esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Emerging technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), machine learning (ML), edge computing, and fog computing have had a significant impact on every application in terms of real-time monitoring, analyzing, and prediction [10]. In addition, the evolution of distinct kinds of biosensors and advanced communication protocols has empowered to implementation of emerging technologies to optimize dairy cattle through remote monitoring and data-driven decision making [11]. The emerging technologies implementation in dairy cattle enables to achieve real-time health monitoring, real-time tracking, real-time disease detection, real-time nutrition monitoring, real-time animal welfare, real-time monitoring of milk hygiene, and vision node-based furious animal attack detection. This work is motivated by the above aspects and explores the significance of emerging technology implementation in dairy cattle for various applications from an architectural and communication perspective as well as a future perspective to empower dairy cattle for achieving sustainability. The contribution of the study is as follows:  
* The study discusses the significance of implementing an intelligent ecosystem with emerging technologies for dairy cattle. * Hybrid architecture with ML-based edge device is detailed explained for physical and mental health monitoring of dairy cattle. * The integration of drones and long-range communication for animal tracking in real-time is discussed in detail. * The implementation of IoT-based devices and blockchain for milk quality monitoring and supply chain is presented. * Feed monitoring of the dairy cattle with real-time behavior and health through edge devices and robots are presented in this study. The organization of the study is as follows: Section 2 covers the overview and description of emerging technologies; Section 3 covers the animal health monitoring; Section 4 covers the animal location tracking and safety of animals; Section 5 covers the milk monitoring and supply chain; Section 6 covers the feed monitoring of the dairy cattle; Section 7 covers the discussion and recommendations.
## 1 Introduction
### Related Research
In contrast, the methodology applied allows for more detailed segmentation of sections with varying traffic flow characteristics, as illustrated in Figure 2. It breaks down the traffic flow into smooth sections, unstable sections near traffic accidents or merging/diverging areas, congestion zones influenced by these unstable sections, and subsequent smooth-flowing sections. Various methodologies have been used to analyze traffic patterns and accident risk factors. A traffic study in Shenzhen [25] focused on analyzing spatial hierarchical heterogeneity through geographic detectors and spatial autocorrelation analysis, emphasizing the identification of major factors and interactions. Research in Valencia [26] used Bayesian spatial models and kernel density estimation (KDE) methodologies to visualize overdispersed traffic data and accident hotspots. For the analysis of pedestrian accidents in Serbia, geographically weighted regression (GWR) was used to capture spatial heterogeneity and local effects [27]. This paper proposes a spatiotemporal dynamic segmentation method using high-resolution vehicle trajectory data and a geohash algorithm. This method contrasts with the aforementioned studies in that it dynamically seg  
Figure 2: Concept of separate spaces based on the geohash algorithm. unstable and low-speed traffic zones in real time, allowing for the precise detection of risks in merging and diverging sections. Additionally, the geohash-based method supplements the limitations of static node-link network analyses by enabling real-time identification of traffic risks. Unlike traditional methods, such as GWR and KDE, which excel in spatial representation and correlation analysis but do not respond immediately to real-time traffic conditions, the dynamic segmentation approach in this paper offers superior real-time assessment and adaptability. This enables immediate traffic management and provides information to drivers or autonomous systems, representing an advancement over static models that require recalibration. Furthermore, this method can capture detailed variations in speed and unstable traffic flows on highways, providing actionable data to prevent secondary accidents. It captures real-time changes more precisely than common spatial analyses, like KDE. The geohash method also demonstrates superior adaptability in analyzing sections affected by traffic flow changes, such as entry and exit ramps, contrasting with segmentation approaches used in other studies that fail to effectively address such dynamic regions. In conclusion, the geohash-based segmentation method shows significant advantages in real-time data processing, high-resolution analysis, and detailed examination of merging and diverging sections.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 5 Simulation Results  
### Distortion Measures for Non-Uniform Quantization  
In this subsection, we analyze the relationship between the threshold \(V_{th}\) and the corresponding minimum distortion value \(d(\mathbf{s},\mathbf{\hat{s}})\). Figures 9 and 10 illustrate the minimum distortion between the original training sequence and the quantized version when different quantization methods are employed. In the simulations, we use the ZC sequences with length \(L=128\) and root indexes \(u=127\), \(u=85\). The search step is set to \(\Delta=0.02\) to obtain the optimal mapping values \(T_{0}\) and \(T_{1}\) using Algorithm 1.  
Based on Figures 9 and 10, we can conclude that a larger quantization bit width leads to a smaller distortion \(d(\mathbf{s},\mathbf{\tilde{s}})\). Unlike uniform quantization methods, the performance of the proposed 2-bit non-uniform quantization method varies with different \(V_{th}\). For the ZC sequence with a root index \(u=127\), as shown in Figure 9, the minimum distortion \(d(\mathbf{s},\mathbf{\tilde{s}})\) is 0.047 when \(T_{th}=0.42\), \(T_{0}=0.2\), and \(T_{1}=0.84\). For the ZC sequence with a root index \(u=85\), as shown in Figure 10, the minimum distortion \(d(\mathbf{s},\mathbf{\tilde{s}})\) is 0.039 when \(T_{th}=0.5\), \(T_{0}=0.28\), and \(T_{1}=0.88\). This reveals that the optimal parameters for ZC sequences with different root indexes \(u\) are also different. The simulation results also indicate that the proposed non-uniform quantization scheme performs better than the  
Figure 10: Distortion function of quantized ZC sequence with different quantization methods (\(L=128\), \(u=85\)).  
Figure 9: Distortion function of quantized ZC sequence with different quantization methods (\(L=128\), \(u=127\)).  
uniform quantization method with the same bit width and is nearly as effective as the 3-bit uniform quantization method.  
It is well known that symbol synchronization based on preamble sequences is achieved by finding correlation peaks. Therefore, the peak-to-average ratio of the correlation results can be used as one of the metrics to evaluate the sequence synchronization performance. In our previous work [32], the feasibility of reducing complexity through non-uniform quantization of the preamble sequence has already been verified. In that work, the peak-to-average ratio (PAR) is used as the cost function for searching the optimal non-uniform quantization parameters. Therefore, we also evaluate the correlation characteristic of the quantized ZC sequence by analyzing the PAR of the
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 2 Materials and Methods
### Accuracy Assessment  
This study employed six metrics to evaluate classification accuracy: overall accuracy (OA), Kappa coefficient (Kappa), misclassification error (CE), omission error (OE), producer accuracy (PA), and user accuracy (UA). OA provides a general measure of classification effectiveness [39], while the Kappa coefficient assesses both the accuracy and reliability of the classifier, with values closer to 1 indicating superior performance [40]. CE [41] and OE [42] quantify the discrepancies between the classified image and the reference image, specifically measuring the overall misclassification rate and the degree to which a particular ground cover type is misclassified, respectively. PA [43] and UA [44] evaluate the probability of correctly classifying a specific land cover type, with PA emphasizing the accuracy of the classification map and UA focusing on the reliability of the classification results. Notably, CE, OE, PA, and UA were evaluated exclusively for water bodies.  
This study employed a multi-step approach to investigate spatiotemporal changes in water bodies within the Kashgar region. First, Landsat imagery (2003-2023) and Sentinel-2 imagery (2013-2023) were acquired from the Google Earth Engine (GEE) platform. Images with cloud cover of less than 15% were selected and masked. Second, five water body extraction methods (Mahalanobis distance, minimum distance, maximum likelihood, support vector machine, and random forest) were evaluated using Landsat 8 and Sentinel-2 imagery from June to September 2023. Accuracy assessment revealed that the random forest algorithm yielded the highest accuracy and was subsequently employed for water body extraction. Third, water body areas were quantified from 2003 to 2023. Temporal trends were analyzed using correlation analysis, [PERSON]'s slope estimation, and the Mann-Kendall test. Spatial patterns were examined through visual interpretation and kernel density analysis. Fourth, the overall area, permanent area, and seasonal area of water bodies were quantified to further elucidate spatiotemporal dynamics. Finally, the influence of precipitation, temperature, evaporation, and regional climate on water body changes was assessed.
## 2 Prerequisite Knowledge  
**Definition 1** (DMOP (Dynamic Multi-optimization Problem)).: _DMOP is the optimization problem consisting of a series of Multi-optimization Problems (MOPs) or DMOP instances that have multiple objectives. The DMOP instance is changed with the time/environment._The DMOP can be formulated into Equation (1). \(F^{(t)}=\{f_{1}^{(t)},f_{2}^{(t)},\ldots,f_{m^{(t)}}^{(t)}\}\) is the objectives to be optimized at time \(t\). \(m^{(t)}\) represents the number of optimization objectives at \(t\). Both the objective functions and their number can be changed with the time or the environments. Equations (2) and (3) are equality and inequality constraints, respectively. The constraint functions, \(H^{(t)}\) and \(G^{(t)}\), may be varied with time. \(X^{(t)}=\{x_{1}^{(t)},x_{2}^{(t)},\ldots,x_{n^{(t)}}^{(t)}\}\) includes the independent variables/arguments of the DMOP (objective and constraint functions), with the domain of \(D^{(t)}\). The domain and the number of arguments (\(n^{(t)}\)) are also changed with the time. \(t\) represents the time that the environment is changed. At this moment, DMOP is changed from one MOP into another. \(T\) is the number of environments that DMOP is concerned about. If the DMOP is concerned about the long-term optimization, \(T=+\infty\).  
\[\begin{array}{ll}\text{minimizing}&F^{(t)}=\{f_{1}^{(t)},f_{2}^{(t)},\ldots,f_{m^{(t)}}^{(t)}\}\end{array} \tag{1}\]  
subject to  
\[H^{(t)}=0, \tag{2}\] \[G^{(t)}\leq 0,\] (3) \[X^{(t)}=\{x_{1},x_{2},\ldots,x_{n^{(t)}}\}\in\mathcal{D}^{(t)},\] (4) \[t=t_{1},t_{2},\ldots,t_{T}. \tag{5}\]  
**Definition 2** (**Dominating**).: _For an MOP or a DMOP instance, an available solution dominates another available solution if and only if the first solution does not have a worse value of all objectives than the latter one and has at least one better objective value._  
It can be formulated as Equation (6) that the available solution \(X_{1}\) dominates the available solution \(X_{2}\), where \(F\) represents the optimization objective functions of the MOP.  
\[X_{1}\prec X_{2}:=\forall_{f\in F}(f(X_{1})\leq f(X_{2}))\wedge\exists_{f\in F }(f(X_{1})<f(X_{2})) \tag{6}\]  
**Definition 3** (**Pareto optimal solution set (PS) and Pareto optimal fronts (PFs)**).: _For an MOP or a DMOP instance, its PS is the set consisting of all available solutions (Pareto optimal solutions) that cannot be dominated by any other solutions. The PF is the set consisting of the objective function values of all Pareto optimal solutions._  
PS and PF can be formulated as Equations (7) and (8), respectively, for the MOP with the domain \(D\) and the objective function set \(F\).  
\[PS:=\{X|\
eg\exists_{X^{\prime}\in D}(X^{\prime}\prec X)\} \tag{7}\]  
\[PF:=\{F(X)|X\in PS\} \tag{8}\]  
The DMOEA is to find the \(PS\) and \(PF\) of all DMOP instances for the DMOP.
# Combined Channel Estimation with Interference Suppression in CPSS
## 4 Complexity Analysis and Performance Simulation
### Simulation Results
#### 4.2.1 Performance without Interference  
Mean square errors (MSE) of the channel estimation schemes are shown in Figure 3. The performance increases obviously by noise cancellation in LS channel estimation. The MSE of DFT-WF outperforms LS over 4 dB. The MSE of DFT-DA and DFT-KNN outperform LS by 9 dB more or less.  
In fact, all of these channel estimation schemes find the noise components by the modulus of the CIR of time domain. According to the envelop feature of CIR being known to the receiver, the signal and noise components are completely independent, and DFT-KNN that is based on a supervised learning algorithm can achieve Bayes minimum error. The frequency domain window has improved resolution of CIR of the time domain, but a lot of extra noise will be introduced to CIR by a filtering window function. It makes the performance gap of MSE between DFT-WF and DFT-KNN 5 dB more or less. DFT-DA is based on the Gaussian distribution of signal and noise components in CIR, but the signal and noise components obey the Rician distribution in the simulation, which makes the performance gap of MSE between DFT-DA and DFT-KNN by 0.5 dB more or less.  
The system bit error rate (BER) based on different channel estimation schemes are shown in Figure 4. Because of the performance difference in channel estimation schemes, the BER of DFT-WF outperforms LS close to 2 dB, the BER of DFT-DA outperforms LS by 2.5 dB more or less, the BER of DFT-KNN outperforms LS by 3 dB more or less. DFT-KNN has the best performance in the simulation.
## 4 Experiment Setup and Evaluation Metrics
### Evaluation Metrics: AWS Cloud Metrics
```
1:Input sys
2:Import ssl
3:Import adafruit_dht
4:Import paho.mqtt.client asmqtt
5:From Crypto.Cipher import ABS
6:Import base64
7:Define the key length which must be either 16, 24, or 32 bytes long
8:Define the MQTT broker variable
9:Define the MQTT port variable
10:While True do
11:Define a connection function
12:Subscribe for all topics in each IoT devices
13:Connect to Internet
14:If (the connection is established) then
15:Print \"connected\"
16:Else (the connection is not established) then
17:Try reconnecting to Internet
18:Define a message function
19:Define keys containing a set of attributes for each sensor data type in each IoT device
20:Define a first key containing a set of attributes for temperature sensor in each IoT device
21:Define a second key containing a set of attributes for humidity sensor in each IoT device
22:Create the cipher config for first key (temperature sensor)
23:Create the cipher config for second key (humidity sensor)
24:Decode the encrypted message using the base64 module
25:Use the cipher of the first key to decrypt the humidity degree message using cipher.decrypt
26:Use the cipher of second key to decrypt the temperature degree message using
27:Print the decrypted message
28:Publish humidity topic with its decrypted message to AWS cloud
29:Publish temperature topic with its decrypted message to AWS cloud
30:end while
31:Create two clients, the first client used for the MQTT broker, and the second client used for the AWS broker
32:Make the first client run connect, and message function
33:Connect the first client to MQTT broker using fog device's IP address and MQTT port
34:Call a loop_start0 method for the first client connection
35:Enable the transport layer security for the second client using the AWS certificates paths and MQTT protocol version
36:Connect the second client to AWS broker using AWS Endpoint and AWS port
37:Call a loop_start0 method for the second client connection
```  
**Algorithm 2**Collect data received from the fog device and send it to the cloud-IoT-Fog-Cloud architecture. This algorithm provides authentication and authorization operations from fog devices to IoT devices (DHT11 sensor + Raspberry Pi) and from fog devices to the AWS cloud.
## 3 Selective Redundancy Removal
### The \"reference\" problem  
Using SRR to hide data places its own challenges. The main reason for this becomes clear when one thinks at the decoding part of the covert transmission procedure. When the receiver detects an optimization that the sender could have made but has not, a '1' bit can safely be associated to it. On the contrary, the absence of a particular redundancy may not conclusively lead to infer a '0' bit. It may well be due to the absence of the specific redundancy in the original substrate, rather than to the omitted application of the particular optimization procedure targeted at that redundancy. It should be noted that this is not a universal problem that will arise in any environment in which SRR is used: in some environments, it is possible to unambiguously find out the subset of optimizations applied. For example, if the process is lossless compression, the substrate file, when uncompressed, will automatically yield a \"reference\" original that can be compressed again, analyzing the result to detect possible but unemployed optimizations. For uncompressed (and hence redundant) substrates, instead, some mechanism is called for to specify a reference. Possible workarounds to this problem are:  
* the selection, or ad-hoc generation, of a substrate guaranteed to contain all of a pre-defined set of redundancies. For example, one may rely on a particular program consistently producing the same set of redundancies when being fed similar input. Such information can be obtained by prior analysis;
* transfer of the catalog of all the possible optimizations for a single substrate, thus including those that have been applied and those that have not, by other means. This \"out-of-band\" transmission may be done through other conventional data hiding techniques.
## 4 Land-Streamer vs. Conventional Data
### Trace Comparison in Frequency-Offset Domain  
The amplitude spectra of the conventional and land-streamer data sets are shown in Figure 4. Here, we show the normalized summation of amplitude spectra of all traces in shot gather no. 74 (Figure 3). Both data sets show almost the same frequency content with peaks at the same frequency values (17 and 22 Hz). However, the conventional seismic data set contains more energy, especially at frequencies higher than 30 Hz. This is probably due to the better coupling of conventional receivers with the ground.
## 4 Candidate Generation
### Critical point detection  
For each 2D slice, each point along the contour of the colon is analyzed in terms of its convexity. Raised objects will have at least one concave point and should be accompanied by two convex points, on either side of the concave point, where the object is attached to the surface. Examples are shown in Figure 6. From this convexity determination, raised objects are extracted from the image.  
First, an angle \(a(i)\) is computed each point \(\mathbf{p}_{i}\) along the contour based on a chain code [31] using an eight neighborhood. Due to the discrete nature of the data, angles are naturally quantized to \(45^{\circ}\) angles. To provide robustness to noise, a lowpass Gaussian filter with standard deviation \(\sigma_{s}=3.5\) is applied to smooth the angles. Then, each point is classified as convex, concave, or smooth, using the following equation:  
\[C(\mathbf{p}_{i})=\left\{\begin{array}{ll}\text{concave},&a(i)>\pi+\epsilon \\ \text{convex},&a(i)<\pi-\epsilon\\ \text{smooth},&otherwise\end{array}\right. \tag{2}\]  
where \(\epsilon=0.3\). _Critical points_ are those for which this classification changes; _i.e._, points where the convexity/concavity transitions. The concave regions of the contour are identified, and the convex critical points defining the start and end points of the raised object are detected. A line between the two critical points is drawn, and the 2D region defined by this line and the concave part of the contour is stored as a 2D candidate. A schematic illustration is provided in Figure 7.  
Figure 6: Convexity analysis along a 2D contour of the colon segmentation.
## 3 Data Assimilation Strategy
### Assimilation Method  
#### 3.2.1 Ensemble Kalman Filter  
The EnKF [10; 54; 55; 56], connects the ensemble forecast with the Kalman filter (KF), which makes an implicit assumption that the observations are related to the true state \(X_{t}\) (e.g., FPC at time \(t\)) by:  
\[Y=HX_{t}+\varepsilon,\ \ \varepsilon\sim N\Big{(}0,\ \sigma^{2}\Big{)}, \tag{4}\]  
where \(Y\) is the \(m\)-dimensional observation vector, \(\varepsilon\) is the observation error with average \(\overline{\varepsilon}\) and covariances \(R=\overline{\left(\varepsilon-\overline{\varepsilon}\right)\left(\varepsilon- \overline{\varepsilon}\right)^{T}}\), \(T\) refers to transposed matrix, and \(H\) is the observation operator. Assuming the availability of observation data at time \(t+1\), the state variable of each corresponding ensemble is updated as:  
\[X_{i,\ t+1}^{a}=X_{i,t+1}^{f}+P_{t+1}^{f}H^{T}\Big{(}HP_{t+1}^{f}H^{T}+R\Big{)} ^{-1}\Big{(}Y_{t+1}-HX_{i,t+1}^{f}\Big{)}, \tag{5}\]  
where the superscripts \(f\) and \(a\) refer to the prior (forecast) and posterior (analysis/updated) estimates, respectively; \(i\) represents each individual ensemble number; \(P_{t+1}^{f}\) represents the error covariance of the state variable.  
Since the observation vector \(Y\) has a higher dimension (\(m\)), EnKF was found to be inefficient when we calculated the inverse matrix shown in Equation (5). To address this issue, a modified inverse calculation was proposed by [PERSON] et al. [57] based on the Sherman-Morrison-Woodbury formula [58]:  
\[\Big{(}HP_{t+1}^{f}H^{T}+R_{t}\Big{)}^{-1}=R_{t}^{-1}-R_{t}^{-1}Z\Big{(}I+Z^{ T}R_{t}^{-1}Z^{T}\Big{)}^{-1}Z^{T}R_{t}^{-1}, \tag{6}\]  
\[Z\equiv\frac{1}{\sqrt{N-1}}\Big{[}H\Big{(}X_{1,t+1}^{f}\Big{)}-H\Big{(} \overline{X}_{t+1}^{f}\Big{)},\ \ H\Big{(}X_{2,t+1}^{f}\Big{)}-H\Big{(}\overline{X}_{t+1}^{f}\Big{)},\ \cdots,\ \ H\Big{(}X_{N,t+1}^{f}\Big{)}-H\Big{(} \overline{X}_{t+1}^{f}\Big{)}\ \Big{]}, \tag{7}\]  
that is, \(HP_{t+1}^{f}H^{T}\equiv ZZ^{T}\). Since the total number of disturbing ensembles of state variable \(N\) is generally not very high, calculation of the inverse matrix of \(I+Z^{T}R_{t}^{-1}Z^{T}\) is more efficient. As a result, this method can significantly reduce the amount of calculation and enhance the operation efficiency.