esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Aware 3D Path Planning by Autonomous Ground Vehicle in Wireless Sensor Networks
## 4 Proposed Energy-Aware Path Planning (EAPP) Approaches
### Computational Complexity
#### 4.4.2 Computational Complexity of GA-Based Approach  
The computational complexity of the GA-based approach is \(O(n^{2})\) since it uses nested loops for calculating the fitness value of each individual in the population, where \(n\) is the number of nodes to be visited in our problem.
## 2 Architecture of the Proposed Method  
Before giving an overview of the proposed method, two fundamental projections, ring- and variance-projection, should be mentioned. The ring- and variance-projection transform the 2D image into 1D signals as function of radius, which are defined as the mean and variance of intensities along the radius, respectively. The main concepts of ring- and variance-projection are shown in Figure 2. The ring-projection calculates the average intensity for the specified radius, here three different rings, \(r_{1}=~{}60\), \(r_{2}=120\), and \(r_{3}=180\) pixels are shown. Furthermore, the corresponding variance-projections can be seen in the bottom figure. Obviously, the value of 180 pixels ring is lower than others because the intensity is almost uniform in this ring.  
The architecture of the proposed algorithm is shown in Figure 3. It consists of two phases: template preprocessing and online alignment. In the template preprocessing phase, template image \(P\) is the input specified manually. A pyramid technique is then used to build a multi-resolution image from the template image. Hybrid-projection values are then determined using ring- and variance-projection values and the weighting coefficients, \(w_{m,l}(r)\) and \(w_{\sigma,l}(r)\), in conjunction with corresponding multi-resolution images. The coefficients \(w_{m,l}(r)\) and \(w_{\sigma,l}(r)\) are those of the ring and variance projection, respectively, depending on the intensity distribution at the specific radius of the template image. In the online alignment phase, the inspection image \(S\) is the input and a sliding window, which is the same size as the region of interest (ROI) in the multi-resolution template image, is moved for multi-resolution inspection image \(S_{l}\). This detects the most likely candidates by evaluating  
Figure 2: Main concepts of ring- and variance-projection.  
the similarity coefficient of the hybrid projection values. The locations of the candidates with scores that exceed the similarity threshold \(T_{\text{min},l}\) are registered. The translation is estimated using the vector \(\overset{\rightarrow}{\text{T}}\) between the centers of the target detected in \(P_{L}\) and \(S_{L}\). These candidates and their corresponding ring intensity profiles for each specified radius undergo the rotation-estimation process. This proposed method is called the ring-shifted technique to estimate the rotation angle. The accuracy of the rotation angle is finally refined by using a parabola curve fitting. The details of the two phases of the algorithm are described in the following subsections.
# Secure LoRa Firmware Update with Adaptive Data Rate Techniques +
### Background  
There are numerous research publications and applications employing LoRa and LoRaWAN due to their excellent communications range at a given power consumption. LoRaWAN has three classes of operation (A, B, and C) that allow devices to optimize their performance at the expense of battery power when communicating to a LoRa Gateway. For example, LoRaWAN Class A consumes the least amount of power but can only receive data after it transmits. Class C consumes significant power but allows for advanced operations like mesh networking and is always able to receive data. Applications that benefit from LoRa and LoRaWAN include emergency response communication systems after earthquakes [5], livestock monitoring [6], and intelligent transportation systems [7]. Previous research primarily focuses on key technical issues, such as scalability [8], optimal parameter selection [9], multi-hop capabilities [10; 11], security [3], and energy consumption modeling [12; 13]. The prior work on LoRa rate adaptation emphasizes challenges with LoRaWAN communication errors in congested RF environments [14; 15]. A probing algorithm to improve LoRaWAN efficiency is proposed in [9], and hysteresis is added to the existing LoRaWAN ADR scheme to improve performance in [16]. The tuning parameters and convergence time are analyzed in detail in [17]. Challenges with ADR for mobile devices were covered in [18].  
In prior work [19], we address challenges specifically associated with ADR in LoRa ad-hoc networks. Dynamic timeouts and error recovery processes were used to maximize the efficiency, and the ADR expanded into using both LoRa and FSK. We then evaluated the performance of this system using simulations and hardware experiments. The ability to update firmware using LoRaWAN, referred to as Firmware Update Over-The-Air (FUOTA), was published by the LoRa Alliance in [20] and a variant of this capability was implemented securely in a STM32L4 in [21]. Subsequent detailed analysis of the energy efficiency of the LoRa Alliance capability was presented in [22; 23]. To the best of our knowledge, no prior work has been published on using LoRa for firmware updates independent of LoRaWAN.
## 2 System Description and Modelling
### Photo-Voltaic System Model (PV)  
The instantaneous generated power output, \(PV_{p}(t)\) of a PV system in kW with respect to the received solar irradiance, \(R(t)\) in W/m\({}^{2}\) can be determined by using Equation (1):  
\[PV_{p}(t)=\left(\frac{R(t)}{R_{stc}}\right)\times S_{pv}\times\eta_{pv} \tag{1}\]  
where \(\eta_{pv}\), expressed in percentage, is the PV module derating factor. The \(R_{stc}\) is the solar irradiance and \(S_{pv}\) is the rated capacity of the PV in kW under the standard test condition.  
Figure 1: Proposed schematic view of the microgrid.
# A Distributed Autonomous Mission Planning Method for the Low-Orbit Imaging Constellation
###### Abstract  
With the improvement of satellite autonomy, multi-satellite cooperative mission planning has become an important application. This requires multiple satellites to interact with each other via inter-satellite links to reach a consistent mission planning scheme. Considering issues such as inter-satellite link failure, external interference, and communication delay, algorithms should minimize communication costs as much as possible. The CBBA algorithm belongs to a fully distributed multi-agent task allocation algorithm, which has been introduced into multi-satellite autonomous task planning scenarios and achieved good planning results. This paper mainly focuses on the communication problem, and proposes an improved algorithm based on it, which is called c-CBBA. The algorithm is designed with task preemption strategy and single-chain strategy to reduce the communication volume. The task preemption strategy is an accelerated convergence mechanism designed for the convergence characteristics of CBBA, while the single-chain strategy is a communication link pruning strategy designed for the information exchange characteristics of satellites. Experiments in various scenarios show that the algorithm can effectively reduce communication volume while ensuring the effectiveness of task planning.  
multi-satellite system; distributed cooperative task planning; communication constraints
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
## 4 Study Case
### Fault Modeling
#### 4.3.2 Fault Decoupling  
Sensor Faults  
In the case of sensor faults, only one sensor is considered as output. So, a residual is generated through the corresponding observer in the observable part of the system. For this case, the sensor faults, an observer of complete order is considered. The output estimation error will be used as a residual. Thus two residuals are obtained, one for each sensor.  
**Subsystem 1.** For the first sensor, we consider the subsystem formed by the following equations, assuming that \(x_{2}\) is free of fault:  
\[\dot{x}_{1} = \frac{x_{3}}{l^{2}(m_{1}+m_{2})}, \tag{34}\] \[\dot{x}_{3} = \frac{-x_{4}^{2}\sin(x_{1})}{l2\cos^{3}(x_{1})(m_{1}+m_{2})}-gl \cos(x_{1})(m_{1}-m_{2})+U_{p},\] (35) \[\dot{x}_{4} = U_{y},\] (36) \[y_{s1} = x_{1} \tag{37}\]  
**Subsystem 2.** For the second subsystem we assume that \(x_{1}\) is free of fault, the following equations form the second subsystem:  
\[\dot{x}_{2} = \frac{x_{4}}{l^{2}\mathrm{cos}^{2}(x_{1})(m_{1}+m_{2})}, \tag{38}\] \[\dot{x}_{4} = U_{y},\] (39) \[y_{s2} = x_{2} \tag{40}\]  
Actuator Faults  
Similar to the case of sensor faults, we obtained the decoupled subsystems that are sensitive only to one particular fault, in this way, the residual is generated from an observer based on output feedback.  
**Subsystem 3.** For the first actuator fault (\(f_{a1}\)), the residual occurs in the channel of \(U_{p}\) since \(x_{1}\) is known, and assuming that \(x_{2}\) is free of fault.  
\[\dot{x}_{1} = \frac{x_{3}}{l^{2}(m_{1}+m_{2})}, \tag{41}\] \[\dot{x}_{3} = \frac{-\big{(}\dot{x}_{2}l^{2}\text{cos}^{2}(x_{1})(m_{1}+m_{2}) \big{)}^{2}\sin(x_{1})}{l^{2}\text{cos}^{3}(x_{1})(m_{1}+m_{2})}\] (42) \[-gl\cos(x_{1})(m_{1}-m_{2})+U_{p},\] \[y_{a1} = x_{1}. \tag{44}\]  
Note that \(x_{4}\) is not available, it can be obtained from the dynamic of the system as:  
\[x_{4}=\dot{x}_{2}l^{2}\text{cos}^{2}(x_{1})(m_{1}+m_{2}). \tag{45}\]  
**Subsystem 4.** For to appreciate the fault in the second actuator (\(f_{a2}\)) is considered the following equations.  
\[\dot{x}_{2} = \frac{x_{4}}{l^{2}\text{cos}^{2}(x_{1})(m_{1}+m_{2})}, \tag{46}\] \[\dot{x}_{4} = U_{y},\] (47) \[y_{a2} = x_{2}. \tag{48}\]  
**Remark 1**.: _Note that for fault decoupling an inspection procedure is used instead of a nonlinear transformation, i.e., for sensor \(i\), the output is selected (omitting all other outputs) as well as all differential equations associated with the states that explicitly appear in the selected output and the ones required to complete the states present in the differential equations selected._  
**Remark 2**.: _The subsystem for sensor fault 2 and actuator 2 is the same, so only one will be used._
## 5 Results
### Validation Experiment  
To strengthen the validation of the method proposed in this paper, we constructed a small dataset consisting of real satellite images. This dataset contains 500 satellite images. Due to the limited number of training samples, the features that the model can learn are also limited, which is both a challenge and a test for the model's performance. We divided the dataset into training, validation, and test sets in a 6:2:2 ratio, resulting in 300 images for training, 100 images for validation, and 100 images for testing. We performed data augmentation on the training set, expanding it to 1800 images, while keeping the validation and test sets unchanged, ultimately obtaining 2000 real satellite images.  
The detection results of YOLOv8s are shown in Figure (a)a, and the detection results of LTSCD-YOLO are shown in Figure (b)b. As can be seen from these figures, in this small real satellite image dataset, the overall mAP50 of LTSCD-YOLO is 84.3%, which is 1.69% higher than YOLOv8s. In solar panel detection, LTSCD-YOLO achieved an mAP50 of 90.0%, 1.58% higher than YOLOv8s; in antenna detection, LTSCD-YOLO achieved an mAP50 of 72.6%, 1.54% higher; and in main body detection, LTSCD-YOLO achieved an mAP50 of 90.2%, 1.92% higher. From the test results, it can be seen that the proposed model performs well on the small real satellite image dataset, further proving the effectiveness of the model proposed in this paper.  
Figure 19: Comparative images of the detection results: (**a**) YOLOv8s detection results and (**b**) LTSCD-YOLO detection results.  
Figure 18: Detection real image: (**a**) Ground Truth; (**b**) YOLOv8s algorithm; and (**c**) ours.
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 4 Evaluation
### Experiments
#### 4.2.4 Analysis of the Results  
We examined the performance of the algorithms in the join tests with different ratios of skewed join keys. The differences in the execution times of the join tests are shown in Figure 12. In the first join test with a high skew ratio (Test 1), the multi-dimensional range partition performed better than the others. The hash-based partition algorithm was the worst, as it was 1.59 times slower than the multi-dimensional range partition and 1.44 times slower than the range-based partition. In the second join test (Test 2), the multi-dimensional range partition was slightly better than the other two algorithms. Nevertheless, the performance of the three algorithms was almost equivalent in the case of the average skew ratio. In the last join test with a low skew ratio (Test 3), the hash-based partition gave a better performance than the others. The multi-dimensional range partition was the worst in this case, as it was 1.7 times slower than the hash-based partition. The range-based partition appeared to have an average performance in comparison with the other two algorithms in the three test cases. Regarding Formula (5), the experimental results were appropriate for the cost models presented above.  
Considering the advantages and disadvantages of the algorithms, with the hash-based partition, the advantage of this algorithm is that it is easy to implement and commonly used in join computation. However, it is not ideal for handling join operations with a high skew ratio. Tuples with the same join keys are hashed to the same reducers, leading to seriously imbalanced tasks. As can be seen, datasets with more skew-join keys had a worse performance; on the contrary, datasets with fewer skew-join keys had a better performance.  
In the case of the range-based partition, the tuples with more skew data were selected for range determination. It is quite easy to determine sub-ranges with a small cost with this  
Figure 12: Execution time of the three algorithms (seconds).  
method; thus, it has been widely used to deal with data-skew problems. This method is more efficient than the hash-based partition algorithm, but it is not a good choice in the case of datasets with a low skew ratio. A limitation of this algorithm is that the determination of the sub-ranges does not take into account the size of the join results; thus, a joining product skew can arise.  
The multi-dimensional range partition overcomes the limitation of the range-based partition by using a partitioning matrix instead of a split vector. The cross products of the sub-ranges represented by cells in the partitioning matrix can be estimated to avoid the oversizing of the join results. With this algorithm, the greater skew ratio of the datasets, the more the performance is improved. Conversely, datasets with fewer skew-join keys incur a higher cost of creating a partitioning matrix, recalculating heavy cells in the partition matrix, and assigning cells to reducers.
## 2 Related Work
Additionally, the algorithm successively refines the correlation parameter after decoding every bit-plane. The authors in [26] attempted to reduce the deviation between the Laplacian statistical distribution model and the small and large residual coefficients by proposing a hybrid distribution correlation noise model. This hybrid model is based on the K-Mediods and Cauchy distributions clustering, which clusters all residual coefficients into small and large coefficients clusters. The small coefficients cluster is K-Mediods modeled toimprove Laplacian distribution, while the large residual coefficients are modeled with Cauchy distribution. On the other hand, the codec in [27] used the parallel LDPC decoding to estimate the correlation noise parameters and decode on the factor graph. This algorithm estimated one parameter for each band. The codec in [11] extended the work of [27] and proposed a recursive variational Bayes factor graph. Furthermore, it deployed a new message-passing algorithm to decode bit-planes corresponding to each band and simultaneously estimates and refines the correlation noise parameter. The correlation noise parameter estimation plays an important role in achieving coding efficiency. In most codecs, the highly computational motion-compensated interpolation (MCTI) [10] is performed for the SI generation, and the resultant motion vectors are utilized for the correlation noise parameter estimation. However, in the codec presented in [15], an empirical study of a phase-based fast frame interpolation algorithm was conducted to effectively generate a low computational and fast SI in DVC when motion vectors are not available. For such codecs, there is a need to design an online CNM framework so it leads to the coding efficient codec framework along with low computational SI-generation feature. In this paper, our main focus is to design an online CNM framework for such codecs that will lead to the development of a full DVC codec with less decoding computational complexity and can compete with DIS in terms of coding efficiency and RD performance. Therefore, in this study, an attempt has been made to design a framework for online estimation of CNM for the Laplacian distribution parameter while considering the mean of the coefficients band.
# A Cooperative MAC Protocol for a M2M Heterogeneous Area Network
## 4 M2M Area Network Design
### IEEE 802.11 MAC Standard
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Release Date** & \begin{tabular}{c} **Frequency Band** \\ **(GHz)** \\ \end{tabular} & **Bandwidth (MHz)** & \
\begin{tabular}{c} **Transmission Data** \\ **Rates Mbits/s** \\ \end{tabular} &
\begin{tabular}{c} **Transmission** \\ **Techniques** \\ \end{tabular} \\ \hline
802.11 & June 1997 & 2.4 & 22 & 1,2 & D6 SS 1, FHSS 2 \\ \hline
802.11a & September 1999 & 5 & 20 & 6,9,12,18,24,36,48,54 & OFDM 3 \\ \hline
802.11g & June 2003 & 2.4 & 20 & 6,9,12,18,24,36,48,54 & OFDM \\ \hline
802.11n & October 2009 & 2.4, 5 & 20: & 72 \(\pm\) 20 722 & MIMO 4-OFDM \\  & & 40: & 15 105 & 4 4 streams \\ \hline
802.11 ac & December 2013 & 5 & 40: & 15 10 200 & MIMO-OFDM \\  & & & 80: & 32 5 \(\pm\) 633 & 8 streams \\ \hline
802.11 ad & December 2012 & 60 & 2160 & Up to 7000 & OFDM, Single carrier \\ \hline
802.11 ah & October 2013 & 0.9 & 1: & 0.3 to 4.0, 0.15 (MCS10) & \\  & & & 2: & 0.65 to 7.8 & \\  & & & 4: & 13 \(\pm\) 18 & MIMO-OFDM \\  & & & 8: & 2.925 to 39 & \\  & & & 16: & 5.85 to 78 & \\ \hline
802.11p & July 2010 & 5.9 & 10: & 3 to 27 & OFDM \\ \hline \hline \end{tabular}  
* 1: DSSS: direct sequence spread spectrum. 2: FHSS: frequency hopping spread spectrum. 3: OFDM: orthogonal frequency division multiplexing. 4: MIMO: multiple input multiple output.  
\end{table} 
Table 2: IEEE 802.11 standards development history. developed in order to optimize the RAW architecture so that the QoS of different applications can be supported. Other benefits of the emerging 802.11 ah standard are: (i) short maximum header size of 28 bytes instead of 40 bytes, the MAC layer also supports a short six-byte header; (ii) supports a transmission range up to 1 km for a single hop link; and (iii) the suggested transmission power level is between less than 10 mW and 1 W depending on country regulations. The emerging standard has been designed targeting M2M networks; however, further research work needs to be carried out to optimize algorithms and network architecture.
## 2 Characteristics of the System Model and Problem Description  
The targeted model in this study was located in Kabul city (capital of Afghanistan). Triple energy sectors, generation, transmission, and distribution systems, suffer technical and economic losses. After a long-term political instability and lack of maintenances, Kabul city distribution networks demonstrate many problems; namely, transformer no-load loss, imbalance between primary and secondary distribution systems in terms of power transfer, scattered distribution transformer from gravity center of load, unbalance reactive power and distributed three phase supply, lack of protection devices, long length of customers cables, use of nonstandard equipment, etc. [26]. Reports pertain 25-40% losses to distribution systems that require a viable solution and proper management of technical and economic losses [27]. Meanwhile, an increasing population growth forces distribution networks to operate close to their stability limit within maximum expandability [28]. Definitely, system expansion under stressed voltage control condition has a direct impact on voltage profile and power losses [29]. For this case study, voltage deviation at distribution level is out of acceptable range; whereas, at the time of peak load demand, it reaches 15% voltage deviation.  
Figure 1 shows the proposed 20 kV distribution system consisting of 22 buses and 21 lines that are considered a real model of simulation. Table 1 illustrates the mentioned distribution system transmission lines parameters. The proposed model supplies residential, commercial, and industrial consumers. This system consists of transformer stations (TSs) and junction station (JS-6) that feeds from the (110/20 KV, 50 MVA Breshna Kot substation).  
Figure 1: Breshna Kot distribution network model.
## 1 Introduction
Most current MOEAs can be classified into three categories. The first category is dominance-based methods, which include various popular algorithms such as NSGA-II [8], GrEA [9], PICEA [10], and RVEA [11]. The second category is indicator-based methods [12]. The hypervolume is the most popular indicator used in these algorithms. The third category is decomposition-based methods. These algorithms decompose a multiobjective optimization problem (MOP) into a set of single-objective subproblems or a set of simple multiobjective subproblems and use a population search method to solve these subproblems in a collaborative manner. The most representative decomposition-based algorithm is MOEA/D [13], of which many variants have been developed. Various MOEA applications have emerged for engineering but seldom have research efforts been devoted to OSP. [PERSON] and [PERSON] [14] considered three influence factors (i.e., the anti-noise ability, linear independence degree, and redundancy rate) to optimize sensor configuration with a cross-sea bridge structure. [PERSON] et al. [15] proposed an information-entropy-based multiobjective OSP to detect damage to bridges subject to ship collisions. [PERSON] et al. [16] introduced two objective functions, namely response covariance sensitivity and response independence, by using NSGA-II. Particle swarm optimization (PSO) [17], which was originally proposed for single-objective continuous optimization problems, is a population-based optimization method. [PERSON] et al. (2018) and [PERSON] et al. (2019) have applied PSO-based dominance methods to solve MOPs. Decomposition-based methods have also been proposed by many researchers ([PERSON] et al., 2019; [PERSON] et al., 2020; [PERSON] et al., 2020). In this paper, we applied a multiobjective approach using decomposition-based multiobjective particle swarm optimization (DMOPSO) to solve the complex, nonlinear, and discrete optimization problem of OSP. This paper is organized as follows: Section 2 presents the research methods and the three objective functions to be optimized. The DMOPSO algorithm and decision-making strategy are given in detail. Section 3 presents the validation of the proposed algorithm and comparison of the OSP solution on Canton Tower with others in the references. Section 4 shows the experimental validation of the proposed OSP method on a laboratory aluminum frame structure. Finally, concluding remarks are presented in Section 5.