esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions
\begin{tabular}{l l l}
**Symbol** & **Description** & **Unit** \\ CAPEX & Capital Expenditure & EUR \\ Cashflows(i) & Sum of the investment's total expenses and income in year i & EUR \\ CC & Combined Cycle & - \\ CCS & Carbon Capture and Storage & - \\ CRETE RES[T] & Creat RES production recorded in hour T & MWh \\ DIRES[T] & RES production on load in hour T & MW \\ ELCAP & Electrolyzer System's hourly maximum H2 production & MWh \\ ELECLYS[T] & Electrolyzer System's hourly H2 production in hour T & MWh \\ ELEFF & Electrolyzer System's overall efficiency & - \\ ExpL & Maximum hourly energy exported & MWh \\ GRID[T] & Total energy imported/exported in hour T & MWh \\ FC[T] & Fuel Cell System's electricity production in hour T & MWh \\ F.C. CAP. & Fuel Cell System's hourly maximum electricity production & MWh \\ F.C. EFF. & Fuel Cell System's overall efficiency & - \\ HEnEx & Hellenic Energy Exchange & - \\ HPS & Hydropower Pumped Storage & - \\ Hydropogenmass[T] & Produced H2 for sale in mass units & kg \\ IND. H2[T] & Produced H2 for sale in energy units & MWh \\ LHV & Lower Heating Value & - \\ LL & Conventional System's Technical Lower Limit & MW \\ LOAD[T] & System's Demand in hour T & MW \\ LOSSES[T] & Hydrogen System's total losses in hour T & MWh \\ MCP & Market Clearing Price & - \\ NPV & Net Present Value & EUR \\ PEM & Proton Exchange Membrane & - \\ PV & Photovoltaic & - \\ PVcap & PV system's installed capacity & MW \\ r & Annual Discount Rate & - \\ REJECT[T] & RES production's rejected energy & MWh \\ RES & Renewable Energy Sources & - \\ RES[T] & RES electricity production in hour T & MWh \\ RES O&M cost & Operation and Maintenance cost of RES system & EUR \\ SG & Smart Grid & - \\ ST[T] & Hydrogen Tank’s State of Charge in hour T & MWh \\ STCAP & Hydrogen Tank’s storage capacity & MWh \\ Total O&M cost & RES and hydrogen systems' operation and maintenance cost & EUR \\ TOTCONV[T] & Conventional System's total production in hour T & MWh \\ UPLL & Conventional System's Technical Upper Limit & MW \\ V2G & Vehicle to Grid & - \\ WT & Wind Turbine & - \\ WTcap & Wind turbine system's total installed capacity & MW \\ \end{tabular}
## 4 Experimental Results and Discussions  
We evaluated the recognition performance of our proposed PSI-CNN model on two datasets--Labeled Faces in the Wild (LFW) [13] and our custom dataset derived from CCTV cameras. For this purpose, we used a 12 GB NVIDIA TITAN X graphics card. For comparisons, we use a modified version of the VGG face model as the baseline. This is because the original VGG model utilizes two fully connected layers with each outputting 4096-dimensional vectors, meaning that the model requires large amount of computation power and memory. To bypass this issue, we replaced the aforementioned fully connected layers by a single smaller (1024-dimensional) fully connected layer. From our preliminary empirical investigations, we were able to shrink the VGG model by almost 60% of its original size, whilst maintaining 98.4% average matching accuracy on the above datasets. This implies that using a fewer dimensional fully connected layer can be a computationally-efficient way of implementing a high-performance face recognition method.
## 3 Results
### Molecular Docking Investigations on Queen Bee Acid Binding by Protein  
Molecular docking is crucial for evaluating the reliability of network pharmacological predictions. In this study, six core target proteins (TNF, AKT1, INS, STAT3, ESR1, and VEGFA) were molecular-docked with QBA. The PPI network analysis identified six hub genes--TNF, AKT1, INS, STAT3, ESR1, and VEGFA--with node degrees exceeding 50, indicating their central roles in aging-related signaling pathways. To mechanistically validate this association, molecular docking simulations were performed using AutoDock Vina (version 1.1.2, 2010) to investigate potential interactions between QBA and the encoded proteins of these genes. Binding energy (kcal/mol) and interaction modes were analyzed to characterize the molecular basis of QBA's anti-aging effects. In Table 2, center X, center Y, and center Z represent the three directional coordinates of the center of the search space; size X, size Y, and size Z are the sizes of the specified search space. The binding energies of these six proteins to QBA were \(-\)6, \(-\)7, \(-\)7.2, \(-\)6.3, \(-\)6.8, and \(-\)5.7 kcal/mol (Table 2). This result indicates that these six proteins can bind to QBA, suggesting they have significant effects in treating aging. Interestingly, QBA forms hydrogen bonds with amino acid residues of TNF (Glu79, Lys78, Gln40) (Figure 6A), AKT1 (Thr195, Glu191, Thr160) (Figure 6B), INS (Glu13, Ser9, His10) (Figure 6C), STAT3 (Pro578, Val580, Asp464, Glu465) (Figure 6D), ESR1 (Arg394, Gly390, Glu353) (Figure 6E), and VEGFA (Asp34, Glu42, Tyr45) (Figure 6F). QBA is connected to at least three amino acid residues of each protein, indicating multiple binding sites. TNF, AKT1, INS, STAT3, ESR1, and VEGFA bound to QBA, with binding energies all below \(-\)5.0 kcal/mol.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **PDB** & \multicolumn{3}{c}{**Center**} & \multicolumn{3}{c}{**Size**} & \multirow{2}{*}{**Binding Energy**} \\  & **ID** & **X** & **Y** & **Z** & **X** & **Y** & **Z** \\ \hline TNF & 1 TNR & 44.59 & 23.62 & 40.88 & 68 & 74 & 66 & \(-\)6 \\ AKT1 & 4 EKL & 19.78 & \(-\)0.59 & 16.17 & 48 & 52 & 60 & \(-\)7 \\ INS & 1 OS4 & 5.54 & 10.52 & 20.81 & 40 & 54 & 52 & \(-\)7.2 \\ STAT3 & 6 NUQ & \(-\)2.03 & 18.68 & 25.84 & 78 & 116 & 94 & \(-\)6.3 \\ ESR1 & 7 UJW & 0.56 & 30.07 & 49.67 & 78 & 76 & 94 & \(-\)6.8 \\ VEGFA & 6D3O & 25.03 & \(-\)38.14 & 4.83 & 44 & 68 & 42 & \(-\)5.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The molecular docking parameters for the energy changes of QBA and target proteins.  
Figure 5: KEGG enrichment analysis of the top 15 pathways. The core targets are on the left, and the pathways are on the right. The number of enriched core targets in each KEGG item is depicted as a circle, with \(p-\)values indicated in different colors.
## 2 Literature Review
### Centralization vs. Decentralization Strategies
The aim of the thesis of [PERSON] and [PERSON] [28] was to optimize, with the use of simulation modelling (in a public transport company), inventory management in the centralized spare parts warehouse. The findings that were obtained in the article demonstrated that the storage centralization strategy is more effective than the decentralization strategy. For the centralized approach, procurement costs as well as storage costs were found to be higher. [PERSON] et al. [22] compared the arguments for both centralization and decentralization of warehouses. Centralization reduces fixed storage costs [11], involves less capital, and reduces the number of warehouse workers. [PERSON] [29] proved that inventory centralization does not necessarily lead to longer delivery times, even if the average distance to customers increases. In addition, inventory centralization enables more effective quality control and greater inventory visibility [30]. On the other hand, arguments in favor of decentralization mainly relate to advantages in terms of delivery costs and times [31] and greater closeness to customers.. Furthermore, [PERSON] [32] wrote that \"in practice, decentralization of control is generally more attractive because a centralized system requires strategic planning and alignment throughout the chain.\" Nevertheless, as [PERSON] (2016) points out, in applying the Square Root law, we are limited by certain assumptions [33]. For example, that replenishment of inventory, both cyclical and safety stock, is done in economic order quantities (EOQ). Despite this, for many years no one has contested the application of the EOQ method.
# Improved Electrochemical-Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
## 2 Materials and Methods
### Error Analysis and Models Comparison  
Once the parameters are estimated using the least squares method, the Root Mean Square Errors (RMSEs) of the fitted curves are computed, and the reliability of the parameter estimates is evaluated using the covariance matrix. For \(n\) parameters \(\theta_{1},\theta_{2},\ldots,\theta_{n}\), the covariance matrix \(\mathcal{C}\) is expressed, as shown in Equation (20).  
\[\mathcal{C}=\begin{pmatrix}Var(\theta_{1})&Cov(\theta_{1},\theta_{2})&\ldots& Cov(\theta_{1},\theta_{n})\\ Cov(\theta_{2},\theta_{1})&Var(\theta_{2})&\ldots&Cov(\theta_{2},\theta_{n}) \\ \vdots&\vdots&\ddots&\vdots\\ Cov(\theta_{n},\theta_{1})&Cov(\theta_{n},\theta_{2})&\ldots&Var(\theta_{n}) \end{pmatrix} \tag{20}\]  
The diagonal elements \(Var(\theta_{i})\) represent the variances of the individual parameter estimates, indicating the variability in each estimation. The off-diagonal elements \(Cov(\theta_{i},\theta_{j})\) represent the covariance between pairs of parameters, reflecting their linear relationship.  
In this model, during the optimization process, each iteration produces a set of parameters that typically improves or maintains the objective function value. These intermediate estimates can be interpreted as a set of plausible solutions within the parameter space. By treating them as empirical samples, it is possible to compute a covariance matrix that captures the spread and correlation of the parameters across iterations. The empirical covariance matrix can then be used to construct confidence (or error) ellipses for selected pairs of parameters. These ellipses are derived from the \(2\times 2\) submatrices of the covariance matrix \(\mathcal{C}\in\mathbb{R}^{nxn}\), which are symmetric and positive definite. The rescaling of the parameters is needed because of the difference in order of magnitude and units of measure of the estimated parameters.  
The orientation of the ellipse is determined by the covariance between the two parameters. Specifically, the orientation of the ellipse can be described by an angle \(\alpha\), measured with respect to the \(x\)-axis: the closer \(\alpha\) is to \(\pm 45^{\circ}\), the stronger the correlation between the parameters, while \(\alpha=0^{\circ}\) or \(\pm 90^{\circ}\) indicates no correlation. On the other hand, a larger ellipse indicates higher variances in the estimated parameters across iterations, suggesting that the algorithm has explored a broader region of the parameter space. While this may reflect a more thorough search of the objective function landscape, it can also be a sign of reduced convergence toward stable parameter values. For this reason, the confidence ellipses--which provide a qualitative representation of the parameter uncertainty and correlation given by the optimization model--should be considered together with the RMSE values to enable a more comprehensive comparison between the electrochemical and the electrochemical-mechanical approach.
## 2 Background of Modeling with NURBS and Boundary elements  
### Modeling Geometric with NURBS  
A NURBS is a parametric curve that can generate lines, conics, and circles accurately. Due to this versatility, it has been widely adopted in computer-aided design programs (CAD). Mathematically, a NURBS curve is defined by Equation (1), where \(n\) is the control points number, \(\xi\) symbolizes the function parameter, \(B\) represents the control points, \(w\) the weights, \(N\) denotes the B-spline basis functions, Equations (2) and (3), with order \(k\) (degree \(k-1\)). Figure 1 shows the basis function as a function of \(\xi\).  
\[C(\xi)=\frac{\sum_{i=1}^{n+1}B_{i}w_{i}N_{i,k}\left(\xi\right)}{\sum_{i=1}^{n +1}w_{i}N_{i,k}\left(\xi\right)}=\sum_{i=1}^{n+1}B_{i}R_{i,k}\left(\xi\right), \tag{1}\]  
\[N_{i,1}(\xi)=\left\{\begin{array}{cc}1&\xi_{i}\leq\xi<\xi_{i+1}\\ 0&\text{otherwise}\end{array}\right., \tag{2}\]  
\[N_{i,k}(\xi)=\frac{(\xi-\xi_{i})N_{i,k-1}(\xi)}{\xi_{i+k-1}-\xi_{i}}+\frac{( \xi_{i+k}-\xi)N_{i+1,k-1}(\xi)}{\xi_{i+k}-\xi_{i+1}} \tag{3}\]  
The parameter \(\xi\) gets its values from the knot vector \(\Xi=\left[\xi_{min}\dots\xi_{max}\right]\), which goes from a minimum \(\xi_{min}\) to a maximum value \(\xi_{max}\), and its length is \(n+1+k\). If \(C\) is a closed curve, the values at the ends of the knot vector are repeated \(k\) times. The weights, \(w\), allow local control in the shape of the curve; they move the curve closer or farther to the corresponding control point.  
The curve continuity can be controlled with the repeated values in the knot vector \(\xi\). On the other hand, a NURB curve is \(k\) times derivable, and its first derivate is shown in Equation (4).  
\[C^{\prime}(\xi)=\sum_{i=1}^{n+1}B_{i}R_{i,k}\left(\xi\right), \tag{4}\]  
Figure 1: Basis function for \(k=3\) and \(\Xi=\left[0\ 0\ 0\ 1\ 2\ 3\ 4\ 4\ 4\ \right]\).  
where:  
\[R^{\prime}{}_{ijk}\left(\xi\right)=\frac{w_{i}N^{\prime}{}_{i,k}(\xi)}{\sum_{i=1 }^{n+1}w_{i}N_{i,k}(\xi)}-\frac{w_{i}N_{i,k}\sum_{i=1}^{n+1}w_{i}N^{\prime}{}_{ i,k}(\xi)}{\left(\sum_{i=1}^{n+1}w_{i}N_{i,k}(\xi)\right)^{2}}, \tag{5}\]  
In the following sections, we can see that the displacements and tractions are a function of \(\xi\) parameter. This causes the method to inherit the properties of the NURBS and accurately represents the body to be analyzed.
# Adaptive Relay Selection Scheme by Using Compound Channel
## 3 The Conventional Relay Selection Schemes  
In this section, the conventional schemes for selection of relay are considered. The various relay selection schemes have been proposed to enhance signal to signal-to-noise ratio (SNR) of receiving signal for different system environments [32; 33; 34]. The various schemes have a different criteria. In [35; 36; 37], relay selections are proposed to enhance energy efficiency so power consumption is the most important factor for the relay selection. The performance of the system always depends on the channel condition of the relay. The relay selection scheme is considered according to channel of the relay. Therefore, the most used criterion is the channel magnitude based on CSI from \(R\). The relay selection schemes are explained as follows.  
Figure 1: Two-hop MIMO relaying system.
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 5 Performance Metrics  
To evaluate the performance of the proposed model, we employed several standard classification metrics: accuracy, precision, recall, F1-score, and inference time. These metrics provide a comprehensive understanding of the model's effectiveness and efficiency.
## 2 Materials and Methods
### Social-Emotional Music Feature Extraction
#### 2.3.1 Mel-Frequency Cepstral Coefficients Processing  
MFCC is a widely used audio feature extraction technique in music classification. The core concept of MFCC is to simulate the human auditory perception of different frequencies by converting an audio signal into a set of key feature coefficients to capture its time-frequency characteristics. The MFCC computation process consists of several steps. First, the audio signal is segmented into short time frames to capture temporal variations. Next, the Fourier transform (FT) transforms the signal into the frequency domain. Then, a Mel-frequency filter bank is applied to emphasize low-frequency features while attenuating high-frequency components, aligning with human auditory perception. Subsequently, the logarithm of the filtered spectral energy is computed to compress the dynamic range. Finally, the Discrete Cosine Transform (DCT) is performed to reduce dimensionality, extracting 12 to 13 key feature coefficients, which form an audio feature vector for further classification and recognition. This study analyzed three social-emotional music elements--perspective-taking, positive outlook, and problem-solving--using Mel-frequency cepstral coefficients (MFCCs). Results indicated that the perspective-taking type exhibited smoother spectral transitions, possibly reflecting melodic continuity and stability, aligning with calm emotional characteristics. The positive outlook type demonstrated more significant variability in cepstral coefficients, potentially reflecting dynamic and expressive melodic qualities consistent with optimism and active emotional states. Lastly, the problem-solving type showed broader spectral energy distributions, likely associated with diverse emotional expressions and more dynamic musical structures.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 5 Case Study: An Inclined Single-Tower Cable-Stayed Bridge
### Calculation of RCS at Each Construction Stage  
Replace the hinge constraint at the bottom of the tower with a fixed constraint in the overall analysis model of the bridge, keep the tension of the stayed cable unchanged, and perform the forward analysis to obtain the reaction force at the bottom of the tower at each construction stage (these details are shown in Table 3). The direction of the coordinate axis can be determined by referring to Figure 17. The maximum value of the vertical force occurs in the final construction stage (stage16) takes a value of 51,157 kN. The maximum reaction torque at the bottom of the tower occurs in the completion stage of the tensioning of the main beam cable (stage14), and takes a value of 1384 kN\(\cdot\)m.  
In this case, the friction coefficient of UHS was provided by the manufacturer, which is \(\mu\) = 0.15. The curvature radius of the contact surface is \(R_{0}\) = 1.5 m and the central angle is \(\theta\) = 23.5\({}^{\circ}\).  
The reaction torque under each vertical force (in Table 2) is compared with the critical torque calculated by Equation (6) for the corresponding vertical force, and the comparison results are shown in Figure 18. The results show that the torque of the actual construction stage was less than the critical torque. It can be concluded that UHS is always in the state of static friction during the whole construction process because of the existence of the friction between the top plate and the bottom basin. Therefore, UHS cannot be simulated according to the ideal hinge support, and its actual rotational parameters (RCS) should be calculated according to Equation (15).  
Figure 17: Integral finite element model of the cable-stayed bridge.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Construction Step**} & \multirow{2}{*}{\
\begin{tabular}{c} **Vertical Force Fz** \\ **/kN** \\ \end{tabular} } & \multirow{2}{*}{**Reaction Torque Mz**} & \multirow{2}{*}{**Construction Step**} & \multirow{2}{*}{
\begin{tabular}{c} **Vertical Force Fz** \\ **/kN** \\ \end{tabular} } & \multirow{2}{*}{**Reaction Torque Mz**} \\  & & & & & **/kN** \\ \hline Stage1 & 5590 & 9 & Stage9 & 47,712 & 1066 \\ Stage2 & 16,253 & 5 & Stage10 & 48,504 & 1202 \\ Stage3 & 25,270 & 65 & Stage11 & 49,305 & 1269 \\ Stage4 & 32,675 & 220 & Stage12 & 50,002 & 1318 \\ Stage5 & 38,161 & 526 & Stage13 & 50,335 & 13
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 3 Materials and Methods
### Unsupervised Pre-Training for ReID  
Some works believe that ReID will not greatly improve the accuracy of the MOT challenge, but this paper believes that ReID is indispensable for solving the problem of occlusion and irregular motion.  
Person ReID has always played an important role in MOT tasks. After the object detection model obtains the position and size of the target, the patch containing the object will be cropped from the original input image and input into the module, so as to obtain the feature embedding of the object in the image as the discriminating condition of the target trajectory. Although person ReID plays an irreplaceable role in MOT, the existing methods generally integrate the model trained on the person ReID dataset directly into the whole model, which is too rough. Moreover, due to the large domain gap between the person ReID dataset and the MOT dataset, the feature embeddings captured by the person ReID model are less credible.  
To solve the above problems, this paper adopts the strategy in [18]. First, the MoCo v2 [19] self-supervised model is used to perform unsupervised pre-training on the large-scale unlabeled dataset LUPerson, and the domain adaptability of the person re-ID model is improved through the sample identity diversity of the large-scale dataset and data augmentation strategies. After pre-training, this paper fuses multiple person ReID datasets such as DukeMTMC [20], Market1501 [21], and MSMT17 [22] to form a labeled hybrid dataset and conducts supervised training here to further improve the performance of the model on the premise of ensuring domain adaptability.  
In the past, the person ReID model generally used ResNet-50 [23] as the backbone network. Although the accuracy is high, the huge amount of parameters greatly improves the inference time of the tracking process, which is not conducive to the application of this technology in real scenarios. To this end, this paper uses OSNet [24] to replace the original feature extraction network, which greatly reduces the amount of parameters and computation.
# Sub-Milliwatt Transceiver IC for Transcutaneous Communication of an Intracortical Visual Prosthesis
## 5 Results
### Non-Coherent BPSK Receiver  
#### 5.2.1 IC Measurement Results  
We connect directly without coils delivering a 200 mV modulated signal level into the receiver to examine its power consumption. Figure 16 shows the transmitted input bits and the recovered output for the receiver. These are recorded using the following settings: 1.3 V supply, a Vcntrl comparator setting of 0.6 V (the input common mode voltage level), and a current source for oscillator controlled at 9.3 \(\mu\)A. To better illustrate internal signal of the receiver, Figure 17 shows the internal timing diagram from simulation, in which the demodulation of the received signal can be observed. The BPSK receiver consumed 0.2 mW while demodulating 1 Mbps data rate which was on a 4 MHz carrier. Figure 18 shows the measured power consumption of the receiver versus supply voltage at a 1 MHz repeated square wave cycle. The purpose of Figure 18 is to show the dependence of the power consumption on the carrier frequency and the supply voltage. A fixed repeated data square wave cycle of 1 MHz has two times the rate of a 1 Mbps bit-stream and should have a higher power consumption than the reported 0.2 mW at 1 Mbps. The decrease in power consumption as the supply voltage is lowered can be observed. The higher power consumption at a 5 MHz carrier demonstrates the predominately digital architecture. The simulated power breakdown of the receiver is as follows: 35% is consumed in the 1-bit ADC, 35% in the digital demodulator, and 30% in the oscillator(clock).  
Figure 15: RF power versus voltage supply for the unoptimized IR-UWB Transmitter at 50 Mbps data rate vs supply voltage, with fixed 1 k\(\Omega\) resistance for current control.  
Figure 16: Measured BPSK receiver recovered bits on 1 Mbps on 4 MHz carrier.  
Figure 17: Simulated waveforms and timing diagram of the BPSK receiver.  
Figure 18: Measured power consumption of the receiver vs supply voltage at 1 MHz repeated data square wave cycle.