esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the new output of the ARIMA is categorized into three groups before it is sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.  
hybrid forecast; integrated forecast; multi-pattern data forecasting; forecast error classification
## 2 Literature Review
### Quality of Service in SDN  
The internet supports various applications and services, such as online gaming, cloud gaming, video streaming, cloud computation, video conferencing, e-commerce, etc., each with its own requirements. Traditional networks based on best-effort models cannot efficiently handle the diverse QoS demands of these applications [21]. To address this issue, different QoS architectures have been proposed, including integrated services (IntServ) [22] and differentiated services (DiffServ) models [23], and multiprotocol label switching (MPLS) technology [24]. However, these methods have their limitations and do not fully support all QoS requirements.  
Software-defined networks (SDNs), with their decoupled data and control plane, offer a promising solution to address QoS issues in networks. Researchers have presented various models to monitor QoS parameters in SDN networks, such as FlowSense [25] and PayLess [26]. These models focus on parameters such as bandwidth, delay, throughput, packet loss ratio, jitter, and route tracing to maintain QoS. FlowSense uses a push-based approach to monitor link utilization in flow-based networks, while PayLess provides accurate information about the network in real time with less overhead, using a flexible RESTful API method to collect statistical information at different aggregation levels.  
Maintaining quality of service (QoS) in large networks is a significant challenge. Factors such as scalability, traffic management, and bandwidth have a significant impact on network quality. Several SDN-based solutions have been proposed to enhance QoS and address these challenges. The author in [27] presented a reactive approach in the forwarding application of SDN in ISP networks, aiming to control the number of OpenFlow messages. Their efficient quantitative model accurately computes the required number of exchanged OpenFlow messages and bandwidth, as well as the number of flow rules to be installed on each switch.  
Another study by [28] highlighted the performance bottleneck of the control channel and introduced a prediction model based on seven functions. Their model achieves a 94% accuracy rate. The load on the SDN controller also affects QoS and other parameters. In [29], the author proposed a novel scheme for load balancing among multiple SDN controllers using the SMCLBRT method. The load balancing is based on the controller response time and load. Additionally, in [30], the author applied a flow redirecting technique to minimize the maximum response time of the SDN controller and improve QoS. Their model takes into account flow-table size and link capacity constraints. The test results demonstrate a 50-80% increase in the maximized controller response time. All these studies highlight the importance of controller response time and capacity in maintaining QoS.
## 3 Method
### Detection-Centered Optimization  
We define a hybrid loss combining  
\[\mathcal{L}=\underbrace{\lambda_{1}\|G_{\sigma}*I_{\text{enhanced}}-G_{ \sigma}*I_{\text{GT}}\|_{1}}_{\text{Low-Freq}\,\text{Constraint}}+ \underbrace{\lambda_{2}\mathcal{L}_{\text{det}}(D(I_{\text{enhanced}}))}_{ \text{High-Freq Alignment}}, \tag{15}\]  
where \(G_{\sigma}\) is a Gaussian kernel (\(\sigma=5\)), and \(\lambda_{1}:\lambda_{2}=3:7\) balance the weights from Theorem 1.  
Optimization of Loss Weight Ratio  
The selection of \(\lambda_{1}:\lambda_{2}=3:7\) was determined through systematic ablation studies and theoretical analysis. **Theoretical Foundation:** From Theorem 1, the optimal fusion convergence requires  
\[\frac{\partial\mathcal{L}}{\partial w_{HF}}=\lambda\frac{\partial\mathcal{L}_ {det}}{\partial w_{HF}} \tag{16}\]  
Through gradient analysis on the validation set, we found  
\[\mathbb{E}[\|\
abla_{w_{LF}}\mathcal{L}_{LF}\|_{2}]\approx 0.3\mathbb{E}[\| \
abla_{w_{HF}}\mathcal{L}_{det}\|_{2}] \tag{17}\]  
This empirically justifies the 3:7 ratio.  
**Experimental Validation:** Table 1 shows the ablation study.  
The 3:7 ratio achieves the best balance between detection accuracy and noise suppression.
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 2 Mathematic Model of Hybrid Power System
### PEMFC Model
#### 2.1.1 Stack Voltage
The concentration polarization voltage can be described as Equation (8):  
\[V_{conc}=\frac{RT_{FC}}{nF}\ln(\frac{I_{\max}}{I_{\max}-I_{FC}})=4.308\times 1 0^{-5}T_{FC}\ln(\frac{I_{\max}}{I_{\max}-I_{FC}}) \tag{8}\]  
where \(I_{\max}\) denotes the maximum current of the PEM, \(R\) is the reaction constant, and \(F\) is the Faraday constant. In practical application, multiple fuel cells are usually connected in series to form a stack to increase the output power. Therefore, the overall output voltage of the fuel cell stack is given by Equation (9):  
\[V_{FC}=N_{cell}V_{oc} \tag{9}\]  
where \(V_{FC}\) is the output voltage of the stack, and \(N_{cell}\) is the number of fuel cells connected in series.
# Scene Equipment Saving and Loading Method for Digital Twin Workshop
## 5 Results and Discussion
### Data Saving and Loading Efficiency  
The test results for basic data type saving and loading efficiency tests based on the DTWorks architecture are displayed in Table 6, whereas the test results for the B/S architecture are displayed in Table 7. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Content** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Average** \\ \hline Saving & & & & & & & & & & & \\ Blank control & 34 & 33 & 33 & 33 & 33 & 34 & 34 & 33 & 34 & 33 & 33.4 \\ Boolean & 33 & 34 & 33 & 33 & 34 & 33 & 34 & 33 & 34 & 33.5 \\ Byte & 42 & 33 & 33 & 34 & 33 & 34 & 33 & 42 & 32 & 33 & 34.9 \\ Integer & 92 & 100 & 100 & 93 & 91 & 91 & 91 & 102 & 92 & 101 & 95.3 \\ Float & 217 & 213 & 217 & 209 & 209 & 208 & 225 & 217 & 217 & 209 & 214.1 \\ String & 120 & 119 & 117 & 118 & 118 & 110 & 114 & 111 & 119 & 118 & 116.4 \\ \hline Loading & & & & & & & & & & & \\ Blank control & 28 & 26 & 22 & 23 & 21 & 23 & 21 & 22 & 22 & 23.1 \\ Boolean & 27 & 25 & 27 & 28 & 26 & 27 & 30 & 22 & 19 & 21 & 25.2 \\ Byte & 30 & 27 & 28 & 31 & 31 & 28 & 34 & 31 & 29 & 28 & 29.7 \\ Integer & 68 & 73 & 67 & 68 & 69 & 65 & 65 & 67 & 71 & 63 & 67.6 \\ Float & 80 & 79 & 77 & 76 & 82 & 73 & 82 & 78 & 81 & 86 & 79.4 \\ String & 47 & 48 & 44 & 44 & 44 & 48 & 45 & 48 & 46 & 44 & 45.8 \\ \hline \hline \end{tabular}  
* Unit in the table: ms.  
\end{table} 
Table 5: Multi-type data saving and loading test results. The test results for the two architectures are summarized in Table 8, and a graph comparing their efficiencies is presented in Figure 11.  
As shown in Figure 11, the DTWorks system proposed in this paper is more efficient than the B/S-based comparison group for varying volumes and types of data saving/loading operations. Not only is the B/S-based DT system marginally slower than DTWorks in terms of processing speed, but it also has variable upload and download times dependent on network quality, in addition to the inherent network communication delays of the B/S architecture. Overall, the test results indicate that using DTWorks can increase saving efficiency by 82.6% and loading efficiency by 74.0% for basic data types. Table 9 displays the test results for typical composite data saving and loading efficiency tests based on the DTWorks architecture, while Table 10 displays the test results for tests utilizing the B/S architecture.
## 4 Experimental Results and Discussion
### Subjective Visual Evaluation of Saliency Models
Because the test database contains all kinds of complex, typical sea conditions, the threshold T of the binary processing increased from 0 to 255 when the PR curve was drawn. The average value of recall and precision in different cases was obtained as shown below. It can be seen from the Figure 13 that the PR and F curves of our algorithm are obviously higher than those of the other two methods. From the PR curve, it can be seen that, when the recall rate is 80%, the precision of ITTI and the method in [12] is about 40% and 73%, respectively. The precision of our algorithm is about 84%, which is 11% higher than the method in [12]. Our algorithm has an obvious advantage in the case of complex sea conditions because it has constructed a contrast map, a Gabor map, and a spatial and texture correlation feature to facilitate the integrity of target edge detection. At the same time, MPQFT constructs a multi-scale space, which is helpful for detecting targets of different sizes.
## 3 Kernel Density Estimation based on Non-Uniform Quantization  
### Overview of the Proposed Quantization Process  
The non-uniform quantization methodology is suitable for performing weights quantization of CNN because it optimizes to reduce quantization errors. However, one of the key features of deep learning models like CNN is the large number of weights. For k-means, the amount of computations increases proportionally as the number of weights increases. In particular, because the k-means is one of the NP-hard problems, it is difficult to proceed until convergence, and by placing an upper limit on optimization, the results of quantization with relatively low performance can be obtained by early stopping. Similarly, the Lloyd-Max quantizer is affected by the number of data when performing an integrated calculation in the actual computing environment. As the range of weights in the pre-trained deep learning model is relatively small and a lot of weights have similar values, it is inefficient to obtain quantized values for all weights to reflect the characteristics of the weights. This paper proposes a kernel density estimation based non-uniform quantization that can be more efficient in terms of computation time.  
Figure 2 shows an overview of the suggested kernel density estimation based non-uniform quantization methodology. First, weights from a pre-trained CNN are used to perform the kernel density estimation to make a probability density function about the original weights. By using this probability density function, we can sample the data reflecting characteristics of original weights. At this point, the number of sampled data is smaller than the number of original weights, enabling more efficient quantization operations. Through sampled data, k-means quantization becomes more efficient in terms of computation. Similarly, the sampled data are used again for kernel density estimation and make a probability density function of data. The Lloyd-Max quantizer using the probability density function of sampled data is more efficient in the integral computation, because the number of sampled data used to calculate the area of the probability distribution function is much smaller than the number of original weights.  
K-means use sampled data in the probability density function of the original data, and the Lloyd-Max quantizer performs the kernel density estimation on the sampled data and uses the probability function of the sampled data. The k-means and Lloyd-Max quantizer of the proposed methodology are separately performed. Therefore, each methodology independently generates a codebook to carry out quantization of the originals.
## Appendix A
### Weibull Distribution  
The Weibull distribution is used to evaluate the product's behavior in its three life phases depending on the shape parameter \(\beta\): infant mortality period (\(\beta<1\)), useful life period (\(\beta=1\)), and wear out period (\(\beta>1\)). It is commonly employed in mechanics.  
The Weibull distribution is characterized by two parameters \(\eta\) and \(\beta\). It is marked by the following:  
The probability density function:  
\[f(t)=\frac{\beta}{\eta}.(\frac{t}{\eta})^{\beta-1}e^{-(\frac{t}{\eta})^{\beta}} \qquad\text{with t}\geq 0 \tag{1}\]  
The reliability:  
\[R(t)=e^{-(\frac{t}{\eta})^{\beta}}\qquad\qquad\qquad\text{with t}\geq 0 \tag{2}\]  
The failure rate:  
\[\lambda(t)=\frac{\beta}{\eta}\;(\frac{t}{\eta})^{\beta-1}\qquad\text{with t}\geq 0 \tag{3}\]
# Interference Monitoring from Low Earth Orbit: The OPS-SAT Experiment +
## 6 Conclusions  
This paper presented an experiment in the field of interference detection and geolocation from LEO by leveraging the availability of OPS-SAT operated by ESA ESOC for 1/Q sample recording and the opportunity of transmitting high power jammer in GNSS band in the context of Jammertest 2023. The JRC team identified the best satellite passage in the Jammertest area and scheduled the data collection. The FFI team, jointly with JRC members, focused on activating the chirp jammer during the satellite passage. The collection of this jammer data set is a fundamental asset for JRC. Unfortunately, due to OPS-SAT hardware constraints, the recording time was limited to few seconds, limiting the recorded Doppler dynamic related to the satellite movement. However, this paper proposes the use of CAF between disjointed acquisition windows in the time domain to derive the FDOA observables, which are then employed for geolocation purposes. Despite biased and noisy FDOA observables, this paper shows that it is possible to perform a coarse geolocation of the jammer. As remarked throughout the paper, gaining knowledge of the jammer signal waveform and its precise location was a unique opportunity to assess performances and limits in terms of jamming detection and geolocation algorithms. Finally, let us remark that, to the best of author's knowledge, this is the first data set collection from LEO in L-band performed during the Jammertest event, which could be replicated in the near future, potentially investigating different jammers or even a combination of them, with the idea of gathering a wider understanding of the interference monitoring from space.  
Conceptualization, F.M.; software, O.M.P.; writing--review and editing, T.S., methodology, L.C. and A.P.; resources, V.Z.; project administration, J.F.-G. All authors have read and agreed to the published version of the manuscript.  
This research received no external fundings.  
Figure 7: Localization results of the jammer emitter via OPS-SAT experiment.
## 3 Results and Discussion
### Sensory Acceptability  
On day zero, the roasted pates with the addition of NS oil received higher scores than the others (Figure 5a).  
In particular, groups O1 and O2 were characterised by a higher aroma intensity and a higher juiciness and appearance. [PERSON] et al. [23] noticed that 0.3% of black cumin in beef pate resulted in a higher flavour rating for the pate stored in the refrigerator. On the other hand, [PERSON] et al. [74] replaced the pork fat in the pork pate with encapsulated fish oil. Consumers noticed a fishy flavor in the pates. However, the overall liking did not change regardless of the level of addition of microcapsules with fish oil instead of pork fat. On the other hand, sensory evaluation of pork pate with pre-emulsified canola showed no differences compared to pate with full fat [75]. After the first month of frozen storage, the pates from the O2 group, with a higher level of black cumin, received the highest ratings in terms of flavour and juiciness. The highest overall acceptability was given to the O1 group. After two months of frozen storage of minced meat for pate, the sensory evaluation of the pate decreased in all criteria and in all experimental groups. For appearance, colour, flavour, juiciness, texture, and overall acceptability, the O1 pate received the highest scores. Only taste was rated highest in the O2 group. Regarding colour, the O1 group was characterised by a significantly higher brightness (L*) after two months of storage compared to the groups without black cumin. Pates with black cumin oil had a very similar profile of volatile compounds throughout the storage period and were dominated by terpen and esters, i.e., compounds that are perceived as a sign of freshness. [PERSON] et al. [23] explained the deterioration in the taste of patties during storage of meat without black seed oil in terms of increased lipid oxidation, the release of free fatty acids, and the dispersion of volatile compounds.  
Figure 4: Statistical quality control chart; odor distances determined on the basis of Euclidean distance (C, negative control; CB, positive control with 0.01% BHA; O1, group with 1.9% _Nigella sativa_ L. oil; O2, group with 3.8% _Nigella sativa_ L. oil; Storage (\(-20\pm 1\)\({}^{\circ}\)C) time: D; day, M; month).
## 3 Results and Discussion  
### Preparation and Characterization of the Material  
#### 3.1.1 Preparation of TiO\({}_{2}\)-Fe\({}_{3}\)O\({}_{4}\) Composite  
A composite material based on titanium(IV) oxide and iron(II,III) oxide was prepared by mechanothermal method. After the annealing process, slight browning of the material was noted and associated with oxidation of magnetite Fe\({}_{3}\)O\({}_{4}\) into maghemite \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\).
# Phenolic Compounds in Trees and Shrubs of Central Europe
## 3 Results
### Phenolic Compounds
#### 3.1.2 Flavonoids  
The chemical structure of all flavonoids is based on the hydrocarbon skeleton of flavone (Figure 1). They differ in the number and type of substituents, while differences between these compounds result primarily from a different structure in only one basic ring. Chalcone formed via biosynthesis from phenylalanine is a precursor of flavonoids. Its synthesis starts with shikimic acid. Flavonoids are found not only as free molecules (aglycones), but also much more frequently in the bound form with sugars (glycos). To date, over 7000 various flavonoids have been identified, which, in terms of their chemical structure, are divided into flavones, flavonoids (3-hydroxyflavones), flavanones, flavanols (flavan-3-oles), flavanonoles, anthocyanin, isoflav and neoflavonoids (Figure 1, Table 5). Thanks to their unique structure, flavonoids may protect the cell against reactive oxygen species (ROS) generated in the organism [81; 82]. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Phenolic Acids**} & \multicolumn{2}{c}{**Fir**} & \multicolumn{2}{c}{**Larch**} & \multicolumn{2}{c}{**Pine**} & \multicolumn{2}{c}{**Spruce**} \\ \cline{2-9}  & \multicolumn{2}{c}{**Range**} & \multicolumn{2}{c}{**Range**} & \multicolumn{2}{c}{**Range**} & \multicolumn{2}{c}{**Range**} & \multicolumn{2}{c}{**Range**} \\ \hline Gallic & 4.5 & 5.4 & 5.5 & 7.3 & 12.5 & 14.1 & 36.6 & 41.3 \\ \hline
4-hydroxybenzoic & 31.5 & 38.8 & 42.3 & 48.6 & 40.2 & 55.8 & 199.5 & 227.0 \\ \hline Vanilic & 183.6 & 194.4 & 11.2 & 20.4 & 4.6 & 7.1 & 5.7 & 7.6 \\ \hline Caffeic & 27.4 & 30.8 & 161.0 & 177.5 & 87.6 & 107.0 & 204.6 & 287.9 \\ \hline Syringic & 3.6 & 4.3 & 15.9 & 16.3 & 5.3 & 8.9 & 18.3 & 20.1 \\ \hline Vanilin & 10.2 & 11.2 & 16.2 & 24.4 & 0.1 & 0.4 & 0.2 & 1.5 \\ \hline p-Cumaric & 10.0 & 15.3 & 225.2 & 249.0 & 14.6 & 19.9 & 6.3 & 9.0 \\ \hline Benzoic & 216.4 & 239.3 & 48.5 & 60.5 & 8.5 & 12.5 & 0.7 & 6.9 \\ \hline Ferulic & 0.5 & 1.1 & 6.4 & 7.4 & 88.6 & 109.9 & 52.4 & 70.2 \\ \hline Sinapic & 5.3 & 6.9 & 3.6 & 5.2 & 3.1 & 4.3 & 50.5 & 68.4 \\ \hline t-Cinnamic & 27.4 & 29.8 & 55.0 & 63.7 & 5.2 & 8.7 & 36.5 & 45.1 \\ \hline Chlorogenic & 3.4 & 10.9 & 28.7 & 32.4 & 24.2 & 29.1 & 201.7 & 246.2 \\ \hline Protocatechuic & 0.3 & 1.2 & 13.5 & 20.1 & 0.3 & 0.7 & 11.8 & 20.5 \\ \hline Rozmaric & 0.3 & 1.1 & 147.9 & 163.7 & 1.6 & 3.6 & 0.3 & 0.8 \\ \hline Salicylic & 0.09 & 0.1 & 0.1 & 0.9 & 0.2 & 0.5 & 5.6 & 6.8 \\ \hline
**Total** & **524.5** & **590.6** & **781** & **897.4** & **296.6** & **382.5** & **830.7** & **1059.3** \\ \hline
**Mean** & \multicolumn{2}{c}{**557.55**} & \multicolumn{2}{c}{**839.2**} & \multicolumn{2}{c}{**339.55**} & \multicolumn{2}{c}{**945**} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Concentration range of phenolic acids (mg/kg d.w. needles) in pine, fir, larch and spruce needles.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 4 Experiments  
In order to evaluate DB2 DPCA, we compare its performances in image reconstruction and recognition with four related algorithms, i.e., PCA, 2 DPCA, A2 DPCA and \((2\mathrm{D})^{2}\)PCA, on four image databases. To further exploit the properties of DB2 DPCA, we additionally conduct image recognition experiments when the pixels in images are reordered by certain rules and when images are reshaped to different sizes.
## 3 Preliminaries
### Mathematical Tools
#### 3.3.2 Elliptic Curve Cryptography  
[PERSON] [44] introduced elliptic curve cryptography (ECC) in 1985. Since its introduction, ECC has been widely employed in many authentication mechanisms. Some mechanisms documented their implementation steps in detail. For example, assume that the symbol \(E/F_{p}\) indicates an elliptic curve. Then, \(E\) is determined using the following equation.  
\[y^{2}=x^{3}+ax+b(mod~{}p) \tag{4}\]  
where \(p\) is a large prime number; \(a\), \(b\in F_{p}\), \(Z_{p}\) is a prime finite field; and \((4a^{3}+27b^{2})\ mod~{}p\
eq 0\). The primary hard problems of ECC are as follows:  
* Elliptic Curve Discrete Logarithm (ECDL) Problem: \(P\) and \(Q=a\mathrm{P}\in Z_{q}^{*}\) are two random points on ECC. The core idea of this problem is to calculate the secret value \(a\) from point \(Q=a\mathrm{P}\in Z_{q}^{*}\). However, it is difficult to calculate the points \(Q=a\mathrm{P}\) with negligible probability based on the supposition.
* Elliptic Curve Computational Diffie-Hellman (ECCDH) Problem: \(R=b\mathrm{P}\) and \(Q=a\mathrm{P}\in Z_{q}^{*}\) are two random points on ECC. The core idea of this problem is to calculate the secret values \(a\) and \(b\) from points \(R=b\mathrm{P}\) and \(Q=a\mathrm{P}\in Z_{q}^{*}\). However, based on the supposition, it is difficult to compute the points \(R=b\mathrm{P}\) and \(Q=a\mathrm{P}\) with negligible probability.