esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
On the other hand, the Meteorom test reference year for Milan generally overestimates the thermal energy, while the SCOP values are similar to those obtained using real weather data. Moreover, in the special case of Milan, CTI TRY seems to be more reliable than Meteorom. Generally speaking, the TRY underestimates the number of defrost cycles. In detail, it underestimates the number of yearly hours when defrost may occur, i.e., when the couple temperature-relative humidity assumes values such that frosting at the evaporator may occur. Finally, another aspect that can be observed from Figure 11 is the percentage increase in the thermal energy demand considering the case with defrost respect to the case that  
Figure 11: SCOP and thermal energy demand results from the simulations considering with defrost (DEF) and without defrost (NO DEF). neglects defrost for the same year: it is evident how, for Milan, the building needs more thermal energy, up to +7.9% (year 2020), than for San Benedetto del Tronto (maximum increase of 5.5% for year 2014) and for Livigno (maximum increase of 4% for year 2019). These results are in agreement with those of other researchers ([31]), confirming the significant influence of energy losses linked to defrost cycles on the heat pump SCOP. In Figure 12, the electrical energy absorbed by the HP for all of the simulations performed is reported together with the percentage increase of electrical energy consumption considering defrost respect to the case with no defrost for every year. It can be observed that the percentage increase is higher for Milan (mean value +10.7%) and San Benedetto del Tronto (mean value +9.5%) than for Livigno (mean value +4.6%).
# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 3 Materials and Methods
### Fine-Tuning Pre-Trained Networks for Pet Breeds Identification  
Transfer learning is a machine learning technique that enables models to leverage knowledge acquired through training on one task not only for that task but also for various  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **N** & **Name** & **N** & **Name** & **N** \\ \hline Abysssinian & 150 & german shorthand & 150 & Russian Blue & 150 \\ american bulldog & 150 & great pyrenees & 150 & saint Bernard & 150 \\ american pit bull terrier & 150 & havanese & 150 & samoyed & 150 \\ basset hound & 150 & japanese chin & 150 & scottish terrier & 150 \\ beagle & 150 & leeshond & 150 & shiba inu & 150 \\ Bengal & 150 & leonberger & 150 & Siamese & 150 \\ Birman & 150 & Maine Coon & 150 & Sphynx & 150 \\ boxer & 150 & miniature pinscher & 150 & staffordshire bull terrier & 150 \\ British Shorthair & 150 & newfoundland & 150 & wheaten terrier & 150 \\ chihuahua & 150 & Persian & 150 & yorkshire terrier & 150 \\ Egyptian Mau & 150 & pomeranian & 150 & \\ english cocker spaniel & 150 & pug & 150 & \\ english setter & 150 & Ragdoll & 150 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: CPI dataset composition. The table lists the pet breeds and for each, the number of images in the CPI dataset.  
Figure 1: Examples of dataset images. This figure presents some images of the proposed CPI dataset.  
other tasks. In practical applications, due to limitations in manpower, material resources, and time costs, only small-scale datasets are typically available. Transfer learning techniques are particularly beneficial in such scenarios, allowing models to transfer knowledge acquired from large datasets such as ImageNet to pet breeds identification tasks.  
Classic neural network architectures typically consist of a backbone, neck, and head. As the name suggests, the backbone extracts features from data to create feature maps. The role of the head is to make a final decision based on the features extracted by the network, such as classification, detection, segmentation, etc. It is also referred to as the decision layer (or decision block). The neck is positioned between the backbone and head, often used to enhance the utilization of the features extracted by the backbone (from a design perspective, the neck is not essential). Therefore, in transfer learning techniques, fine-tuning the pre-trained network involves first removing the decision layer, redesigning it for the current task, and then retraining it. We transfer the knowledge obtained from large datasets such as ImageNet to pet breeds identification tasks. Furthermore, to enhance recognition accuracy in practical applications (especially in complex scenarios), we do not fine-tune the model directly on the Oxford-IIIT Pet dataset, but on the CPI dataset (see Figure 2).
# Intelligent Neural Network Schemes for Multi-Class Classification
## 2 Proposed Network Schemes
### RBF-Based Network Schemes  
Two schemes, named SL-Scheme and ML-Scheme, are developed. SL-Scheme, as shown in Figure 2, is for single-label multi-class classification. Note that M-RBF is a multi-class RBF network. The competitive activation function [31], compet, is used in Figure 2 at all the output nodes, defined as  
\[\text{compet}(\mathbf{x})\ =\ \left\{\begin{array}{c}+1,\text{ for output }k\ if\ k\ =\text{argmax}_{i}\{x_{i}\}\\ -1,\text{ for all other output nodes}\end{array}\right.. \tag{1}\]  
Note that compet could also be defined as a continuous function like the competitive layer in a Hamming network. Namely, the neurons compete with each other to determine a winner and only the winner will have a nonzero output. The winning neuron indicates which category of input was presented to the network. Both definitions can achieve the same goal.  
The architecture of M-RBF is shown in Figure 3. There are \(n\) nodes in the input layer, \(J\) nodes in the hidden layer, and \(m\) nodes in the second layer of M-RBF. Each node \(j\) in the hidden layer of M-RBF is associated with a basis function \(g_{j}(\mathbf{x})\). For a given input vector \(\mathbf{p}\), node \(j\) in the hidden layer of M-RBF has its output as  
\[o_{j}^{1}(\mathbf{p})\ =\ g_{j}(\mathbf{p}) \tag{2}\]  
for \(1\leq j\leq J\). Node \(i\) in the second layer of M-RBF has its output as  
\[o_{i}^{2}(\mathbf{p})\ =\ w_{i,0}+o_{1}^{1}(\mathbf{p})w_{i,1}+\ \ldots\ +o_{l}^{1}( \mathbf{p})w_{1,J} \tag{3}\]  
where \(w_{i,0}\) is the bias of the output node and \(w_{i,1}\), \(\ldots\), \(w_{1,J}\) are the weights between node \(i\) of the second layer and node \(j\) of the hidden layer. Then, \(\text{compet}\big{(}\mathbf{o}^{2}(\mathbf{p})\big{)}\) is computed. If the \(k\)th output of \(\text{compet}\big{(}\mathbf{o}^{2}(\mathbf{p})\big{)}\) is \(+1\), i.e., \(o_{k}(\mathbf{p})\ =\ +1\), \(\mathbf{p}\) is classified to category \(k\).  
Figure 3: Multi-class radial basis function (RBF) network M-RBF.  
Figure 2: Diagram of single-label (SL)-Scheme for single-label classification.  
ML-Scheme, as shown in Figure 4, is used for multi-label multi-class classification. Note that the symmetrical hard limit activation function [31], hardlims, is used at every output node, defined as  
\[\text{hardlims}(x)\ =\ \left\{\begin{array}{l}+1,\text{ if }x\ \geq 0\\ -1,\text{ if }x\ <0\end{array}\right.. \tag{4}\]  
To predict the classifications of any input vector \(\mathbf{p}\), we calculate the network output \(o_{k}(\mathbf{p}),\ 1\leq k\leq m\), as  
\[o_{k}(\mathbf{p})\ =\ \text{hardlims}\big{(}o_{k}^{2}(\mathbf{p})\big{)}. \tag{5}\]  
If \(o_{i}(\mathbf{p})\ =\ +1\) for any \(i\), \(\mathbf{p}\) is classified to category \(i\). Therefore, \(\mathbf{p}\) can be classified to several categories.
## 5 Experimental Case Planning, Results, and Analysis  
The across-loading fault diagnosis of induction motors of the experimental case was divided into two major classes, which are model comparison and analysis of signal processing and AI with limited data. Each experimental result was the average result of ten trainings and tests, and the indexes were average accuracy and standard deviation (STD). (1) The influence of different data preprocessing methods on motor fault diagnosis was analyzed in the signal processing case, favorable for the intelligent diagnostic model to obtain more feature information. The signal processing techniques were compared in the experimental case, so each signal conversion method used the residual VGG-based Siamese network model proposed in this study, and the data were sufficient and balanced. (2) In the experimental case of AI with limited data, the method proposed in this study was compared with different AI models in the fault diagnosis effect with training dataset imbalance ratio.
# Longitudinal Seismic Response of Continuously Welded Track on Railway Arch Bridges
## 4 Influential Factors of Seismic Response on CWR on Arch Bridge
### Seismic Wave Spectrum  
The rail seismic forces under El-Centro, San Fernando, and James RD waves are compared in Figure 9. It can be seen that, under the same peak acceleration, the maximum rail seismic force under San Fernando waves increases by 71.6% compared with El-Centro waves--up to 1059.4 kN. The maximum rail seismic force under James RD waves increases by 147.5% and 44.2% as compared with El-Centro and San Fernando waves respectively--up to 1527.7 kN. This indicates that the seismic wave spectrum characteristics exert great impact on the seismic response of arch bridges. According to the existing research, the maximum allowable rail longitudinal force is about 1500 kN (the safety factor is 1.3) [22]. For the San Fernando wave, the maximum rail longitudinal force could be up to 1628.7 kN, when accompanied by a rise in rail temperature of 30 \({}^{\circ}\)C. For James RD waves, the maximum rail longitudinal force might exceed 1500 kN, regardless of the temperature rise. The track stability may not be guaranteed under these two cases, which should be control conditions for the design of CWR track on bridge. These cases demonstrate the significance of seismic action for the design of CWR track on bridges in earthquake zones.  
The effect of seismic forces on CWR tracks on arch bridges varies greatly under different seismic waves. To avoid potentially large calculation errors, seismic waves consistent with the spectrum features of bridge site should be used for analysis. In the following section, only San Fernando wave input is used; the role of longitudinal force of CWR tracks on arch bridges has already been highlighted above.
## 3 Formulation of the Wind Power Curtailment Scheduling Problem
### Constraints for the Expected Wind Power and Load
#### 3.1.2 Constraints for the Uncertain Wind and Load Scenario  
1. Active power balance for scenarios: \[\sum_{i=1}^{N_{G}}p_{G,i,t}^{s}+\sum_{i=1}^{N_{W}}p_{W,i,t}^{s}=\sum_{i=1}^{N _{B}}p_{D,\;i,t}^{s},\qquad\qquad\forall i,t,s,\] (18)where \(p^{s}_{G,\;i,t}\) is the power generated by unit \(i\) at time \(t\) under scenario \(s\), and \(p^{s}_{W,\;i,t}\) and \(p^{s}_{D,\;i,t}\) respectively represent the wind power generated and the load for scenarios.
2. Absolute power limits of the wind farm for scenarios: \[p^{s}_{W,i,t}=\min\bigl{(}p^{s}_{AW,\;i,\;t},\;l_{i,\;t}\bigr{)}, \forall i,t,s,\] (19) where \(p^{s}_{AW,\;i,\;t}\) is the randomly sampled available wind generation for scenarios.
3. Generation limits of the conventional unit for scenarios: \[p^{s}_{G,i,t}+reg^{s}_{up,i,t}\leq p_{G,i,t}+r_{up,i,t}, \forall i,t,s,\] (20a) \[p^{s}_{G,i,t}-reg^{s}_{dn,i,t}\geq p_{G,i,t}-r_{dn,i,t}, \forall i,t,s,\] (20b) where \(reg^{s}_{up,i,t}\) and \(reg^{s}_{dn,i,t}\) are respectively the upward and downward regulation powers required under scenarios. These constraints ensure that the generation and regulation powers of a unit are under its scheduled capacity limit.
4. Ramping capability limits of the unit for scenarios: \[reg^{s}_{up,i,t}\leq UR^{\max}_{i}\cdot\tau\cdot u_{i,t}, \forall i,t,s,\] (21a) \[reg^{s}_{dn,i,t}\leq DR^{\max}_{i}\cdot\tau\cdot u_{i,t}, \forall i,t,s,\] (21b) These constraints ensure that a generation unit can increase or decrease its regulation power by changing its ramping rates appropriately during a certain time period \(\tau\).
5. System ramping requirements for scenarios: \[\sum_{i=1}^{N_{G}}reg^{s}_{up,i,t}\geq\sum_{i=1}^{N_{W}}dv_{W}(p^{s}_{AW,i,t}) +uv_{D}, \forall i,t,s,\] (22a) \[\sum_{i=1}^{N_{G}}reg^{s}_{dn,i,t}\geq\sum_{i=1}^{N_{W}}uv_{W}(p^{s}_{AW,i,t}) +dv_{D}, \forall i,t,s,\] (22b) where \[uv_{D}\] and \[dv_{D}\] represent the maximum short-term variations of the system load, which are assumed to be identical over the time period; \[uv_{AW}(p^{s}_{AW,i,t})\] and \[dv_{AW}(p^{s}_{AW,i,t})\] are the maximum upward and downward variations for scenario \[s\], which were derived in ( 6 ) and ( 7 ).  
The above formulation is a large-scale, mixed-integer nonlinear problem (MINLP), because it contains the continuous integral for the expected wind power in ( 14 ) and the min-function for the absolute power limit in ( 19 ). In order to avoid this computational intractability, a nonlinear structure in the above model is transformed into MIP forms by discretizing random variables, which is described in the following section.
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 6 Conclusions  
This article discusses how to support mobility in ICN. First of all, we proposed a mobility support method for dynamically allocating mobile data flows (DAFM) based on the ICN mobility support architecture, thus realizing mobile route optimization. Based on this method, we modeled the mobile data flow allocation problem as a late-binding node selection problem. We proposed a double-constraints heuristic algorithm (DCHA) based on the Moser algorithm. The DCHA minimizes the average packet delay by simultaneously adjusting the bandwidth resource constraints of the switching path and the space resource constraints of the Mobility Flow Table (MFT). Finally, we implemented our proposal in Mininet and compared it with classic anchorless proposals such as MAP-ME and the OPT. Simulation results show that our proposal outperforms other methods in terms of average packet delay, packet loss rate, and handover delay under the guarantee of a low-latency Name Resolution System (NRS).  
In future work, we can combine caching with mobility support solutions, cache packets in access routers, further reduce packet loss, and improve mobile user performance. In addition, we will study ways of obtaining switching path information to further improve the practicability of our proposal, such as sending detection messages. We should also further consider how to select the late-binding node. We can select the late-binding node based on the router's situation. For example, the core router should not be selected as the late-binding node because of the large data flow through it, and frequent entry updates will affect its forwarding performance.  
Conceptualization, M.X. and R.H.; investigation, M.X.; methodology, M.X.; software, M.X.; validation, M.X.; writing--original draft preparation, M.X.; writing--review and editing, R.H. and H.D.; supervision, R.H. and H.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Strategic Leadership Project of the Chinese Academy of Sciences: SEANet Technology Standardization Research System Development (Project No. XDC02070100).  
All of the necessary data are included in the article.  
We would like to express our gratitude to [PERSON] and [PERSON] for their meaningful support for this work.  
The authors declare no conflict of interest.
# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 2 Background
### Case-Based Approach: Basic Principles
For qualitative values:  
\[\mathrm{h_{j}(p*_{j},p_{ij})=1,\ v*_{j}\in\{v_{j1},\ldots v_{jM}\}} \tag{3}\] \[\mathrm{h_{j}(p*_{j},p_{ij})=0,\ v*_{j}\
otin\{v_{j1},\ldots v_{jM}\}}\]  
where \(\{v_{j1},\ldots,v_{jM}\}\) is a list of qualitative values. For interval values:  
\[ \begin{split}\text{h}_{\text{j}}\Big{(}\text{p*}_{\text{j}},\text{p} _{\text{ij}}\Big{)}&=1,\;\text{v*}_{\text{j}}\;\in\big{[}\text{v}^{ +}\text{j},\;\text{v}^{++}\text{j}\big{]}\\ \text{h}_{\text{j}}\Big{(}\text{p*}_{\text{j}},\text{P}_{\text{ij}} \Big{)}&=0,\;\text{v*}_{\text{j}}\;\
otin\big{[}\text{v}^{+}\text{ j},\;\text{v}^{++}\text{j}\big{]}\end{split}  \tag{4}\]  
where \(\text{v}^{+}\text{j}\) and \(\text{v}^{++}\text{j}\) are the boundaries of the intervals of the values of the case properties. Reuse consists of using information from retrieved cases to solve a new problem. If necessary, the extracted information is adapted. In this paper, adaptation is not required; the main purpose of reuse is to obtain a number of cases with a certain similarity. Revise implies checking the obtained solution by an expert. Retain is aimed at preserving the obtained solution in a case base to expand it. This is how training of the CBR systems takes place. When solving the problem of forecasting wildfires, the CBR revise and retain stages were not implemented. As noted above, CBR provides effective use of accumulated experience in the absence of in-depth knowledge, allowing one to solve problems based on superficial knowledge, and can be considered as an intermediate method when moving to deeper research. In addition, the advantage of the method is the fact that the case base can be continuously updated as new wildfire data become available, while there is no need to train the model and select model parameters; there are also no problems of retraining it, etc.
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Used** & **Method** & **Study Area** & **D 1 (days)** & **ref.** \\ \hline MODIS EVI (250 m) & TIMESAT & China’s 32 major cities & 11.9 & [25] \\ MODIS EVI (500 m) & Sigmoid function & Conterminous United States & 9 & [24] \\ MODIS EVI (1 km) & TIMESAT & Northeast, China & 16.8 & [23] \\ Landsat EVI (30 m) & Threshold & Shanghai, China & 5–10 & [26] \\ MODIS EVI (1 km) & Logistic function & Eastern North, America & 7 & [29] \\ Landsat EVI (30 m) & LPA 2 & Boston, United States & 10–12 & [30] \\ MODIS EVI (1 km) & Curvature & Northern mid-high latitudes & 4–9 & [31] \\ AVHRR NDVI (1 km) & Threshold & Eastern United States & 5.7 & [32] \\ SPOT NDVI (1 km) & Model fit & Yangtze River Delta, China & Less than 15 & [27] \\ Fused NDVI 3 (30 m) & TIMESAT & Salt Lake City, United States & Less than 3.56 & [28] \\ \hline \hline \end{tabular} \({}^{1}\) D means average difference of vegetation spring phenology between rural and urban areas; \({}^{2}\) LPA means Landsat phenology algorithm; \({}^{3}\) Fused NDVI means NDVI time series derived from Landsat and MODIS fusion.  
\end{table} 
Table 1: Summary of previous studies of urbanization effects on vegetation spring phenology using satellite images.
# Copper Price Prediction Using Support Vector Regression Technique
###### Abstract  
Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is nonlinear and non-stationary, and that has periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes, the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of a trend-cycle. In this paper, we study a copper price prediction method using support vector regression (SVR). This work explores the potential of the SVR with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchange. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data sets, our results obtained indicate that the parameters (\(C,e,\gamma\)) of the model support vector regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, as the root-mean-square error (RMSE) was equal to or less than the 2.2% for prediction periods of 5 and 10 days.  
copper price; prediction; support vector regression Article
# Cyclic Detectors in the Fraction-of-Time Probability Framework
## 2 Materials and Methods  
### Signal Decomposition  
Let us denote by  
\[\left\langle z(t)\right\rangle_{t}\triangleq\lim_{T\to\infty}\frac{1}{T}\int _{t_{0}-T/2}^{t_{0}+T/2}z(t)\;\mathrm{d}t \tag{1}\]  
the continuous-time infinite-time average of the signal \(z(t)\), provided that the limit in the right-hand side exists. When such a limit exists, it does not depend on \(t_{0}\). In notation \(\left\langle\cdot\right\rangle_{t}\), subscript \(t\) emphasizes that the average is made with respect to \(t\).  
A signal \(z(t)\) can be decomposed into its almost-periodic component \(z_{\mathrm{ap}}(t)\) and a residual term \(z_{\mathrm{r}}(t)\) not containing any finite-strength additive sine-wave component [2] (Section 2.3.1)  
\[z(t)=z_{\mathrm{ap}}(t)+z_{\mathrm{r}}(t) \tag{2}\]  
where  
\[z_{\mathrm{ap}}(t)=\sum_{\alpha\in\mathrm{A}}z^{\alpha}\;e^{\beta\pi\alpha t} \tag{3}\]  
is the (generalized) Fourier series of the almost-periodic component whose convergence can be uniform, or in a generalized sense [2] (Appendix B.4), [3] (Chapter 2),  
\[z^{\alpha}=\left\langle z_{\mathrm{ap}}(t)\,e^{-\beta 2\pi\alpha t}\right\rangle _{t}=\left\langle z(t)\,e^{-\beta 2\pi\alpha t}\right\rangle_{t}\quad\forall \alpha\in\mathrm{A} \tag{4}\]  
are the Fourier coefficients, and  
\[\left\langle z_{\mathrm{r}}(t)\,e^{-\beta 2\pi\alpha t}\right\rangle_{t}=0 \quad\forall\alpha\in\mathbb{R} \tag{5}\]  
is the residual term. In (3), A is a countable set of possibly incommensurate frequencies.  
The _almost-periodic component extraction operator_ is defined as [2] (Definition 2.14)  
\[\mathrm{E}^{\{\alpha\}}\{z(t)\}\triangleq z_{\mathrm{ap}}(t) \tag{6}\]  
that is, the operator \(\mathrm{E}^{\{\alpha\}}\{\cdot\}\) extracts all the finite-strength additive sine-wave components of its argument. This turns out to be the expectation operator in the FOT probability framework.