esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Spectral Components of Brushed DC Motor Current
### Ripple Component  
Brushed dc motors have a number of coils, \(B\), in the rotor, which are connected to an external circuit through brushes and a commutator. According to [PERSON]'s law, when the motor is rotating, a back electromotive force (back-EMF) is induced in each coil. This back-EMF is rectified by the brushes and commutator, and carried to an external circuit. The back-EMF and commutation occur in each coil such that the back-EMFs are added and rectified by the brushes and commutator. The rectification ideally produces a constant signal in the external circuit. In contrast, the real rectification process produces a small ac component added to a constant signal in the external circuit. This ac component is the ripple component.  
The amplitude of the ripple component depends on the number of coils, the inductance of the coils, and the power of the motor. The frequency of the ripple component depends on the number of undulations per second produced in the current. Each undulation is produced when a coil is in front of a pole of a brushed dc motor. This pole can be either a north or south magnetic pole. There is no difference in pole polarity because of the rectification by the commutator.  
Figure 1: [PERSON]’s equivalent of the source and brushed dc (direct current) motor.  
A study conducted by [PERSON] et al. (2017) relates the frequency of the ripple components, \(f_{r}\), to the number of field poles, \(2p\), the number of commutator segments, \(k\), and the motor speed, \(n\), as:  
\[f_{r}=(2p\cdot k\cdot n)/(60\cdot\eta) \tag{2}\]  
where \(\eta\) is the largest common divisor of \(2p\) and \(k\).  
This component is important in brushed dc motors with a small number of coils, as in fractional horsepower brushed dc motors, because the amplitude of the ripple component is relatively large ([PERSON] et al., 2017).
## 2 Frequency Domain Filtering of Overlapping Patches
### Frequency Domain Filtering of Overlapping Patches
Elements of \(\tilde{P}_{\mathcal{F}}\) can be used to obtain a set of filtered overlapping patches in the spatial domain in the following way:  
\[\tilde{P}=\Big{\{}\text{crop}\Big{(}nm\mathcal{F}^{-1}(\tilde{p}_{\mathcal{F}} )\Big{)}\ \Big{|}\ \tilde{p}_{\mathcal{F}}\in\tilde{P}_{\mathcal{F}}\Big{\}}.\]  
The goal of cropping the resulting image patch in the spatial domain is to remove possible unwanted artifacts and distortions that occur while processing the boundary regions of an image. The set \(\tilde{P}\) is used for the \"synthesis\" of a filtered image. It is important to note that after processing a patch with MLMVN, we restore a zero-frequency coefficient of the Fourier transform by setting it equal to a zero-frequency coefficient of the respective input (unprocessed) patch. This step is valid because our modeled noise has a zero mean. Thus, we can preserve a respective mean over a patch that is being processed. This is important to avoid a shift in patch intensities, which would occur if the mean value were modified. For reconstructing an image from \(\tilde{P}\), we use a window of size \(t\times r\). By \"moving\" this window starting from the top-left corner with vertical step \(t\) in the range \([1,M-t]\) and with horizontal step \(r\) in the range \([1,N-r]\), and collecting all patches that lie inside the window at each step, we are able to reconstruct an image fragment of size \(t\times r\). This should be carried out by combining the intensities of these overlapping patches. Intensities in areas where patches overlap are restored by applying an aggregation function. In this work, we employed median and mean aggregation functions. Below, an algorithm (Algorithm 2) for the \"synthesis\" of a filtered image from processed overlapping patches in the frequency domain is provided. In general, the image noise filtering process proposed in this paper can be illustrated in Figure 3.  
The algorithm for the complete process of filtering a noisy image is outlined here (Algorithm 3). ```
1:Input:
2:\(\tilde{P}\): A set of filtered overlapping patches
3:\(m\): The width of a patch
4:\(n\): The height of a patch
5:\(t\): The width of a window
6:\(r\): The height of a window
7:\(agr\): An aggregation function
8:Output:
9:\(I_{\text{filtered}}\): A filtered image
10:Procedure:
11:for\(i\gets 1\) to \(M-t\) by \(t\)do
12:for\(j\gets 1\) to \(N-r\) by \(r\)do
13:\(R\leftarrow\emptyset\)
14: Collect the patches from \(\tilde{P}\):
15:\(R\leftarrow\left\{p_{(x,y)}\mid p_{(x,y)}\in\tilde{P},-(x+m<i\lor x>i+t\lor y +n<j\lor y>j+r)\right\}\)
16: Set the image intensities in the window:
17:\(I_{\text{filtered}}[i:i+t,j:j+r]\gets agr\Big{(}\left\{p_{(x,y)}\mid p_{(x,y )}\in R\right\}\Big{)}\)
18:endfor
19:endfor
20:return\(I_{\text{filtered}}\)
```  
**Algorithm 2** \"Synthesis\" of a filtered image from processed overlapping patches
# Understanding and Enhancement of Internal Clustering Validation Indexes for Categorical Data
## 4 Internal Clustering Validation Index: CUBAGE  
As discussed previously, the CVIs cannot effectively evaluate the partitions of different sizes if the separation measures or assumptions are absent, and the crude separation assumptions without respect to the attribute values are rather questionable.  
In this section, we first proposed a new method to measure the inter-cluster separation. Then, we present our algorithm to internally measure the clustering validation, namely, the clustering utility based on the averaged information gain of isolating each cluster (_CUBAGE_).
## 4 Results
### Evaluation Criteria  
To evaluate the performance of the trained models thoroughly, five evaluation metrics were used. The first criterion is the overall accuracy (\(Acc\)), which is determined by dividing the number of all correctly classified pixels by the total number of pixels (Equation (1)).  
\[Acc=\frac{\sum_{i=1}^{n}(TP_{i}+TN_{i})}{\sum_{i=1}^{n}p_{i}} \tag{1}\]  
where \(n\) is the number of images; \(TP_{i}\) and \(TN_{i}\) are the number of correctly labeled pixels in image \(i\); and \(p_{i}\) is the number of pixels in image \(i\).  
Although Acc is a very intuitive and thus common evaluation criterion, it is not very meaningful in cases where the distribution of classes is highly skewed, as is the case with the dataset we utilize (Table 2). Therefore, the metrics precision (Equation (3)), recall (Equation (2)), IoU (Equation (4)), and \(F_{1}\) score (Equation (5)) are used for each class \(j\):  
\[recall_{j}=\frac{\sum_{i=1}^{n}TP_{ij}}{\sum_{i=1}^{n}(TP_{ij}+FN_{ij})} \tag{2}\]  
\[precision_{j}=\frac{\sum_{i=1}^{n}TP_{ij}}{\sum_{i=1}^{n}(TP_{ij}+FP_{ij})} \tag{3}\]  
\[IoU_{j}=\frac{\sum_{i=1}^{n}TP_{ij}}{\sum_{i=1}^{n}(TP_{ij}+FP_{ij}+FN_{ij})} \tag{4}\]  
\[F_{1}^{j}=2*\frac{precision_{j}*recall_{j}}{precision_{j}+recall_{j}} \tag{5}\]  
where \(n\) is the number of images; \(TP_{ij}\) is the number of pixels in image \(i\) that are correctly predicted as class \(j\); \(FP_{ij}\) is the number of pixels in image \(i\) that are incorrectly predicted as class \(j\); and \(FN_{ij}\) is the number of pixels in image \(i\) that are incorrectly predicted as any class other than class \(j\).  
Since this is a multi-class problem statement, we first calculate the proposed metrics per class and then determine the mean among all classes Despite providing all the above metrics for a holistic evaluation of the models, the \(F_{1}\) score is used as the sole decisive evaluation score. Furthermore, the class _void_ carries essentially no information. However, it is still necessary as the data outside the study area boundaries of the commercial forest boundaries (in the case of masked ground truths) must be represented. Nonetheless, although the class void is required for training the DCNNs, it is omitted in the presentation of the results as it was nearly perfectly classified and carried no valuable information. Moreover, its presence in the metrics would distort the results and indicate a better model performance than the particular model can achieve in reality.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 4 Proposed A2C-Based Algorithm  
In this section, we detail our proposed Advantage Actor-Critic (A2C)-based dynamic migration and resource allocation approach. We discuss the transformation of problem \(\mathcal{P}\) into an MDP and describe the A2C-based algorithm.
# Proactive Scheduling and Routing of MRP-Based Production with Constrained Resources
###### Abstract  
This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution--the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures--not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%).  
MRP-driven production; proactive routing and scheduling; resource constraints; mathematical programming; optimization
## 3 Results and Discussion
### Volatile Compounds
ethylester & 2.15 \(\pm\) 0.97 & 3.54 \(\pm\) 2.49 & 3.77 \(\pm\) 3.09 & 6.28 \(\pm\) 8.87 & 1.16 \(\pm\) 1.34 & 9.36 \(\pm\) 5.65 \\ \hline
2-Ethewloxy ethanol & 0.75 \(\pm\) 0.29 & 4.27 \(\pm\) 2.09 & 77.59 \(\pm\) 35.09 & 6.98 \(\pm\) 4.52 & 20.30 \(\pm\) 8.44 & 33.09 \(\pm\) 67.68 \\ \hline
**Total Esters** & **29.04** & **160.78** & **220.00** & **99.41** & **102.68** & **275.48** \\ \hline
**Kotones (11)** & & & & & & \\ \hline
2-Butanone & 4.90 \(\pm\) 2.85 & 5.79 \(\pm\) 2.50 & 7.04 \(\pm\) 3.59 & 7.72 \(\pm\) 4.25 & 7.51 \(\pm\) 2.55 & 9.09 \(\pm\) 3.55 \\ \hline
2-Heplanene & 2.68 \(\pm\) 1.23 & 3.02 \(\pm\) 1.85 & 3.76 \(\pm\) 2.05 & 4.49 \(\pm\) 3.20 & 3.26 \(\pm\) 1.25 & 3.61 \(\pm\) 1.64 \\ \hline
2-Octanone & 3.43 \(\pm\) 2.22 & 4.24 \(\pm\) 2.89 & 5.05 \(\pm\) 3.66 & 6.34 \(\pm\) 3.65 & 5.59 \(\pm\) 2.38 & 6.63 \(\pm\) 2.22 \\ \hline
2-Nonanone & 5.72 \(\pm\) 3.25 & 7.77 \(\pm\) 2.01 & 1.60 \(\pm\) 0.22 & 1.34 \(\pm\) 1.02 & 1.74 \(\pm\) 0.80 & 1.04 \(\pm\) 0.55 \\ \hline
2-Decanone & 2.30 \(\pm\) 1.55 & 3.19 \(\pm\) 2.99 & 4.44 \(\pm\) 1.85 & 5.12 \(\pm\) 3.81 & 4.91 \(\pm\) 2.46 & 6.49 \(\pm\) 5.02 \\ \hline
2-Undecanone & 0.08 \(\pm\) 0.04 & 0.42 \(\pm\) 0.37 & 1.16 \(\pm\) 1.13 & 1.89 \(\pm\) 1.79 & 0.66 \(\pm\) 1.06 & 1.01 \(\pm\) 1.67 \\ \hline
2-Dodecanone & 0.83 \(\pm\) 0.31 & 1.64 \(\pm\) 1.10 & 2.45 \(\pm\) 2.83 & 3.74 \(\pm\) 2.79 & 2.99 \(\pm\) 1.19 & 4.03 \(\pm\) 2.60 \\ \hline
5-Methyl-2-Hexanone & 5.12 \(\pm\) 3.54 & 5.17 \(\pm\) 2.95 & 2.75 \(\pm\) 0.84 & 5.93 \(\pm\) 2.23 & 2.19 \(\pm\) 1.14 & 7.45 \(\pm\) 4.25 \\ \hline
5-Methyl-3-Heplanene & 2.28 \(\pm\) 1.95 & 6.78 \(\pm\) 2.44 & 8.38 \(\pm\) 5.12 & 12.76 \(\pm\) 6.89 & 8.71 \(\pm\) 4.88 & 19.46 \(\pm\) 10.17 \\ \hline
2-Methyl-4-heptanone & 3.41 \(\pm\) 1.99 & 7.71 \(\pm\) 2.81 & 8.77 \(\pm\) 10.99 & 14.84 \(\pm\) 10.08 & 8.31 \(\pm\) 6.71 & 19.46 \(\pm\) 13.17 \\ \hline
2-Piveridinone & 0.14 \(\pm\) 0.14 & 1.57 \(\pm\) 1.27 & 1.35 \(\pm\) 1.41 & 0.96 \(\pm\) 0.22 & 1.06 \(\pm\) 1.63 & 2.32 \(\pm\) 2.00 \\ \hline
**Total ketones** & **35.41** & **54.22** & **50.25** & **67.75** & **47.73** & **95.96** \\ \hline \hline
**Lactones (5)** & & & & & \\ \hline Gamma Nonalactone & 1.02 \(\pm\) 0.55 & 0.25 \(\pm\) 0.22 & 1.28 \(\pm\) 0.69 & 2.55 \(\pm\) 0.65 & 2.05 \(\pm\) 1.55 & 0.88 \Regarding the chemical class of esters, it was observed that their total amount increased with time, with the maximum value being noted on the 180 th day in both TC-(220 mg/kg) and SST- (275.48 mg/kg) ripened cheeses. The esters detected in large quantities in all cheese samples were acetic acid 2-phenylethylester, decanoic acid ethylester, and 2-etheynoxyethanol. Among the less abundant groups, alkanes and terpen, lactones, and ketones were more represented in both cheeses at 180 days. The volatile profile of white brine cheese in this study is close to those reported by other studies concerning this type of cheese [21; 31].
###### Abstract
[16] found that undercharged CO\({}_{2}\) systems lead to a fast decrease of the cooling capacity and COP, whereas overcharged CO\({}_{2}\) systems cause an abrupt increase of the compressor power input. [PERSON] et al. [17] experimentally investigated the charge characteristics and refrigerant migration behavior of an electric transcritical CO\({}_{2}\) MAC unit. The authors observed that a plateau region involving the optimal system performance occurred for a refrigerant charge between 1100 g and 1600 g at 35 \({}^{\circ}\)C, for which COP and cooling capacity achieved their optimal performance of 1.87 and 3.5 kW, respectively. An experimental study on the normalized refrigerant charge (NRC) of the MAC system was carried out by [PERSON] et al. [18] who found that the optimal NRC range for a transcritical CO\({}_{2}\) air conditioning system was between 0.111 and 0.321. [PERSON] et al. [19] experimentally compared the COPs and cooling/heating capabilities of MAC systems using R134a and CO\({}_{2}\) as work fluid. The optimal CO\({}_{2}\) charge amount was determined to be 600-650 g and the COP can reach 1.36 and 2.16 at 40 \({}^{\circ}\)C (cooling mode) and \(-\)20 \({}^{\circ}\)C (heating mode), respectively. Different from the above studies, [PERSON] et al. [20; 21] experimentally investigated the cooling and heating performance of a transcritical CO\({}_{2}\) MAC system for an electric bus. The results showed an optimal COP of 2.88 and a cooling capacity of 31.4 kW at 9 kg charge amount in cooling mode (outdoor temperature of 35 \({}^{\circ}\)C and indoor temperature of 19.5 \({}^{\circ}\)C) as well as an optimal COP of 1.78 and a heating capacity of 15.3 kW at 8.8 kg in heating mode (outdoor temperature of \(-\)20 \({}^{\circ}\)C and indoor temperature of 20 \({}^{\circ}\)C). From the literature above it can be concluded that there is little research on transcritical CO\({}_{2}\) air conditioning systems for electric buses. In order to fill this research gap, a test rig of an electric bus air conditioning system was built and employed for evaluating the influence of the refrigerant charge on the performance of the air conditioning unit based on the energy and exergy analyses.
# A Lightweight and Practical Anonymous Authentication Protocol Based on Bit-Self-Test PUF
## 1 Introduction
Therefore, this paper starts the research from the lightweight PUF reliability enhancement method and the helper data algorithm based on PUF, proposing a lightweight HDA model based on BST-PUF [27]. In the key generation and recovery process, a reliable response is extracted from the original response \(R\) by using the reliable flag \(F\), and then an entropy extractor is used to generate a high entropy key that is reliable and uniform. The algorithm does not use any error correction codes, which greatly reduces the implementation complexity and execution overhead that can generate a large number of highly reliable keys with less hardware resource consumption, which significantly reduces the application cost of PUF. Moreover, the helper data and the PUF response are independent of each other, and will not reveal any information about the key. In addition, a lightweight practical anonymous authentication protocol is designed based on this HDA and enables mutual authentication between the terminal device containing a PUF entity and the server. The protocol uses the reliable flag to pre-process PUF responses, instead of the original error correction mechanism. While using PRF instead of hash functions and using XOR operation instead of symmetric encryption algorithms, it significantly reduces the overhead while ensuring security. During protocol execution, the server does not need to store a large number of CRPs, which reduces the storage overhead on the server side and avoids the risk of leakage of CRPs; during protocol authentication, the device does not transmit its ID explicitly, which prevents the device from being tracked and protects privacy; after security analysis, the proposed protocol proves to be resistant to various attacks against the protocol and PUF, such as modeling attacks, cloning attacks, and desynchronization attacks. The proposed protocol is also verified on the Zynq-7000 SoC XC7Z010 FPGA development board, and the experimental results show that the proposed protocol has significant advantages over similar protocols in terms of implementation overhead. The schematic diagram process of the proposed lightweight anonymous authentication protocol based on the novel HDA is shown in Figure 1.
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 3 Signal Processing and Design
### Data Component  
Let us focus now on the signal processing and design of the data component of a PNT system. First of all, it is important to note that the primary goal of a PNT signal is the ranging capabilities needed to obtain the user's PNT solution. Therefore, the signal design parameters should be optimized, targeting the sensitivity and accuracy performance required for the (pilot) ranging component. Data dissemination capabilities are dependent on the system. For instance, for standalone GNSS receivers [18], a very small amount of data is needed to solve PNT (e.g., 50 bps is enough for GPS L1/CA), but a larger amount of data is needed for an SBAS receiver [14] (e.g., 250 bps for SBAS L1). Furthermore, different requirements are needed in terms of data latency, data rate, or quality depending on the application [14]. When targeting low-complexity GNSS, we should follow the same signal design followed in [11]; that is, in this section, we consider a chirp period design given by the sensitivity analysis (see Figure 2). Then, we define the relevant KPIs for the data component, namely, the bit error rate (BER) and achievable bit rate, \(R_{\text{b}}\). We do so for two different CSS modulations: the BOK- and FSK-CSS signals.
## 4 Simulation Results and Analysis
### Impact of Task Size on the System  
Figures 4 and 5, respectively, show the influence of task input data sizes on system delay and energy consumption. As shown in Figure 4, the delay of all the five schemes increases with the increase in task input data. Since there is no transfer and offload of tasks in local computing, the delay value of local computing remains unchanged. However, with the increase in offloading data, the MEC scheme will compete for communication resources  
Figure 3: Comparison of system energy consumption under the different number of vehicle terminals.  
Figure 2: Comparison of system delay under the different number of vehicle terminals.  
and edge node computing resources, so its delay is higher than that of local computing. The IBES computing scheme can quickly process some tasks requiring a large amount of input data. As can be seen from the figure, when the task data size is 3.0 MB, the delay of the proposed scheme is reduced by 4.55%, 44.89%, 20.75% and 12.14%, respectively, compared with the Local algorithm, MEC algorithm, PSO algorithm, and BES algorithm.  
Figure 5 shows the impact of task data sizes on system energy consumption. It can be seen that as the task data size increases, the energy consumption of the five calculation scheme's increases, but the energy consumption of IBES is much smaller than that of other schemes. As the amount of task input data increases, local computing does not involve data input, and its energy consumption changes steadily. The energy consumption changes gradually increase in other schemes, but IBES performs better than other schemes. Therefore, when faced with large data input, the energy consumption performance of the proposed scheme is far superior to other modes. As can be seen from the figure, when the task data size is 3.0 MB, the delay of the proposed scheme is reduced by 37.79%, 26.31%,  
Figure 4: Comparison of time delays under different task data size.  
Figure 5: Comparison of energy consumption under different task data size.  
14.38% and 16.76%, respectively, compared with the Local algorithm, MEC algorithm, PSO algorithm, and BES algorithm.
## 2 Materials and Methods  
### Materials  
#### 2.1.1 MODIS GPP Algorithm  
The GPP calculation used in the MODIS GPP algorithm (MOD17A2) is based on the original logic of [PERSON] (1972) [32] that relates gross photosynthesis to the amount of photosynthetically active radiation (PAR) absorbed by plants over a growing season. As described in detail by [PERSON] _et al._ (2000) [33], the algorithm was developed as follows:  
\[\mathrm{GPP}\;=\;\mathrm{PAR}\times\mathrm{FPAR}\times\varepsilon \tag{1}\]  
where FPAR is the fraction of PAR absorbed by the vegetation canopy, and PAR is determined as a fraction of the downward solar shortwave radiation (SWRad):  
\[\mathrm{PAR}\;=\;\mathrm{SWRad}\times 0.45 \tag{2}\]  
The magnitude of LUE \(\varepsilon\) in Equation (1) is calculated as  
\[\varepsilon\;=\;\varepsilon_{\mathrm{max}}\times\mathrm{T_{s}}\times\mathrm{ VPD_{s}} \tag{3}\]  
where \(\varepsilon_{\mathrm{max}}\) is the maximum LUE obtained from a Biome Properties Look-Up Table (BPLUT) on the basis of biome type, and \(\mathrm{T_{s}}\) and \(\mathrm{VPD_{s}}\) are the attenuation scalars from cold temperature (low daily minimum temperature, \(\mathrm{T_{min}}\)) and water stresses (high daily vapor pressure deficit, VPD), respectively [34; 35].  
Following the MODIS GPP algorithm, essential MODIS product images from 2003 to 2005 were downloaded from the Level 1 and Atmosphere Archive and Distribution System (LAADS) website [36]. Using the latitudes and longitudes of the flux tower sites, we obtained 5 km \(\times\) 5 km cutouts centered over each tower location that represented the: (1) land cover classification (MCD12Q1, C5.1, 500-m resolution, annual product); (2) LAI and FPAR (MOD15A2, C5, 1-km resolution, eight-day product); and (3) GPP (MOD17A2, C5.5, 1-km resolution, eight-day product).  
In particular, MCD12Q1 is used in the MOD15A2 and MOD17A2 calculations. Unlike the MOD15A2 algorithm, which uses MCD12Q1 Land Cover Classification Type 3 (LAI/FPAR scheme) to calculate FPAR, the MOD17A2 algorithm uses MCD12Q1 Land Cover Classification Type 2 (University of Maryland land cover classification scheme) to map biome-specific physiological parameters (\(\varepsilon_{\mathrm{max}}\), maximum and minimum air temperature and VPD) according to the BPLUT [37].