms based on a duty-cycle mechanism: periodically, devices that are in a state of full activity (ON), sensing the medium, collecting data and/or forwarding information, switch to a mode of low-power consumption (sleep/OFF) in which communications are temporarily disabled (the radio transceiver is deactivated) and other hardware components, as the micro, are set to the minimum activity. Duty-cycling allows us to extend the device lifetime months or even years, depending on the type of application. This is a clear enhancement in comparison with other technologies or standards, such as Zigbee Pro, which lacks energy-saving mechanisms, or the IP500 proposal, that only facilitates an energy solution to end-devices, obviating this mechanism to the rest of battery-powered devices in the network. This shortcoming forces these nodes to remain in active mode (they never switch to a sleep state), which implies a clear degradation of the device lifetime in comparison with the IEEE 802.15.5 standard. However, duty-cycle mechanisms require the coordination of all the network devices (SES) or at least the nodes involved in the data transmission (ASES), so as to avoid undesirable situations as, for instance, a device sending data to another one in sleeping state. This fact would entail the loss of messages and their later retransmissions, increasing the energy cost and delaying the delivery of messages. This aspect is tackled by SES and ASES under different strategies which are explained in detail in the next subsections.
## 5 Optimal Route Selection  
Shortest path planning is very important in path navigation. Traffic efficiency can be improved by planning the shortest path based on the distance information between locations. Floyd algorithm is a classic dynamic programming algorithm that uses dynamic programming to find the shortest path between multiple source points in a given weighted graph. The algorithm aims to find the shortest path from one point to another [32].  
Assume that the shortest path from a node \(i\) to another node \(j\) has no more than two possibilities, one is directly from \(i\) to \(j\), and the other is from \(i\) through node \(k\) to \(j\). Hence, the dynamic transfer function of this algorithm is: \(dis(i,j)=min\left(D_{i,j},\ D_{i,k}+D_{k,j  
point and the end point, respectively. The transfer function \(dis(i,j)\) is updated by \(dis(i,j)=\min\Bigl{(}D_{i,j},\ D_{i,k}+D_{k,j}\Bigr{)}\) to select the smaller of the two distances from \(i\) to \(j\) and from \(i\) to \(k\) to \(j\). The Floyd algorithm finds an intermediate node \(k\) to determine whether there is a shorter distance through this node \(k\).
# Improved Performance on Wireless Sensors Network Using Multi-Channel Clustering Hierarchy
###### Abstract  
Wireless sensor network is a network consisting of many sensor nodes that function to scan certain phenomena around it. WSN has quite a large problem in the form of delay and data loss which results in low WSN performance. This study aims to improve WSN performance by developing a cluster-based routing protocol. The cluster formation is carried out in several stages. The first is the formation of the cluster head which is the channel reference to be used by node members by means of probability calculations. The second determines the closest node using the Euclidean approach when looking for the closest member of the node to the cluster head. The third is determination of the node members by means of single linkage grouping by looking for proximity to CH. The performance of the proposed MCCH method is then tested and evaluated using QoS parameters. The results of this research evaluation use QoS parameters for testing the MCCH method, channel 1 throughput 508.165, channel 2 throughput 255.5661, channel 3 throughput 479.8289, channel 4 throughput 646.5618.  
enhancement; performance; wireless sensor network; multi-channel clustering
## 2 Related Works
### Q-Learning Network
#### 2.2.2 Double Deep Q-Network  
The integration of neural networks with reinforcement learning introduces the issue of overestimation. Overestimation occurs when there is a discrepancy between the estimated value function and the true value function. As a result of inaccuracies in the neural network's evaluation model, the predicted values fail to precisely represent the true values, thereby causing consistent discrepancies. In scenarios where the action values of different actions in the same state are similar, the model might opt for a suboptimal action, preventing it from learning the optimal policy. In turn, this can reduce the overall performance of the agent.  
At time t, the estimated value of the action \(a\) under the selected state \(s\) is represented as \(Q^{estimation}(s,a)\), the target value is \(Q^{target}(s,a)\), and the evaluation error caused by thefunction evaluation model is denoted by \(Y_{s}^{a}\). Moreover, the evaluation error \(Y_{s}^{a}\) is uniformly distributed in the range of [\(-\)e, e], and we can obtain Equation (9):  
\[Q^{estimation}(s,a)=Q^{target}(s,a)+Y_{s}^{a} \tag{9}\]  
DDQN [9] addresses the overestimation issue present in DQN by decoupling action selection from evaluation. First, the primary network, with parameters \(\theta\), determines the action with the highest Q-value. Then, the target network, with parameters \(\theta^{\prime}\), calculates the target value for the chosen action. The loss function \(L(\theta)\) is computed based on the evaluation value, and the parameters \(\theta\) of the main network are updated through backward error propagation, as outlined in Equations (10) and (11):  
\[Y^{DDQN}=r_{s}^{a}+\gamma*Q(s^{\prime},\arg max\,Q(s^{\prime},a^{\prime},\theta ),\theta^{\prime}) \tag{10}\]  
\[L(\theta)=E[(Y^{DDQN}-Q(s,a,\theta))^{2}] \tag{11}\]  
where \(\gamma\) is the discount factor and \(r_{s}^{a}\) is the reward value. The loss function \(L(\theta)\) is determined by the mean square error between the predicted value and the actual value.
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 2 Dynamic Model for Moving Platform
### Equations of Motion
 \tag{5}\]  
The displacement vectors (\(\delta_{n}\)) are decomposed into \(\delta_{nx}\) and \(\delta_{ny}\), as shown in Figure 6. The displacements and velocities in the \(y\)-direction are given as,  
\[\delta_{ny}=s_{n}(t)\theta(t)\sin\rho_{n}(t) \tag{6}\]  
\[\dot{\delta}_{ny}=\dot{s}_{n}\theta\sin\rho_{n}+s_{n}(t)\dot{\theta}(t)\sin \rho_{n}(t)+s_{n}(t)\dot{\rho}_{n}(t)\cos\rho_{n}(t) \tag{7}\]  
The horizontal reaction forces (\(R_{n}\)) on the blocks are functions of the displacements (\(\delta_{ny}\)) given as  
\[R_{n}=k_{n}\delta_{ny}+c_{n}\dot{\delta}_{ny}\quad\text{where }n=1,\;2,\;3,\;4 \tag{8}\]  
The coordinate of the LM block n is (\(x_{n}\), \(y_{n}\)), and (\(x_{g}\), \(y_{g}\)) is the geometric center, as shown in Figure 7. (\(x_{mid}\), \(y_{mid}\)) and (\(x_{mid}\), \(y_{mid}\)) are the midpoints of blocks 1 and 2 and blocks 3 and 4, respectively. The relation between the center of mass (\(x_{c}\), \(y_{c}\)) and the center of rotation (\(x_{p}\), \(y_{p}\)) is given by the theorem of similar triangles as follows: where \(y_{c}(0)\) refers to the initial y-coordinate value of the platform's center of mass. \[\begin{array}{l}x_{1}=x_{p}-s_{1}\cos(\tau_{1}+\theta),\;y_{1}=y_{p}+s_{1} \sin(\tau_{1}+\theta)\\ x_{2}=x_{p}+s_{2}\cos(\tau_{2}-\theta),\;y_{2}=y_{p}+s_{2}\sin(\tau_{2}-\theta) \\ x_{3}=x_{p}-s_{3}\cos(  
Substituting Equations (2)-(8) into the force equilibrium Equation (1) of the free body diagram shown in Figure 4 leads to the following equations of yaw motion:  
\[ \begin{array}{l}\sum F_{x}:F-\mu mg-\mu(R_{1}+R_{2}+R_{3}+R_{4})=m\ddot{x}_{p}\\ \sum F_{y}:-R_{1}+R_{2}-R_{3}+R_{4}-\mu mg=m\ddot{y}_{p}\\ \sum M:F(d-y_{p})-\left(x_{p}-\mu y_{p}+\frac{l+\mu w}{2}\right)R_{1}+\left(x_{ p}+\mu y_{p}-\frac{l+\mu w}{2}\right)R_{2}-\left(x_{p}-\mu y_{p}+\frac{q-\mu w}{2} \right)R_{3}+\left(x_{p}+\mu y_{p}-\frac{q-\mu w}{2}\right)R_{4}=l\ddot{\theta} \end{array}  \tag{10}\]
## 4 Results and Discussion  
### Overview of Reviewed Articles  
Table 3 presents basic information about the 10 eligible articles. There is a predominance of ex-ante studies analyzing the acceptance of a new technology prior to the actual adoption. Only one article examined the ex-post determinants of the choice to adopt an existing technology. Their research encompassed the field from general digital agriculture to specific technologies (e.g., smart pesticide technology, IoT traceability technology, and unmanned aerial vehicles technology, etc.). The quantitative approaches employed in these articles were mainly regression modeling based on data gathered from surveys or interviews with farmers. The geographical regions surveyed in these studies were principally the main agricultural production regions across 11 provinces in China. The sample sizes in these articles ranged from 264 to 3890. The findings from these studies provide the empirical basis for our review analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Authors and Publication Year** & **Approach** & **Analytical Method** & **Studied Technology** & **Study Area** & **Famper Type** & **Sample Size** & **No. of Variables** & **Model of Significance** \\ \hline [PERSON] et al. [30] & Ex-ante & Logit & General digital technology & National & General farmers & 3890 & 14 & Sig. \\ [PERSON] et al. [31] & Ex-ante & Probit & Precision pesticide technology & Five provinces & Apple farmers & 545 & 15 & Sig. \\ [PERSON] et al. [32] & Ex-ante & Probit & Digital pest and disease technology & Guangdong and Guangxi Province & Ltichi farmers & 901 & 18 & Sig. \\ [PERSON] et al. [33] & Ex-ante & Unified theory acceptance & IoT traceability technology & Shaanxi Province & Pig farmers & 264 & 10 & Sig. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of adoption analyses drawn from 10 reviewed studies.  
Figure 2: Diagrammatic flow of selected studies through the search and searching stages.
# Adaptive Wavelet Coding Applied in a Wireless Control System
## 3 System Description  
Figure 3b shows an overview of the control loop considered in the work, it being evident in this figure that the feedback and control signals are transmitted through wireless links. This control loop is adopted for the computational simulations and for the experimental tests carried out in this work. To carry out these experimental tests, a test bench was developed in which the wavelet coding is embedded in an ATMEGA 328p microcontroller associated with the Arduino UNO development kit. The experimental tests were carried out with the sole purpose of observing the processing time associated with the wavelet coding calculations and thus verifying if the addition of wavelet coding in the control loop could affect the performance of the system controller. In these experimental tests, the radio frequency (RF) module NRF24L01, from Nordic, was used for the transmission of RF signals needed to control the system under consideration.  
Figure 2: General structure of the wavelet decoder.  
In the context of this work, the evaluated system consists of a reservoir, a pump and two vertical tanks. The two tanks contain a hole in their base, which allows the flow of water, the upper tank receiving water pumped from the reservoir. Thus, the upper tank feeds the bottom tank through the hole in the base, and the lower tank closes the cycle with water returning to the reservoir by its bore. Despite other possibilities, the configuration adopted was a SISO (single-input-single-output) system, according to Figure 3a.
# Nitrogen Removal Using a Membrane Bioreactor with Rubber Particles as the Fouling Reducer
###### Abstract  
The use of granule activated carbon (GAC) and rubber particles as the bio-fouling reducer in a membrane bioreactor (MBR) was evaluated in this study. The addition of GAC tends to temporarily reduce Transmembrane Pressure (TMP). Then, after the initial reduction, TMP gradually increased back up to 0.7 bar, indicating significant fouling on the membrane. Low TMP values were observed after adding 0.5% (V/V) rubber particles to the same MBR. The organic compound and nitrogen removal efficiencies of the MBR under intermittent aeration were over 94% and 93.3%, respectively. The results showed that _Dysgonomonas_, _Acidobacteria_, and _Pantoa_ sp. contributed to the nitrification process while _Lactobacillus_, _Erythrobacter_, _Pythobacter_, and _Mycobacterium_ contributed to the denitrification process.  
membrane bioreactor; fouling reducer; granule activated carbon; rubber particle Article