esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Method
### Pedestrian Detector for Crowded Scenes  
Incorporating the proposed MA-NMS and ATTB, a pedestrian detector for crowded scenes is constructed on the basis of Faster R-CNN, as shown in Figure 3. Notably, the detection branch of the detector remains unchanged. Subsequently, the detector is trained on two datasets separately, and extensive experiments are conducted to verify the effectiveness of our proposed method.  
During the training phase, the constructed detector optimizes the entire network using a weighted loss function \(L\), which is expressed in Equation (12).  
\[L=\lambda_{1}L_{rpn\_cls}+\lambda_{2}L_{rpn\_reg}+\lambda_{3}L_{box\_cls}+ \lambda_{4}L_{box\_reg}+\lambda_{5}L_{density}+\lambda_{6}L_{count}, \tag{12}\]  
where \(\lambda_{i}\) is employed to balance the gradient magnitude of the corresponding loss. \(L_{rpn\_cls}\) and \(L_{box\_cls}\) are computed using cross-entropy loss, and the \(L_{rpn\_reg}\) and \(L_{box\_reg}\) are calculated using smooth \(L_{1}\) loss, which is in line with [8]. Furthermore, the detector treats the detection of density and count attributes as regression tasks, employing smooth \(L_{1}\) loss to calculate \(L_{density}\) and \(L_{count}\).  
During the inference phase, MA-NMS is utilized to suppress the numerous duplicate proposals based on the count and density attributes. As illustrated in Figure 3, proposals with bounding boxes that overlap with that of the current pedestrian will be removed by strong suppression. Additionally, proposals belonging to occluded pedestrians are retained by weak suppression. Eventually, our pedestrian detector obtains more accurate predictions for pedestrian detection in crowded scenes.
# Design and Analysis of Systematic Batched Network Codes
## 3 Systematic Outer Codes
### Computation Cost
#### 3.3.2 Decoding Computation Cost  
For the systematic outer code, the decoding computation cost depends on the number \(K_{m}\) of message packets received by the destination node. When \(K_{m}=K\), i.e., all the message packets are received, no computation is required for decoding. When \(K_{m}<K\), the systematic code decoder needs to execute DEC, which has a computation cost \(c\), and \(\text{ENC}^{*}_{n_{s}}\), which takes computation cost \(b\) on average to generate a packet. As \(K_{m}\) message packets have been received, we only need to use \(\text{ENC}^{*}_{n_{s}}\) to generate the remaining \(K-K_{m}\) message packets. Therefore, the overall decoding computation cost is \((K-K_{m})b+c\approx 2c-K_{m}b\). When \(K_{m}\) is close to \(K\), the systematic outer code decoding computation cost is close to the ordinary outer code decoding. In the worst case, i.e., \(K_{m}=0\), the systematic outer code decoding computation cost is doubled compared with the ordinary outer code decoding. See an illustration in Figure 4.  
To illustrate how the decoding computation cost affects the operation at the destination node, we consider that coded packets are received one-by-one with a unit time interval between two consecutive packets. We assume that the ordinary outer code decoder starts decoding at time \(K\) and takes additional \(\Delta\) time to decode all the message packets. When \(K_{m}=K\), all the message packets are decoded at time \(K\). When \(K_{m}<K\), the systematic outer code decoder executes DEC at time \(K\) and starts to use \(\text{ENC}^{*}_{n_{s}}\) from time \(K+\Delta\) to generate the \(K-K_{m}\) message packets that have not been received. In the worst case, where  
Figure 3: Illustration of the encoding computation cost for the ordinary outer code and the systematic outer code. (**a**) shows the encoding computation cost of generating the first \(k\) coded packets. For the ordinary outer code, the computation cost increases linearly with \(k\). For the systematic outer code, the computation cost is \(0\) when \(k\leq K\). The jump in the computation cost after time \(K\) is used to execute \(\text{DEC}^{*}_{n_{s}}\). (**b**) illustrates the number of encoded packets generated over time. The curve “systematic-1” is for the systematic outer code encoder when the message packets arrive one-by-one in each unit time. The curve “systematic-2” is for the systematic outer code encoder when the message packets arrive all at time \(K\). From time \(K\), these two curves overlap. The ordinary outer code behaves in the same way for both message packet arrival models.  
\(K_{m}=0\), the systematic outer code decodes all the message packets at time \(K+2\Delta\). See an illustration in Figure 4b.
# Development of a Thermal Analysis Tool for Linear Machines
## 5 Discussion  
It is clear from Figures 13 and 14 that the thermal model that is described in this article is able to predict the thermal performance of different types of linear machines precisely. The model can also predict the thermal performance of the machines in different layouts.  
Figure 14: LSFPM thermal test result vs model response. (**a**) Horizontal test. (**b**) Vertical test.  
Figure 13: LIM thermal test result vs model response. (**a**) Horizontal test. (**b**) Vertical test. (**c**) Vertical test with aluminium plate at 135 W. (**d**) Vertical test with aluminium plate at 195 W.  
Although the model calculates the values of the thermal resistors based on the geometric parameters of the machine, some parameters must be calibrated.  
The main calibration parameters are the quality of the impregnation, and the convective resistances of the core and the end winding. The value of the impregnation quality has to be coherent with the fabrication process of the machine. [PERSON] et al. analyze the parameter sensitivity of the thermal response of some totally-enclosed fan-cooled induction machines in Reference [4]. For the impregnation goodness factor, the authors report values between 0.4 and 0.5 in machines where the impregnation process is not optimized. Thus, this value is a good starting point when there is no previous reference about the impregnation quality of the employed process. If more advanced techniques, such as vacuum impregnation, are adopted, the impregnation quality can be enhanced up to a value of 1 [15].  
When calibrating the model, the impregnation quality is used to define the difference between the average temperatures of the copper and the primary core. However, a higher goodness factor will also increase the temperature of the primary core. Thus, if the temperature of the primary core gets too high, the convective heat transfer of the end windings has to be increased to reduce the heat flow through the core.  
The modification of the convective heat transfer coefficients of the core is another calibration alternative. If the temperature of the core is too high, there are two ways to lower its value. The first one is to enhance the convective heat transfer coefficient of the primary core. This will increase the thermal jump from the copper to the core. If the thermal jump gets too high, then the end winding convection must be corrected.  
If the resultant correction factors for the convective heat transfer are too high or too low, the impregnation quality should be reviewed. As a reference, [PERSON] et al. [4] apply correction factors in a range of 0.87 to 1.35 to the convective heat transfer of the models of five different induction motors.  
There is no such thing as a generic mechanical system. The mechanisms are specific to each machine and its application. Therefore, the model cannot predict the influence of the external structure of the machine. In this case, the increased heat-flow due to the aluminium plate of the LIM and the mechanical system of the LSFPM are modeled as a reduced convective thermal resistance in the surface of the yoke.
# Load Forecasting with Machine Learning and Deep Learning Methods
## 3 Methodology
### Modeling
#### 3.2.2 Support Vector Regression  
The learning of the support vector regression is carried out to approximate the input data to a regression line considering a threshold. In this model, the trend line that best fits the data is called the hyperplane, and the lines delimiting the threshold are the boundary lines [40; 52]. Thus, in SVR, the model maps the training data using a series of mathematical equations, known as kernels, to obtain the hyperplane containing the largest amount of input data within the boundary lines. The radial basis function (RBF) is a very frequent kernel similar to the Gaussian distribution [53; 54] (5).  
\[K(x_{1},x_{2})=exp\left(-\frac{\sum_{k=1}^{n}(x_{1,k}-x_{2,k})^{2}}{2\sigma^{2 }}\right) \tag{5}\]  
where \(\sigma\) is the variance, \(x_{1,k}\) is the point being evaluated, \(x_{2,k}\) is the orthogonal representation of \(x_{1,k}\) in the hyperplane, and \(K(x_{1},x_{2})\) represents the kernel between the set of points to be evaluated, i.e., \(x_{1}\) and its orthogonal representation \(x_{2}\).  
The main difference between this model and typical linear regression is that the objective function seeks to fit the data within a range, rather than minimizing the error between forecasted and real data (empirical risk minimization). This feature allows SVR to represent linear and non-linear functions [36; 43]. Thus, denoting the slack margin of the model as \(\xi\), the objective function \(J\) can be defined as in (6):  
\[J=\frac{\sum_{j=0}^{m}\theta_{j}^{2}}{2}+c{\sum_{k=1}^{m}}|\xi_{k}| \tag{6}\]  
where \(m\) is the number of independent variables, and \(c\) is a hyperparameter that represents the regularization. Therefore, if \(c\) increases, the tolerance of out-of-bound values also increases. On the other hand, if \(c\) approaches 0, the tolerance does too, which simplifies the problem and, therefore, does not consider the effect of slack [55].  
SVR is a powerful ML algorithm that can be beneficial for electricity load forecasting due to several reasons. The main ones are presented below:  
* Non-linearity: It can effectively capture non-linear relationships between electricity consumption and important features.
* Robustness to outliers: Due to its structure, it is less sensitive to outliers.
* Tunability: It has parameters that can be adjusted to control the balance between model complexity and generalization to adapt better to the fluctuations in electricity data.
* High-dimensional data: It can handle a large number of features without sacrificing performance.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
### America
#### 4.2.1 The United States
Reproduced with the permission of [PERSON], “Utility-Scale Smart Meter Deployments: Building Block of the Evolving Power Grid”; published by Edison Foundation, 2014. Company utility installed 4.2 million _SM_s equipped with _RF_ mesh technology in 2014. The objective of the deployment integrates advanced metering, communications, and other appliances to provide customer service at reduced operating costs [62]. Other remarkable deployments are: Oncor utility with 3.3 million installed _RF_-based _SM_s and focused on customer services, power quality monitoring, reliability, and outage prevention [67]; and CenterPoint Energy utility, with 2.2 million _RF_-based _SM_s in 2012, with the aim of improving customer service and remote connections/disconnections and enhancing system reliability [68]. Additional _Smart Metering_ deployments in the U.S. are discussed in [62]. U.S. utilities are now focused on integrating and optimizing information gathered by _SM_s to provide benefits and new capabilities to customers and system operators. Four main areas can be distinguished [62]:  
* Systems integration: the introduction of outage and distribution management systems provides enhanced outage management and restoration services as well as improved distribution system and device monitoring. * Integration of new resources: _SM_s position the grid as platform for the integration of distributed energy resources (DG, DS, EVs, microgrids, _etc._). * Operational savings: remote activities (reading, connection/disconnection) and the reduction of energy theft are some financial benefits of _SM_s. * New customer services: _SM_s have enabled services to end-users such as automated budget assistance and bill management tools, energy use notifications, smart pricing, and demand response programs.
# Stimuli-Responsive Photonic Crystals
## 4 Active Tuning of the Photonic Band Gap
### Light Stimuli  
Incorporating a photosensitive material [75; 118; 119; 120] or a dye [77; 121] inside a photonic crystal structure allows for tuning of its optical properties upon exposure to light stimuli. Light can change the refractive index or lead to a modification of the structural properties. For instance, PhCs can be used like a casing for liquid crystals and they will then be stimulated by light [50; 51; 76]. [PERSON] et al. fabricated an optically switchable SiO\({}_{2}\)/ITO 1D photonic crystal. Through a UV-light photodoping process, it is possible to tune the indium tin oxide (ITO) plasmonic response in the near-infrared range and translate the effect to the visible light range, switching the optical properties of the device [8].
## 5 Conclusions  
In this paper, we propose a hybrid approach for developing a CDSS aimed at determining the appropriate timing for changing the treatment plan for patients with AMD based on the analysis of OCT images. The architecture of our hybrid classifier includes a global classifier, several local binary classifiers, and a regressor. This developed architecture effectively addresses challenges commonly faced by machine learning models that utilize medical datasets collected during clinical practice. Specifically, it deals with visual similarities and overlaps among predictors in medical images, particularly the difficulty in detecting the intermediate stage of AMD amid early and late stages. It also addresses class imbalance, which presents challenges in identifying the stages of GA and SF. As a result of our hybrid approach to CDSS development, we achieved significant improvements in accuracy, sensitivity, and specificity, and an F1-score exceeding 0.90 across all AMD stages. This performance enhancement surpassed the average results of the original CNN-based four-layer encoder architecture by more than 20%. Additionally, we calculated Cohen's Kappa coefficient to evaluate the level of agreement between the algorithm's outcomes and expert assessments regarding the severity of the intermediate stage of AMD. The calculated value of 0.708 indicates a high level of agreement between the algorithm's results and the experts' evaluations.  
Conceptualization, E.A.L.; methodology, E.A.L. and E.S.Y.; software, E.A.L. and E.S.Y.; validation, R.R.I. and G.M.I.; formal analysis, G.M.I.; investigation, E.A.L., E.S.Y., R.R.I. and G.M.I; resources, T.R.M., R.R.I. and G.M.I.; data curation, R.R.I. and G.M.I.; writing--original draft preparation, E.A.L.; writing--review and editing, E.A.L., E.P.G. and R.V.K.; visualization, E.A.L. and E.S.Y.; supervision, E.P.G. and G.M.I.; project administration, T.R.M. and R.V.K.; funding acquisition, E.P.G. and R.V.K. All authors have read and agreed to the published version of the manuscript.  
The research is supported by the Ministry of Science and Higher Education of the Russian Federation within the state assignment for UUST (agreement \(N\) 075-03-2024-123/1 dated 15 February 2024) and conducted in the research laboratory \"Sensor systems based on integrated photonics devices\" of the Eurasian Scientific and Educational Center.
## 4 Discussion
### Workflow  
* The workflow was tested on two case studies: a small-scale and a large-scale dam. In the case of a large-scale dam such as GERD, it is necessary to take into account (as mentioned above) the need for a larger team and/or a longer period for data processing and analysis. In the second case, it is therefore possible that the remote sensing activity may last up to one year.
* The workflow is designed to be also adapted by embedding other steps that should demonstrate necessary. An example is the addition of a fully automated or machine-assisted site identification step, associated with proper training on real site samples.
* The case of GERD dam also proves how the methodology is capable of flowing seamlessly from PCARA to PFAP protocols, depending on the actual stage of the dam construction and the situation on the ground that is captured by satellite images, provided that the latter are regularly collected.
* Furthermore, from a purely technical point of view, a future outlook of the proposed methodology will consist in the integration of manual site identification with automated detection of signals and patterns extracted from different types of satellite imagery that can be used as proxies of archaeological features and sites. This well-established practice includes a rich literature and a wide array of algorithms specifically developed for this scope [5; 64; 70; 87]. This future integration of rescue archaeology methodologies in the case of dam construction will provide a further step forward in the protection of cultural heritage.
## 4 Methodology
### Research Task's Mathematical Formulation  
In the integration of EVs and smart grids, the task can be framed as a multi-objective optimization problem. The mathematical formulation is as follows:  
* Let \(D_{p}\) be the distributed databases, \(W_{d}\) the distributed web servers, and \(W_{r}\) the web server links.
* Let \(K\in(K_{1},K_{2},\ldots,K_{i})\) represent a set of clients.
* Let \(P_{a}(P_{a_{1}},P_{a_{2}},\ldots,P_{a_{i}})\) be the set of program agents, with \(P_{a}\) performing smart charging tasks [36].  
Define the cost matrix \(C=[c_{ij}]\) and the quality matrix \(Q=[q_{ij}]\), where \(c_{ij}\) is the cost and \(q_{ij}\) is the quality of the \(i\)-th charging station at the \(j\)-th coordinates. The binary decision variable \(r_{ij}\) indicates whether the \(j\)-th charging point is selected for the \(i\)-th station.  
The optimization problem is  
\[\min\sum_{i=1}^{n}\sum_{j=1}^{n}c_{ij}r_{ij} \tag{6}\]  
subject to  
\[\sum_{i=1}^{n}r_{ij}=1\quad\forall i,\quad\sum_{j=1}^{n}r_{ij}=1\quad\forall j,\quad r_{ij}\in\{0,1\} \tag{7}\]To apply this to multi-criteria tasks, matrices are normalized:  
\[c_{ij}^{norm}=\frac{c_{ij}-c_{ij(min)}}{c_{ij(max)}-c_{ij(min)}},\quad q_{ij}^{ norm}=\frac{q_{ij}-q_{ij(min)}}{q_{ij(max)}-q_{ij(min)}} \tag{8}\]  
These normalization steps ensure that the criteria are comparable and ready for analysis through the PROMETHEE method. The numerical example used is based on theoretical scenarios derived from prior analyses conducted in the author's engineering and master's thesis projects [37]. These scenarios reflect realistic conditions in EV charging infrastructure deployment, including cost projections and performance benchmarks. This example serves to clearly demonstrate the PROMETHEE methodology's application, emphasizing its relevance for sustainable energy systems and EV infrastructure planning. For this example, the cost and quality matrices are as follows:  
\[C=\begin{bmatrix}2&4&6\\ 3&1&5\\ 7&8&2\end{bmatrix},\quad Q=\begin{bmatrix}7&5&8\\ 6&9&4\\ 3&2&6\end{bmatrix}\]  
Normalized matrices:  
\[C^{norm}=\begin{bmatrix}0&0.5&1\\ 0.333&0&0.833\\ 0.833&1&0.167\end{bmatrix},\quad Q^{norm}=\begin{bmatrix}0.833&0.5&1\\ 0.666&1&0.333\\ 0.333&0&0.666\end{bmatrix}\]  
The task solution algorithm proceeds in steps, starting with the client \((K)\) initiating an inquiry. The algorithm optimizes charging station locations by integrating smart charging strategies and multi-criteria decision analysis [36]. By using the PROMETHEE method, a ranking of charging station locations is derived, considering both cost and quality indicators.
## 3 Close-Proximity Concepts Design and Analysis
### Zones  
A zone is a predefined envelope (set of states, e.g., position and velocity) during the proximity operations mission timeline. The fundamental zones in this work are the following [11]:  
* Approach Zone (AZ): The zone around the client that can be entered only if the servicer has on-board access to a continuous reliable estimate of the current relative client-servicer position to ensure mission safety. The parameters of the shape and size of this zone are mission specific.
* Keep Out Zone (KOZ): The zone around the client where a potential collision could occur. Inside the KOZ, the servicer must follow the Approach corridor (during the Close Rendezvous Phase). The shape and size of the zone are mission specific. In this zone, during the Close Rendezvous Phase, the servicer must ensure a 6 DOF relative closed loop control.  
The association of phases and zones is depicted in Figure 4. The requirements associated with the definition of such zones, the need for transition to be intentional, and the separation of AZ and KOZ are found in [11]. For readability and conciseness, these can be summarized as follows:  
* The design of the servicer integrates the notion of the zones, in which the servicer enters only intentionally;
* The servicer is able to follow any trajectory within the AZ as long as such a trajectory does not enter the KOZ at any point during its execution.  
These requirements can be verified and validated, firstly, by a proper definition and sizing of the zones:  
* The Approach Zone is sized such that it defines an effective separation between closing trajectories (Client Phasing) and fly-around trajectories (Far Rendezvous), which are to be performed in the AZ itself.
* The Keep Out Zone design is based on the geometry of the client and the servicer: the KOZ is a sphere around the client, with a radius equal to the worst-case sum of the radii of the servicer and the client, with a 50% margin.  
The zones are further validated by an extensive testing of the trajectories, presented in Section 5.