esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligently Counting Agricultural Pests by Integrating SAM with FamNet
## 1 Introduction
Subsequently, the potential of large-scale pretrained models was recognized not only in the field of language but also in other domains. For example, Meta AI recently released the segment anything model (SAM), which has garnered attention for its impressive performance in class-agnostic segmentation. SAM can accurately generate masks and segment different instances in an image, even when the instances are unknown [13]. However, the application of such large-scale models like SAM in agriculture remains unexplored. This field holds immense potential but has yet to be fully developed. The enormous parameter count of SAM gives it strong generalization capability, laying the foundation for a unified agricultural model. In a recent study, the performance of SAM was evaluated in diverse real-world segmentation scenarios, including natural images, agriculture, manufacturing, remote sensing, and medical scenes [14]. The findings of this study revealed that the SAM demonstrates remarkable generalization capabilities in common scenes, such as natural images. However, its effectiveness is limited in low-contrast scenes, and it necessitates strong prior knowledge in complex scenes. The initial assessment of the SAM model on this task and its comparison with other baseline few-shot counting methods were carried out in the study conducted titled \"Can sam count anything? an empirical study on sam counting\". The findings revealed that SAM exhibited lower effectiveness compared to the state-of-the-art baseline when dealing with small and crowded objects. As a result, it is imperative to make further enhancements to SAM in order to overcome these limitations, particularly in specific scenarios [15]. This study proposes a scheme that utilizes SAM to assist FamNet in achieving better counting results. SAM's robust generalization capability can help FamNet handle variationsin different environments and lighting conditions. FamNet, in turn, can alleviate the low-precision segmentation issue of SAM in dense scenes. By combining these two approaches as proposed in this study, better solutions can be achieved for counting different crop pests and diseases. This method allows for reduced collection of pest datasets in smart agriculture and enables unsupervised pest density statistics with the introduction of external databases. With the continuous improvement in large-scale models and the expansion of quantifiable agricultural data, more models can be seamlessly integrated, forming the cornerstone of an integrated intelligent detection system for agricultural water, fertilizer, and pest diseases. It is worth noting that large-scale models also have limitations, which will be analyzed in the Section 4.
# Direct Mobile Coaching as a Paradigm for the Creation of Mobile Feedback Systems
## 6 Discussion
### Mobile Health  
Another area where feedback systems are used is mobile health (mHealth). Similar to feedback systems for sporting applications, these applications can address a wide variety of challenges, such as behaviour change or assisted living combined with remote measurement technologies. Behaviour change applications can involve becoming more conscientious [77], reducing chronic pain [78], reducing alcohol intake [79] or promoting weight loss [12]. In assisted living, mHealth technology has been shown effective of monitoring patients with cardiac [80] or neurological [81] conditions or stroke patients [82]. Due to their ubiquity, smartphones are often used as mobile recording devices in mHealth. While it is most likely that most mHealth applications can be modelled using DMC, it has to be pointed out that models specialised for this area, such as MobileCoach [16] and RADAR [17], are likely better suited.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 3 Neural Network-Based Forecasting Approaches
### Wavelet NN-Based Forecasting Approaches  
The merits of wavelet and neural networks are hybridized to form a new WNN to achieve better forecasting ability. WNNs have been used with great success in a wide range of applications. In some applications, it was proven that if the combination of a neural network and wavelet is used, the proposed model's efficiency is increased. The WNN architecture also follows the same fashion as the network shown in Figure 4. However,in the hidden layer, wavelet basis functions are used as activation functions instead of the conventional function of the FFNN.  
[PERSON] et al. proposed machine learning algorithms, namely wavelet network and genetic programming, for forecasting the average temperature precisely when it comes to weather derivative pricing, compared to SVM and RBF [185]. [PERSON] et al. developed a WNN model for electricity-based chaotic time series data to predict the spot market prices [38]. In [45], the researchers proposed the WNN model and compared it with the BPNN model for single-step forecasting of Lorenz and Mackey-Glass chaotic time series. It is to be noted that this approach can be extended further to be used for real-world chaotic data. In [74; 81], the authors proposed the forecasting models for wind farms. The wavelet decomposition method and ITSM in [74] showed an improved accuracy compared to ANN in predicting wind speed and power. Similarly, the developed hybrid algorithm using wavelet transform, chaotic theory, and grey model in [81] showed better prediction than the direct prediction method. The models in [74; 81] can be further optimized and applied in various countries' wind farms, such as the Dongtai wind farm in China. [PERSON] and [PERSON] presented the phase space reconstruction-based WNN method to predict [PERSON] and [PERSON]'s chaotic time series [95]. The significant benefit of this proposed method over a WNN is the improvement in SMAPE. It is to be noted that this concept can also help optimize the process parameters and the execution time during the simulation. [PERSON] et al. presented the wavelet transform and multiple model fusion for forecasting the [PERSON] and Mackey-Glass time series and achieved more effective performance in terms of SMAPE [120]. The models can be applied to real-world chaotic systems, such as geomagnetic series, network traffic series, etc. In [130], the ANN-discrete wavelet transform method was presented for forecasting the photovoltaic system's power based on chaos theory. The significant benefit of this method over the ANN and ANN-phase space reconstruction is the improvement in the Theil index.
## 2 The Proposed Fractional Order Auto-Tuning Method
The _third step_ in the design consists in a simple sine test performed on the process, with the frequency equal to the gain crossover frequency. Based on this, the process frequency response and its slope can be easily determined [19]. Other techniques can also be used to determine the process frequency response and its corresponding slope at the gain crossover frequency. The controller frequency response can be computed considering the following relation:  
\[C(j\omega_{c})=\frac{L(j\omega_{c})}{P(j\omega_{c})}=a+jb \tag{7}\]  
where \(L(j\omega_{c})=M_{L}e^{j\varphi_{L}}\), with the modulus \(M_{L}=1\), at the gain crossover frequency, and the phase \(\varphi_{L}=-180^{\circ}+PM\). Replacing (1) in (7), leads to the following results:  
\[k_{i}=-\frac{b}{\omega^{-\lambda}x}\text{ and }k_{p}=\frac{1}{\sin\frac{ \lambda\pi}{2}x} \tag{8}\]  
with \(x=a\sin\frac{\lambda\pi}{2}+b\cos\frac{\lambda\pi}{2}\). Once \(a\) and \(b\) are determined as in (7), the controller parameters in (8) depend solely on \(\lambda\). The derivative of the controller frequency response \(\left.\frac{\mathrm{d}C(j\omega)}{\mathrm{d}\omega}\right|_{\omega=\omega_{c}}\) can be computed numerically once the parameters of the FO-PI controller are determined. The _last step_ of the FO-KC auto-tuning method consists in taking small increments for the fractional order \(\lambda\) in the range \(\lambda_{\text{min}}<\lambda<2\) and computing the parameters in (8) as well as \(C(j\omega_{c})\) and \(\left.\frac{\mathrm{d}C(j\omega)}{\mathrm{d}\omega}\right|_{\omega=\omega_{c}}\). Then, evaluation of (5) leads to the real and imaginary parts in (6) required to compute the slope of the frequency response at the gain crossover frequency. For each \(\lambda\) in the range \(\lambda_{\text{min}}<\lambda<2\), it is then possible to evaluate the difference in (3) and select the minimum value. This then results in the optimal FO-PI controller.
###### Abstract  
For more than a decade, communication systems based on the IEEE 802.11p technology--often referred to as Dedicated Short-Range Communications (DSRC)--have been considered a de facto industry standard for Vehicle-to-Infrastructure (V2I) communication. The technology, however, is often criticized for its poor scalability, its suboptimal channel access method, and the need to install additional roadside infrastructure. In 3 GPP Release 14, the functionality of existing cellular networks has been extended to support V2X use cases in an attempt to address the well-known drawbacks of the DSRC. In this paper, we present a complex simulation study in order to benchmark both technologies in a V2I communication context and an urban scenario. In particular, we compare the DSRC, LTE in the infrastructural mode (LTE-I), and LTE Device-to-Device (LTE-D2D) mode 3 in terms of the average end-to-end delay and Packet Delivery Ratio (PDR) under varying communication conditions achieved through the variation of the communication perimeter, message generation frequency, and road traffic intensity. The obtained results are put into the context of the networking and connectivity requirements of the most popular V2I C-ITS services. The simulation results indicate that only the DSRC technology is able to support the investigated V2I communication scenarios without any major limitations, achieving an average end-to-end delay of less than 100 milliseconds and a PDR above 96% in all of the investigated simulation scenarios. The LTE-I is applicable for the most of the low-frequency V2I services in a limited communication perimeter (<600 m) and for lower traffic intensities (<1000 vehicles per hour), achieving a delay pf less than 500 milliseconds and a PDR of up to 92%. The LTE-D2D in mode 3 achieves too great of an end-to-end delay (above 1000 milliseconds) and a PDR below 72%; thus, it is not suitable for the V2I services under consideration in a perimeter larger than 200 m. Moreover, the LTE-D2D mode 3 is very sensitive to the distance between the transmitter and its serving eNodeB, which heavily impacts the PDR achieved.  
dedicated short-range communications; cellular-V2X; vehicle-to-infrastructure communication; packet delivery ratio; end-to-end delay +
Footnote †: journal: _Sensors_  
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## 5 Use Case
### Hazard Analysis and Risk Assessment  
In the Hazard Analysis and Risk Assessment (HARA), hazard scenarios are identified and assigned an ASIL (automotive safety integrity level) rating, which determines safety goals to mitigate risks. HARA begins with defining operational situations relevant to the ITEM, characterized by attributes, categories, and an exposure value indicating operational frequency. Malfunctions are identified via a HazOp procedure, categorizing failures in functionality. Accident scenarios arise from malfunctions occurring in specific operational situations, with each scenario assigned a controllability rating to assess manageability by the driver. From accident scenarios, hazardous events are identified, representing situations that may lead to accidents. Hazards--conditions or behaviors arising from malfunctions--are documented and linked to hazardous events using tailored HazOp or FMEA frameworks. Multiple hazards may correspond to a single hazardous event and vice versa. Effects of hazardous events are defined at both ITEM and vehicle levels, linked to specific elements, and assigned a severity rating. Ratings range from severe (e.g., collisions) to minor (e.g., slow driving). Severity, controllability, and exposure ratings collectively determine ASIL values. Each hazardous event requires at least one safety goal, aimed at reducing associated risks to acceptable levels. The diagrams of the model are shown in Figures A9-A14.
## 6 Data Analysis
### Techniques Based on Authenticity Requirements:
#### 6.2.4 Performance Evaluation and Metrics Employed by the Techniques  
This section reviews the performance evaluation and the metrics used to evaluate the techniques. To categorize the performance evaluation analysis methods, the review distinguishes between techniques with tools (methods utilizing software or hardware in the assessment process) and methods without tools (methods relying on mathematical analysis). Figure 10 illustrates the methods using tools, including case studies, simulations (e.g., MATLAB, NS2, computer simulation experiment, and Anylogic), datasets (e.g., Public, Real, and Synthetic), formal security proofs (e.g., Scyther), and prototype implementations. Conversely, analysis without tools relies solely on mathematical analysis.  
Previous research has identified specific evaluation metrics for each security and privacy requirement in technology. These metrics have been replicated in other studies to assess various security requirements. For instance, the authenticity requirement is evaluated based on response time, access response (deny or permit), and cost. The confidentiality requirement is measured using metrics such as execution time, communication costs, communication complexity, exchange key time, CPU usage, memory usage, and boot time. Attack detection requirements are assessed through metrics like accuracy, precision (PPV), recall (TPR), detection rate, RAM usage over time, CPU usage over time, overhead, and time cost. However, it is important to note that performance evaluation matrices may not adequately cover all the security and privacy requirements outlined in BYOD policies, highlighting the need for further exploration in this field.  
Overall, performance evaluation and metrics provide invaluable insight into the efficacy, efficiency, and vulnerabilities of security access control systems. By employing these evaluation techniques, organizations can strengthen their access control measures, mitigate security risks, and protect sensitive resources and data. Figure 10 illustrates the classification of the performance evaluation analysis methods adopted by the techniques  
Figure 9: Techniques that did not satisfy classified privacy requirements.  
examined in this review. Furthermore, Tables 13 and 14 provide a summary of the metrics used by the techniques and their respective purposes.
# The Local Integrity Approach for Urban Contexts:
## 2 Local GNSS Integrity Approach  
### General Idea and Suitable Architecture  
The local integrity is based on the concept of using connected vehicles as sensors in order to implement a cooperative monitoring of the integrity of GNSS signals. As shown in Figure 1, the general idea is to take advantage of multiple observations of GNSS signals shared by multiple On-Board Units (OBUs) mounted on vehicles participating to a VANET architecture [10].  
It must be pointed out that the possible system architecture is not just limited to a VANET with a centralized processing facility, as in Figure 1. In fact, depending on the user case and also on the available communication links (VANET or 3G/4G, as a backup in case of insufficient coverage), multiple architectures can be considered for the local integrity, also including de-centralized and non-real-time approaches:  
* a (fully or partial) post-processing-based approach, in case of insufficient or intermittent connectivity of the vehicles with the centralized processing facility. In this case GNSS measurements are temporarily stored on board of each vehicle. They are downloaded and transmitted to the server only when the vehicle is connected to the network (e.g., once per day or per month). The suitability of this non-real time approach depends on the application requirements (e.g., in case of a road tolling solution);
* a server-side implementation, requiring one-way communications from the vehicles to the control centre, with centralized processing and without on-board data storage and display (e.g., fleet management);  
Figure 1: Illustration of the local integrity architecture: VANET communications are exploited for updating a data base with information on local GNSS measurement quality and then for predicting the local position confidence of each vehicle.  
* a vehicle-side real-time implementation, requiring a two-way communication in order to also display local integrity information on-board of each vehicle (useful for example in a collision avoidance system);
* a completely de-centralized approach, based on a collaborative architecture without the need for a control centre (e.g., in case of info-mobility solutions for leisure applications).  
However, for the sake of simplicity but without losing generality, a centralized architecture is considered in the following discussion. The vehicles are assumed to travel the same road segments with a comparable velocity and to be equipped with OBUs including GNSS receivers with similar characteristics. This assumption implies a comparable pseudorange measurement quality for receivers in similar operative conditions, or the adoption of proper compensation factors in case of different classes of receivers (e.g., consumer-grade or survey-grade). At this point it is necessary to identify a measurable quantity related to the pseudorange statistics in order to highlight possible local signal degradations.
## 5 Performance Evaluation and Results Discussion  
### Setup Phase  
The validation of effective deployment of the proposed strategy is presented in this section via numerical computations. The nodes are completely distributed in the 200 \(\times\)200 m\({}^{2}\) field. In Figure 2, in a randomly formed topology, the percentage of relay nodes is 0.1 and for ten layers of clusters there are 10 CHs and 45 CCOs. \(E_{{}_{elec}}\) for all the nodes is 50 nJ/bit. \(E_{{}_{l}}\) for long distance the energy consumed is 0.0013 pJ/bit/m4 and \(E_{{}_{s}}\) for short distance is 10 pJ/bit/m2. Energy used in beam forming \(E_{{}_{bf}}\) is 5 nJ/bit. The value of N is 1055 for 200 m\({}^{2}\) and 1500 for 300 m\({}^{2}\). The length \(\ell\) of data is 4000 bits. For a scalable and energy efficient IoT, we considered two scenarios one with the value of N, 1055 for 200 m\({}^{2}\) area and other with value of N, 1500 for 300 m\({}^{2}\) area. The initial energy of all the nodes is 0.5 J.
## 3 Proposed Methodology
### Model Training and Performance Evaluation
#### 3.6.2 Evaluation Metrics  
It is possible to classify transitional activities as a multi-class categorization. Accuracy and F1-score are commonly used measures for evaluating and comparing the efficiency of the proposed models. These performance indicators are derived using a confusion matrix to accurately determine the model's ability to recognize transitional activities.  
Given a multiclass classification issue using a collection \(A\) having \(n\) distinct class labels \(C_{i}\), \((i=1,2,3,\dots,n)\) represented by \(\{C_{1},C_{2},C_{3},\dots,C_{n}\}\). For that situation, the confusion matrix is an \(n\times n\) matrix, as seen in Figure 8. Each row of the matrix corresponds to an actual instance of a class, whereas each column corresponds to an anticipated instance of a class. At row \(i\) and column \(j\), an element \(C_{ij}\) of the confusion matrix specifies the number of cases for which the actual class is \(i\) and the signified class is \(j\).  
True Positive (\(TP\)), False Positive (\(FP\)), True Negative (\(TN\)), and False Negative (\(FN\)) are all factors that can be extracted from the confusion matrix and used to determine performance measures. Given the label class \(C_{i}\), the following mathematical formulae are used to determine the metrics of \(TP_{i}\), \(FP_{i}\), \(FN_{i}\), and \(TN_{i}\).  
\[TP(C_{i})=C_{ii} \tag{15}\]  
\[FP(C_{i})=\sum_{l=1}^{n}C_{li}-TP(C_{i}) \tag{16}\]  
\[FN(C_{i})=\sum_{l=1}^{n}C_{il}-TP(C_{i}) \tag{17}\]  
\[TN(C_{i})=\sum_{l=1}^{n}\sum_{k=1}^{n}C_{lk}-TP(C_{i})-FP(C_{i})-FN(C_{i}) \tag{18}\]  
From Equations (15)-(18), we defined accuracy, precision, recall, and f1-score, for a multiclass confusion matrix as in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **Formulas** \\ \hline Accuracy & \(Accuracy=\frac{\sum_{l=1}^{n}TP(C_{i})}{\sum_{l=1}^{n}\sum_{l=1}^{n}c_{ij}}\) \\ \hline Recall of class \(C_{i}\) & \(Recall(C_{i})=\frac{TP(C_{i})}{TP(C_{i})+FN(C_{i})}\) \\ \hline Precision of class \(C_{i}\) & \(Precision(C_{i})=\frac{TP(C_{i})}{TP(C_{i})+FP(C_{i})}\) \\ \hline F1-score of class \(C_{i}\) & \(F1-score(C_{i})=2\times\frac{Precision(C_{i})\times Recall(C_{i})}{Precision(C_{i})+ Recall(C_{i})}\) \\ \hline Recall & \(Recall=\frac{1}{n}\sum_{i=1}^{n}Recall(C_{i})\) \\ \hline Precision & \(Precision=\frac{1}{n}\sum_{i=1}^{n}Precision(C_{i})\) \\ \hline F1-score & \(F1-score=2\times\frac{Precision\times Recall}{Precision+Recall}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance metrics for a multiclass confusion matrix.  
Figure 8: Confusion matrix for a multiclass classification problem.
# Iterated Orthogonal Simplex Cubature Kalman Filter and Its Applications in Target Tracking
## 2 Brief Review of Gaussian Approximation Filter
**Measurement Update:**  
The updated state \(\hat{\mathbf{x}}_{k}^{+}\) and corresponding error covariance \(\mathbf{P}_{k}^{+}\) are estimated, respectively, as follows:  
\[\hat{\mathbf{x}}_{k}^{+}=\hat{\mathbf{x}}_{k}^{-}+\mathbf{K}_{k}(z_{k}-\hat{z}_{k}) \tag{6}\]  
\[\mathbf{P}_{k}^{+}=\mathbf{P}_{k}^{-}+\mathbf{K}_{k}\mathbf{P}_{z}\mathbf{K}_{k}^{\text{T}} \tag{7}\]  
where \(\mathbf{K}_{k}=\mathbf{P}_{xz}\mathbf{P}_{z}^{-1}\) denotes the Kalman filtering gain, \(\hat{\mathbf{z}}_{k}=\sum\limits_{i=1}^{N_{p}}\omega_{i}\mathbf{h}\Big{(}\hat{\mathbf{x} }_{k}^{(i)}\Big{)}\) represents the predicted measurement, \(\hat{\mathbf{x}}_{k}^{(i)}=\hat{\mathbf{x}}_{k}^{-}+\sqrt{\mathbf{P}_{k}^{-}}\mathbf{\sigma}_ {i}\) denotes the integral points generated by the numerical rule, \(\mathbf{P}_{z}=\sum\limits_{i=1}^{N_{p}}\omega_{i}\Big{(}\mathbf{h}\Big{(}\hat{\mathbf{x} }_{k}^{(i)}\Big{)}-\hat{\mathbf{z}}_{k}\Big{)}\Big{(}\mathbf{h}\Big{(}\hat{\mathbf{x}}_{k }^{(i)}\Big{)}-\hat{\mathbf{z}}_{k}\Big{)}^{\text{T}}+\mathbf{R}_{k}\) denotes the innovation covariance matrix, and \(\mathbf{P}_{xz}=\sum\limits_{i=1}^{N_{p}}\omega_{i}\Big{(}\hat{\mathbf{x}}_{k}^{(i)}- \hat{\mathbf{x}}_{k}^{-}\Big{)}\Big{(}\mathbf{h}\Big{(}\hat{\mathbf{x}}_{k}^{(i)}\Big{)}- \hat{\mathbf{z}}_{k}\Big{)}^{\text{T}}\) represents the cross-covariance matrix.