esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Multiple Factors Affecting the Accuracy of Multiclass Skin Disease Classification
## 3 Skin Disease Classification
### Batch Normalization and Mini-Batch Size  
In order to overcome the overfitting problem, where the network performs very well in the training set but poorly in the test set, batch normalization layers are used. Batch normalization can help train the network faster and in a more stable manner, less sensitive to the initial random weights, and it is said to be able to solve the internal covariate shift problem. In Models II, III, and IV, a batch normalization layer is attached to each of the convolutional layers. The skip connection in ResNet50, as well as in Models II, III, and IV, is used to mitigate the gradient explosion problem caused by using batch normalization. In training the deep neural network, batch normalization standardizes the inputs to a layer for each mini-batch. By transforming the data to have a mean of 0 and a deviation of 1, the distribution of the inputs during the weight update will not change dramatically, and the training of the network can be stabilized and accelerated. The generalization error can also be reduced.  
The choice of hyperparameter mini-batch size is also important. If the mini-batch size is too small, the distribution of the mini-batches will be largely different from the actual dataset. The differences in the standardized inputs between training and using the model after training can result in noticeable differences in performance. In this paper, different mini-batch sizes are compared for the evaluation of the deep networks on a skin disease dataset.
## 4 Discussion  
### Performance of Satellites  
Overall, compared to Landsat 5 and 7, Sentinel-2 and Landsat 8 demonstrate superior performance, with RMSLE values ranging from 0.18 (ML8-WLO and ML8-OM) to 0.49 (ML8-AI) for Landsat 8, and 0.15 (M\({}_{\text{S2-EH}}\)) to 0.38 (M\({}_{\text{S2-All}}\)) for Sentinel-2. The average RMSLE for all seven scenarios is 0.34 for Landsat 8 and 0.27 for Sentinel-2, suggesting a slightly better performance for Sentinel-2. In contrast, Landsat 5 and 7 have RMSLE ranges of 0.21-0.41 and 0.18-0.52, respectively. The other performance metrics similarly imply the superior performance of Sentinel-2, followed by Landsat 8. For example, Sentinel-2, Landsat 8, 7, and 5 have R\({}^{2}\) ranges of 0.16-0.52, 0.08-0.39, 0.04-0.38, and 0.01-0.38, respectively. The comparable performance of Landsat 5 and 7 is consistent with the similar sensor configurations of both satellites. The weak performance of Landsat 5 and 7 is likely due to sensor-specific radiometric capabilities resulting in lower SNR. It should also be noted that the Sentinel-2 models were trained on less data than those for other satellites, mainly because of its more recent launch in mid-2015 as well as the scarcity of in situ measurements during the years of COVID-19 restrictions.  
Among the four satellites, the Chl-\(a\) concentrations predicted by the Landsat 7 models tend to exhibit the lowest slopes relative to the in situ concentrations, averaging 0.14 across all seven data categories (Figure 6). The average slope is slightly better for Landsat 5 (0.19) and Landsat 8 (0.22), but distinctly superior for Sentinel-2 (0.40). In principle, a perfect match between satellite-derived and in situ-measured Chl-\(a\) should yield a 1:1 slope. Given this, models such as ML7-AI, ML7-HH, ML7-SS, as well as ML5-HH and ML5-SS, with slopes less than 0.04, can be considered entirely ineffective. However, even for the better performing Sentinel-2 models, the slopes are less than the theoretical 1:1 slope. This means that above and below a threshold value a given model underpredicts and overpredicts the Chl-\(a\) concentration, respectively. For the HH plus EH categories this threshold is approximately 12.0 \(\upmu\)g/L, while for the WLO plus OM categories it is about 1.2 \(\upmu\)g/L. One factor contributing to the less than 1:1 slope could be the different spatial scales of the satellite versus in situ Chl-\(a\) concentrations. While the former average Chl-\(a\) concentrations cover entire pixels, the in situ measurements sample the small-scale heterogeneity in phytoplankton distributions in the lake's surface waters. The range of the in situ Chl-\(a\) (point) measurements can thus be expected to exceed that of the satellite-derived concentrations. On a model-predicted versus in situ Chl-\(a\) concentration plot (Figure 6), this would translate in a slope of less than one.
## 4 Self-Selective Routing (SSR)  
In this section, we provide a brief explanation on SSR [40; 41; 51; 52] and SHR [24; 53]. In SSR, the network nodes know their distance through the mechanism of route request and reply. In this scheme, the nodes broadcast packets with an estimated distance to the destination neighbors. The node then employs the SSR protocol to determine autonomously the node that should transmit a given packet to the next node using a prioritized transmission backup delay. Upon receiving the packet, the node then schedules further packet transmission immediately with a random delay that is proportional to its path length from the destination. The transmission back-off delay is expressed using the mathematical expression given in Equation (1) as [40; 41; 51; 52]:  
\[Trans\_Back\_off\_Delay=\begin{cases}\beta*\left(\alpha-\ \alpha_{expected}*\text{ Random(0,1)}\right)\ \text{if}\ \alpha\ >\ \alpha_{expected}\\ \frac{\beta}{\alpha_{expected}-\alpha+1}*\text{Random (0,1)}\ \text{if}\ \alpha\ \leq\ \alpha_{expected}\end{cases} \tag{1}\]  
Figure 1: Modular approach to fault propagation in wireless sensor network.  
where \(\alpha\) is the hop count of the destination node, and \(\alpha_{expected}\) is the sender node expected hop counts in the destination reply packets. The random function is the random number generator that produces real values uniformly distributed between the interval [0,1]. This random function is used to randomize the delays and reduce packet collisions. The parameter \(\beta\) is a scaling factor used to stretch the random delay values generated by the randomize function. Equation (1) assigns a \(Trans\_Back\)-\(off\_Delay\) greater than \(\beta\) to those nodes with hop counts that are greater than \(\alpha_{expected}\). In this case, the smaller the value of \(\alpha\), the least is the Trans\(\_\)Back\(off\_\)delay, and the node has the highest probability of transmitting the packet [40,41,51,52]. The limitations of SSR are:  
1. The SSR routine computes a node's transmission back-off delay in continuous time, in contrast to slotted time. The disadvantage is that it has a higher rate of packet collisions.
2. Because of non-zero probability, the packets may travel a longer route while a shorter route exists that increases network delay.
3. In SSR, there is no route repair mechanism for propagating packets around faulty routes.
## 5 Results and Analysis
### Discussion
Furthermore, rules-based strategies should be attuned to changes of individual drivers since the assessment of workload levels may diverge from one user to another, owing to differences in the multi-dimensional properties of cognitive load [78]. One promising solution for mitigating this limitation, which will be a focal point of future research endeavors, consists of revolutionizing A-HMIs by applying cloud computing and Internet of Things (IoT) technologies of ICVs [79]. By harnessing these technologies, it can be possible to collect and transmit driver data, as well as other relevant information from the vehicle and environment, to a cloud-based platform. This platform can then be utilized to construct a comprehensive data-driven model of driver workload [80]. Using this model, it will become feasible to dynamically assess driver distraction and establish correlations between application requests and their impact on workload over time.
## 2 Review: Regional-Scale Assessment of Buildings' Seismic Vulnerability
### Analytical Vulnerability Assessment  
Amid improvements in advanced earthquake engineering as well as computer science, computational structural modeling has served as a powerful tool for assessing buildings' response to ground shaking. Static pushover analysis, for example, provides a practical means for evaluating inelastic structures' displacement responses to earthquakes of given intensities [9; 10; 11; 12]. This type of analysis generates capacity curves, which express seismic capacity in terms of the horizontal displacement of a building under increasing lateral load [13]. The seismic-demand spectrum, on the other hand, is generated based on the geotechnical and seismicity characteristics of the building'slocation. A building's performance point, found at the intersection of its capacity curve with the seismic-demand curve of its site, refers to the maximum displacement it can withstand under the given seismic-demand curve. Once its performance point is known, the failure mechanism of the building's structure can be closely investigated, based on the ductility capacity of all structural elements under the calculated maximum displacement. Therefore, a building's capacity curve is critically important to the representation of its seismic vulnerability.
# A Method of Estimating an Object's Parameters Based on Simplication with Momentum for a Manipulator
## 1 Introduction
Additionally, the force and torque data required for estimation depend on acceleration data, which are typically obtained by differentiating velocity with respect to time. This differentiation process amplifies noise, making precise estimation more difficult. To mitigate these issues, researchers have attempted to use high acceleration to reduce estimation error. However, increasing acceleration often results in larger motion during estimation, raising the risk of collisions between the object and the robot's links or surrounding structures. Moreover, if the object has substantial inertial parameters, achieving the desired acceleration becomes challenging. Even if high acceleration is attained, the resulting inertial forces could weaken the coupling between the robot and the object, potentially causing the grasp to slip or even damaging the object during estimation. This paper introduces a novel approach for estimating the inertial parameters of a grasped object by simplifying the estimation model using momentum. By integrating force and moment data over time and assuming a zero initial condition, the method reduces noise and derives linear and angular momentum. To address the underconstrained nature of the estimation model, the object's parameters are estimated separately, ensuring that the number of parameters aligns with the available dynamic equations. These parameters include the object's mass, center of mass, and inertia tensor. The estimation process is structured so that each parameter is independently determined using distinct linear and rotational motions, with rotational motions applied separately along each axis. The key contribution of this study is its ability to achieve accurate parameter estimation with significantly smaller motion ranges, velocities, and accelerations compared to previous research. The effectiveness of the proposed method is validated through comparative analyses against established parameter estimation techniques. The subsequent sections of the paper are organized as follows: Section 2 formulates the estimation model, while Section 3 describes the suggested parameter estimation method. The evaluation of the suggested method is presented in Section 4, and the results of the simulations are discussed in Section 5.
## 2 Methodology
### Single-Fidelity Universal Kriging  
Kriging [5] is a Gaussian random process realization to estimate a function value in a sample location, \(\hat{f}(x)\):  
\[\hat{f}(x)=\beta^{T}h(x)+Z(x) \tag{1}\]  
where \(\beta^{T}h(x)\) is the mean value or trend of the Gaussian process and \(Z(x)\) represents a stationary Gaussian process with zero mean, variance \(\sigma^{2}\) and the covariance of two locations, \(x_{i}\) and \(x_{j}\), is given by \(Cov(Z(x_{i}),Z(x_{j}))=\sigma^{2}R(x_{i},x_{j},\theta)\) where \(R\) is an auto-correlation function, which is only dependent on the distance between the two locations due to stationarity and a hyper-parameter, \(\theta\). In this work Wendland C4 [36] auto-correlation functions are employed. The most general kriging formulation is called universal kriging, in which the mean is given by a sum of \(P+1\) pre-selected trend functions, \(h_{k}(x)\), that is  
\[\beta^{T}h(x)=\sum_{k=0}^{P}\beta_{k}h_{k}(x) \tag{2}\]  
where \(\beta_{k}\) are the trend coefficients. In ordinary kriging the mean has a constant but unknown value, so \(P=0\), \(h_{0}(x)=1\), and \(\beta_{0}\) is unknown.  
Given a value for \(\theta\) the calibration of the universal kriging model parameters can be computed analytically using an empirical best linear unbiased estimator (BLUE):  
\[\beta(\theta) = \Big{(}H^{T}R^{-1}H\Big{)}^{-1}HR^{-1}Y \tag{3}\] \[\sigma^{2}(\theta) = \frac{1}{N}(Y-H\beta)^{T}R^{-1}(Y-H\beta) \tag{4}\]  
where \(Y=\{f(x_{i}),i=1,\ldots,N\}\) are the \(N\) training point observations and \(H_{ij}=h_{j}(x_{i})\) is the information matrix.  
The optimal \(\hat{\theta}\) is determined here by maximization of a log-likelihood function via a genetic algorithm (for more details see [PERSON] et al. (2016)):  
\[\hat{\theta}=\underset{\theta}{arg\,max}\Big{[}-N\ln(\sigma^{2})-\ln(det(R)) \Big{]} \tag{5}\]  
and the resulting \(\beta(\hat{\theta})\) can then be used to predict the kriging response via  
\[\hat{f}(x)=\beta^{T}h(x)+r(x)^{T}R^{-1}(Y-H\beta) \tag{6}\]  
where \(r_{i}(x)=R(x,x_{i},\hat{\theta})\) is the correlation between a new sample location, \(x\), and an existing training point, \(x_{i}\).
# Target Localization via Integrated and Segregated Ranging Based on RSS and TOA Measurements
###### Abstract  
This work addresses the problem of target localization in adverse non-line-of-sight (NLOS) environments by using received signal strength (RSS) and time of arrival (TOA) measurements. It is inspired by a recently published work in which authors discuss about a _critical distance_ below and above which employing combined RSS-TOA measurements is inferior to employing RSS-only and TOA-only measurements, respectively. Here, we revise state-of-the-art estimators for the considered target localization problem and study their performance against their counterparts that employ each individual measurement exclusively. It is shown that the hybrid approach is not the best one by default. Thus, we propose a simple heuristic approach to choose _the best_ measurement for each link, and we show that it can enhance the performance of an estimator. The new approach implicitly relies on the concept of the critical distance, but does not assume certain link parameters as given. Our simulations corroborate with findings available in the literature for line-of-sight (LOS) to a certain extent, but they indicate that more work is required for NLOS environments. Moreover, they show that the heuristic approach works well, matching or even improving the performance of the best fixed choice in all considered scenarios.  
target localization; integrated measurements; received signal strength (RSS); time of arrival (TOA); critical distance +
Footnote †: journal: Sensors
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 3 Routing on Wireless Sensor Networks
### Cluster Purposes  
Improving the lifetime of the networkthe success of an SN deployment is highly dependent on the network's longevity; as a result, it is important to study routing algorithms that minimize energy waste while maximizing throughput.  
Fault toleranceMany methods are developed to deal with the problem of a node failing. Proxy CHs are used or CH rotation is used (when both nodes fail) to deal with this problem. When developing the clustering algorithm, fault tolerance was one of the primary considerations.  
Fill handlingWhen outlining the goals of a clustering protocol's design, fill handling is an important one. Overloading the CHs will quickly drain their energy reserves and is therefore strictly prohibited. Thus, it is crucial that each cluster's nodes be evenly distributed as CHs carry out the work of data aggregation or signal processing tasks.
## 4 Simulation and Results
### The IEEE Test Systems (33-Bus and 69-Bus)
Hence, the batteries will not be charged anymore and remain idle until 18:00. At 18:00, the batteries are to some extent discharged. However, they are charged again at 19:00. With regard to the high level of consumption at 20:00, the batteries deliver all of their stored energy to the microgrid. The diesel generator is connected to the microgrid at 8:00, and it is disconnected at 9:00. It continues until 19:00. Again, at 19:00, the diesel generator starts to supply the load until the end of the time horizon of this study (24:00). With respect to the high level of solar generation, a considerable share of this production is sold to the distribution grid. The obtained economic benefit has improved the objective function. As it is clear, the microgrid is so designed that the loads are supplied entirely and load shedding has not occurred. The microgrid behavior in August is depicted in Figure 9.  
Figure 8: The comparison of the convergence behavior of different methods in seeking for the best solution for the 69-bus distribution test system. Figure 7: The comparison of the convergence behavior of different methods in seeking for the best solution for the 33-bus distribution test system. The power purchased from the main grid is shown in Figure 7. As it shows, during the day hours when there is solar energy, the demanded power from the upstream grid is drastically reduced. It should also be noted that the network losses and the corresponding costs were 1061 kW and 55.86 \(\upmu\) per day, which reached the values of 956.79 kW and 51.78 \(\upmu\) per day, after the installation of microgrids in the network, respectively. Figure 8 shows the amount of load shedding in the distribution network at different times of the day. The results figure out that during the hours when there is solar energy, solar unit injects power into the distribution network, the amount of shed load has reached zero, which is very beneficial in terms of cost and efficiency for the distribution network as well as reliability improvement from microgrid point of view. The model is also tested on the 69-bus test system. The results imply that the bought power from the main grid by the 69-bus distribution network is illustrated in Figure 10. As it is evident, during the hours that the PV panel can generate power, the absorbed power from the sub-transmission grid is considerably diminished. The power losses of the distribution grid and the subsequent cost of them were about 1350 kW equivalent with 65.76 \(\upmu\)/day before the implementation of the microgrid scheme, and it is alleviated to 989.32 and 56.28 \(\upmu\)/day after implementation of the microgrid. In Figure 11, the hourly shed load in the distribution network is shown. It is obvious that whenever the PV generation exists, all of the loads in the distribution grid are satisfied, and no load-shedding measure in the distribution network is executed. This matter significantly improves the performance of the distribution network.
## 2 Materials and Methods
### Models and Tools
#### 2.4.2 GDM-DEA Method  
GDM-DEA adds a new DMU average decision unit \(j_{n+1}\), by taking the average value of each input and output of \(n\) evaluated DMUs as the input and output value of \(j_{n+1}\). If the evaluated cultivation measures \(j_{0}\) are removed, the remaining \(n-1\) original cultivation measures and mean cultivation measures \(j_{n+1}\) can form a new group with \(n\) evaluated cultivation measures.  
If \(j_{0}\) has a great influence, the efficiency of the new group will change greatly after removing \(j_{0}\); otherwise, it will not change or will change very little. In short, the removal and retention of \(j_{0}\) can reflect the degree of its impact on the efficiency of new groups, and can also reflect the role of \(j_{0}\).  
Each DMU also has \(m\) kinds of \"input\" and \(n\) kinds of \"output\"; \(x_{ij}\) denotes the \(i\)th input of the \(j\)th DMU; \(x_{ij_{0}}\) denotes the \(i\)th output of the \(j_{0}\)th DMU; \(v_{i}\) represents the corresponding weight of \(x_{ij}\); \(y_{\tau j}\) denotes the \(r\)th output of the \(j\)th DMU; \(y_{\tau j_{0}}\) denotes the \(r\)th output of the \(j_{0}\)th DMU; \(u_{r}\) represents the corresponding weight of \(y_{\tau j}\). For \(n\) DMUs, \(\overline{x}_{i}\) denotes the average value of the \(i\)th input, and \(\overline{y}_{r}\) denotes the average value of the \(r\)th output. \(E_{i_{0}}\) represents the GDM-DEA efficiency of \(j_{0}\). The below expression represents the GDM-DEA model used for calculating the GDM-DEA efficiency value of the \(j_{0}\)th DMU.  
\[(P_{GDM})\text{s.t.}\left\{\begin{array}{l}\max\sum\limits_{r=1}^{s}u_{r} \overline{y_{r}}\\ \sum\limits_{i=1}^{m}v_{i}x_{ij}\geq\sum\limits_{r=1}^{s}u_{r}y_{rj}\,j=1,2, \cdots,n,j\
eq j_{0}\\ \sum\limits_{i=1}^{m}v_{i}\overline{x_{i}}=1\,i=1,2,\cdots,m\\ v_{i},u_{r}\geq 0\,r=1,2,\cdots,s\\ \overline{x_{i}}=\frac{1}{n}\sum\limits_{j=1}^{n}x_{ij},\overline{y_{r}}= \frac{1}{n}\sum\limits_{j=1}^{n}y_{rj}\end{array}\right. \tag{2}\]  
\[E_{j_{0}}=\frac{\sum\limits_{r=1}^{s}u_{r}y_{\tau j_{0}}}{\sum\limits_{i=1}^{s} v_{i}x_{ij_{0}}}\,\,\,(j_{0}=1,2,\cdots,n) \tag{3}\]  
Firstly, taking the optimal efficiency of the average decision unit \(j_{n+1}\) as the goal, and the efficiency of the \(j\)th DMU (\(j=1,2,\ldots,n\), but \(j\
eq j_{0}\)) as the constraint condition, \(P_{GDM}\) should be solved to obtain weights \(v_{i}\) and \(u_{r}\). Then, the weights can be regarded as the weights of the corresponding input and output indicators of \(j_{0}\). Lastly, the GDM-DEA efficiency value of \(j_{0}\)--\(E_{j_{0}}\) can be calculated. If \(E_{j_{0}}\geq 1\), the \(j_{0}\)th DMU is effective in the GDM-DEA model; otherwise, the \(j_{0}\)th DMU is ineffective in the GDM-DEA model.
# A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
## 3 Proposed Approach
### The RPL Protocol
\[l(ni,ap)>l(ni,aj)\ \forall aj\in l \tag{7}\]  
In conclusion, the construction of the network for each \(ni\) node can be defined as follows:  
\[C=\{\forall ni\in N,\ \exists\ ap\in A(ni)\}{:}l(ni,ap)>l(ni,aj)\ \forall aj\in l\} \tag{8}\]RPL protocol is implemented in 4 stages: (a) setup stage; (b) routes establishment stage; (c) data communication stage; and (d) path reconstruction stage. At the route Setup stage, the RPL protocol (in its original version) uses the OF0 to enable the nodes to select the parent nodes (default route), based on information from the rank and on the number of hops of a given node for the sink, as described in Equation (9):  
\[R(N)=R(P)+rank\_increase \tag{9}\]  
where:  
* \(R(N)\), is the node new rank;
* \(R(P)\), is the preferred parent rank;
* \(rank\_increase\), is a variation factor (delta) between the node and the parent rank.