esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Extraction of the Word-Based SISO Semisystolic Processor
### Word-Based SISO Projection Function
Through the same clock periods, the updated words \(X\), \(Y\), \(P\), and \(S\), saved in FIFO-X, FIFO-Y, FIFO-P, and FIFO-S, as well as the words of \(E_{n-1}^{i-1}\), \(E_{n-2}^{i-1}\), \(E_{2i-2}\), \(E_{2i-1}\), \(F_{2i-2}\), and \(F_{2i-1}\) are serially passed (one word at each clock period) to the corresponding inputs of the semisystolic array block to compute serially (one word at each clock period) the intermediate partial results of \(X\), \(Y\), \(P\), \(S\), and \(E\);
5. At clock period \(t=\left\lceil\frac{d}{k}\right\rceil\left\lceil\frac{n}{k}\right\rceil+1\), the control signal \(u\) in the dark blue PE of the postprocessing array, Figure 4, should be assigned a zero value (\(u=0\)) to enable the tristate buffers designated in Figure 8 to serially transfer the updated bits of \(s_{n-1}^{c}\) and \(y_{n-1}^{c}\) to the remaining light blue nodes of the postprocessing array. The bits of \(s_{n-1}^{c}\) and and \(y_{n-1}^{c}\) alongside the bits \(g_{j^{\prime}}\), \(x_{j}^{d}\), \(y_{j-1}^{c}\), \(p_{j}^{d}\), and \(s_{j-1}^{c}\) are used to determine the final words of the products \(Q\) and \(R\);
6. At clock period \(t=\left(\left\lceil\frac{d}{k}\right\rceil+1\right)\left\lceil\frac{n}{k}\right\rceil\), the control signal \(v\), indicated at the right side of the postprocessing array designated in Figure 4, should be forced to have a zero value (\(v=0\)) to force the least significant bits of \(y_{d}\) and \(s_{d}\), through the AND gates, to have zero values as shown at the rightmost edge of the DG shown in Figure 2;
7. Through clock periods \(t\geq\left\lceil\frac{d}{k}\right\rceil\left\lceil\frac{n}{k}\right\rceil+1\), the final output words \(Q\) and \(R\) produced from the postprocessing array will be loaded serially (one word at each clock period) in registers \(Q\) and \(R\) as presented in \[o = i\bmod k\]4  
It is worth noticing that the updated words of \(E\) are produced from the dark orange PEs of the semisystolic array, shown in Figure 3, starting from the second clock period. Therefore, the words of \(X\), \(Y\), \(P\), \(S\), \(E_{n-1}^{i-1}\), \(E_{n-2}^{i-1}\), \(E_{2i-2}\), \(E_{2i-1}\), \(F_{2i-2}\), and \(F_{2i-1}\) should be delayed by one clock period to synchronize the operation. This is implemented by adding delay elements (represented by the D squares) to the semisystolic array, as shown in \[o = i\bmod k\]5
###### Abstract  
Electric vehicles can serve as controllable loads, storing energy during off-peak periods and acting as generation units during peak periods or periods with high electricity prices. They function as distributed generation resources within distribution systems, requiring controlled charging and discharging of batteries. In this paper, we address the problem of the optimal allocation of parking lots within a distribution system to efficiently supply electric vehicle loads. The goal is to determine the best capacity and size of parking lots to meet peak hour demands while considering constraints on the permanent operation of the distribution system. Using the particle swarm optimization (PSO) algorithm, the study maximizes total benefits, taking into account network parameters, vehicle data, and market prices. Results show that installing parking lots could be economically profitable for distribution companies and could improve voltage profiles.  
e pacs: 12 February 2025 12 February 2  
locations of DG units are determined. Sound decision-making in this regard can provide benefits to the distribution network, suppliers, and customers alike.  
Optimal allocation of parking lots [7; 8; 9; 10], as a new type of distributed generation (DG), should be prioritized alongside other resources for electricity generation. One of the key distinctions between parking lots and other traditional distributed generators (DGs) is the stochastic nature of their output. Due to the stochastic nature of EV owners' behavior, the output of parking lots is also stochastic. Parking lots serve as charging stations for EVs for driving purposes. Parking lots function as storage devices, storing electrical power in the batteries of vehicles during times of low electricity prices and delivering power to the distribution system during times of high electricity prices.  
High penetrations of distribution-connected storage devices or plug-in vehicles can have adverse impacts on the grid due to their charging loads, which are often randomly located or unmanaged additions. However, the optimal allocation of parking lots can help mitigate these issues by reducing network losses, improving voltage profiles, and consequently bringing economic benefits for the distribution system company (DISCO).
# Deep Learning-Based Method for Classification and Ripeness Assessment of Fruits and Vegetables
## 2 Theoretical Background
### Metrics for Deep Learning Model Evaluation  
There are two general types of evaluation metrics for deep learning models, classification metrics and regression metrics. In this study, the classification category is used because the topic is image classification, so the metrics used are accuracy, precision, recall, and F1-score. These metrics are shown in Equations (1)-(4).  
Accuracy is important because it gives us an overall sense of how well the model is performing. This metric reports the percentage of all predictions that were correct. For example, if a model is 90% accurate, it has correctly identified 90 out of 100 fruits or vegetables it has been tested on. The values obtained for true positives (_TP_), true negatives (_TN_), false positives (_FP_), and false negatives (_FN_) are used to determine the accuracy.  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{1}\]  
A _TP_ is a result where the model correctly predicts the positive class. Similarly, a _TN_ is a result where the model correctly predicts the negative class. A _FP_ is a result where the model incorrectly predicts the positive class. And a _FN_ is an outcome where the model incorrectly predicts the negative class.  
On the other hand, precision shows how reliable the model's positive predictions are. In other words, it represents the proportion of positive predictions that were correct. For example, if a model is 80% accurate, then 80 out of 100 fruits or vegetables that it predicted would be ripe were ripe.  
\[Precision=\frac{TP}{TP+FP} \tag{2}\]  
Recall indicates how many ripe fruits or vegetables are missing from the model. This is the proportion of actual positives that were correctly identified. For example, if a model has a recall of 70%, then 70 out of 100 ripe fruits or vegetables were correctly identified as ripe.  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
F1-score is important because it provides a balanced measure of precision and recall; the metric reports a weighted harmonic mean of precision and recall. It is often considered a more balanced measure of performance than accuracy, especially when there is an unequal number of positive and negative examples in the dataset.  
\[\text{F1}-\text{Score}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{4}\]  
In general, a high accuracy score is desirable, but it is also important to consider the other metrics, as high accuracy could also be a sign of overfitting. For example, a model with a high accuracy score but low precision may have a high number of false positives. This could be problematic if the model is used to make decisions about the ripeness of fruits or vegetables, as it could misclassify ripe fruits or vegetables as unripe.  
Confusion matrix is another type of evaluation metric for deep learning models. This type of metric is more visual, so this table shows the true and predicted classifications for a set of data. It can be used to visualize the performance of a model and identify areas for improvement.
# Computational Analysis of PDE-Based Shape Analysis Models by Exploring the Damped Wave Equation
## 5 Introductory Synthetic Tests
### Experiments
#### 5.2.1 Details for the Damped Wave Equation
The blue line belongs to the CN scheme, and the green one belongs to the SOI\({}_{0}\) method. For obtaining faster results, we increased the time increment from \(\tau=0.1\) to \(\tau=0.5\). Figure 5: For all plots, red circles indicate the CN method, and the blue triangle denotes the \(\text{SO}l_{0}\) scheme. The black solid line indicates the analytical damped feature descriptor, and the red line stands for the difference between the two analytical damped feature descriptors obtained from the second-order methods. (**Top left**) The damping parameter for the analytical damped feature descriptor was set to \(k=\tau/2\). (**Top right**) Damping parameter was set to \(k=\tau/8\). (**Bottom left**) We plotted the difference between the analytical damped feature descriptors obtained with the CN and \(\text{SO}l_{0}\) method. The difference will not exceed \(\pm 1\cdot 10^{-2}\). Therefore, only CN is used as a representative of the second-order methods. (**Bottom right**) We compared the damped feature descriptor (\(k=\tau\)) obtained using the CN method with the feature descriptor using IE. All (damped) feature descriptors are extracted from a solution \(u(x,t)\) at the point \(x=0\). For the numerical calculations, we set the time increment \(\tau=0.1\) and the lattice parameter to \(h=0.05\). Figure 6: (**Top row**) Cosine initial condition. (**Bottom row**) We plotted the solution from our academic setting at different iterations. The solution was generated using the \(\text{SO}l_{0}\) (green) scheme and the CN (blue) method. We chose iterations close to 100, 250 and 500 at which the \(\text{SO}l_{0}\) solution has a maximum amplitude. We notice a phase drift and small damping as well. For faster results, we increased the time increment to \(\tau=0.5\). While both solutions \(u(x,t)\) are still quite close together at 100 iterations, there is a phase difference that becomes noticeable after 250 iterations and can no longer be denied after 500 iterations. Since we see the \(\text{SOI}_{0}\) solutions \(u(x,t)\) at their turning point, we can notice that the second-order features not only a phase error but also inherent damping as well. We only realise it apparently later in time evolution in this simple setting. Let us note, however, that the use of non-uniform grids as appearing in higher dimension over a shapes' surface may possibly enhance this phase-shift effect.
# Parallel Seasonal Patterns of Photosynthesis, Fluorescence, and Reflectance Indices in Boreal Trees
## 4 Discussion
### Complementarity Hypothesis  
Overall, our comparison of the NDVI and PRI to photosynthetic phenology reveal the complementary nature of the NDVI and PRI when sampling deciduous and evergreen vegetation, consistent with the complementarity hypothesis [31; 34]. By contrast, the strong relationships between the CCI and photosynthetic activity in both evergreen and deciduous species indicated that the CCI is sensitive to both seasonally changing pigment pools in evergreens, and canopy structural changes linked to photosynthetic activity in deciduous species, which has not previously been reported. If the CCI is sensitive to both canopy structural changes influencing absorbed light (A\({}_{\text{PAR}}\)) and photosynthetic \(\varepsilon\), then the CCI can be used as a direct indicator of photosynthetic activity in multiple vegetation types, as proposed in Figure 1. SIF may also have a similar capability to the CCI in providing a direct indicator of photosynthetic activity [31], which has been suggested in large-scale studies linking fluorescence to GPP in multiple biomes [29], as it is known to be sensitive to photosynthetic downregulation and absorbed light [10].  
The CCI showed strong parallels with F\({}_{\text{S}}\) in evergreen species, and moderate parallels in deciduous species. The relatively poor relationships when comparing the CCI and F\({}_{\text{S}}\) in deciduous species could be a consequence of comparing leaf-level F\({}_{\text{S}}\) measurements to canopy-level reflectance indices, where the fewer F\({}_{\text{S}}\) samples for deciduous species resulted from the lack of foliage year-round for sampling, and limited the sample size and sampling period for F\({}_{\text{S}}\) to the growing season. Relative to the NDVI and PRI, stronger and more consistent correlations between the CCI and F\({}_{\text{S}}\) for both evergreen and deciduous species suggested that these metrics might yield similar information about photosynthetic phenology, and further work at large scales and across multiple vegetation types would be required to confirm this.
## 3 Preliminaries
### Reconfigurable Intelligent Surfaces  
In communication systems equipped with MIMO antennas, the directions of beamforming depend on the constructive interference of all RF signals from radiators [59]. Figure 1 illustrates that the signal directions of two scenarios are different due to different time delays of two radiators. Such time delays are manifested as phase shifts in the frequency domain. Hence, the two radiators together in Figure 1a,b can be considered as a phase shifter.  
RISs are capable of reflecting the incident signals in the desired directions, as an RIS contains a 2D array of such phase shifters whose phases are individually reconfigurable and controlled by assigned impedance [5]. Codewords are CV vectors characterizing phase shifts of all RIS elements. Given an RIS with \(M\) elements, its codeword \(\psi\in\mathbb{C}^{M\times 1}\), where for the element \(m\) with a phase shift \(\phi\), \(\psi_{m}=e^{\lvert\phi\rvert}\)[60]. Codewords determine  
Figure 1: Different beamforming directions for two radiators with different time delays. In (**a**), two radiators emit the same signal at the same time. In (**b**), two radiators also emit the same signal, but the left one starts from \(t_{3}\), inducing a different constructive interference compared with (**a**), thereby a left direction signal.  
the RIS behaviors, which generally include two categories: anomalous reflection and focusing [61; 62], as shown in Figure 2. Concretely, anomalous reflection means that the RIS reflects the impinging signals towards arbitrary directions in parallel, thereby also including the common specular reflection (the angle of incidence equals the angle of reflection). Focusing would converge the reflected signals to one point, which is very effective for a single DOI, but at the same time means low versatility. Hence, in this paper, we only consider the RIS as a pure anomalous reflector, whose function is merely the manipulation of phase shifts.  
In this context, assigning different codewords to the RIS can help obtain different reflection angles for different devices/functions, as shown in Figure 3. Hence, when the codewords change, shifts in RSSI values occur even for the same locations, which consequently downgrade the estimation accuracy of conventional RSSI fingerprint-based localization solutions in RIS-enhanced wireless communication systems.  
Limited by material and production costs, the number of codewords supported by the RIS is usually restricted [63]. The number of available RIS codewords \(C\) depends on the RIS's resolution [64]. For instance, if an RIS supports a phase shift range of \(180^{\circ}\) with a \(30^{\circ}\) resolution, then \(C=(180/30)^{2}=36\), because the RIS can manipulate reflection angles in two planes, as shown in Figure 4. An alternative way to denote the resolution is the number of bits; i.e., a resolution of \(b\) bits means \(C=2^{b}\)[65]. Most current RIS prototypes support only 1 bit (\(C=2\)) or 2 bits (\(C=4\)) [66; 67], and a few achieve up to 6 bits (\(C=64\)) [68].
# Elastoplastic Analysis of Frame Structures Using Radial Point Interpolation Meshless Methods
## 3 Elastoplastic Model
### Elastoplastic Constitutive Matrix  
The way to obtain the elastoplastic constitutive matrix, \(\mathbf{c}^{p}\), starts by differentiating Equation (9):  
\[dF=\left(\frac{\partial F}{\partial\sigma}\right)\!d\mathbf{\sigma}+\frac{\partial F }{\partial\kappa}d\kappa=0 \tag{13}\] \begin{table}
\begin{tabular}{c l} \hline \hline \(d\lambda<0\) & Elastic unloading occurs (elastic behavior) and the stress point returns inside the yield surface. [PERSON]’s law is still valid. \\ \hline \(d\lambda=0\) & Stress point is on the yield surface. Neutral loading (plastic behavior for an elastic–perfectly plastic material). If the material has hardening, this situation corresponds to the beginning of the plasticity. \\ \hline \(d\lambda>0\) & Plastic loading. The stress point is on a constantly expanding yield surface (plastic behavior for a strain-hardening material). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Possible situations resulting from the plastic multiplier and stress point. Figure 4: Elastoplastic material behavior. (**a**) Elastic-perfectly plastic model. (**b**) Bilinear elastoplastic model. (**c**) Non-linear elastoplastic model. Equation (13) can be written as function of \(d\lambda\):  
\[\mathbf{a}^{T}d\boldsymbol{\sigma}-Ad\lambda=0 \tag{14}\]  
where the vector \(\mathbf{a}\) is the flow vector, \(\frac{\partial f}{\partial\boldsymbol{\sigma}}\), and \(A\):  
\[A=-\frac{1}{d\lambda}\frac{\partial F}{\partial\boldsymbol{\kappa}}d\kappa \tag{15}\]  
Using the Prandtl-Reuss flow rule and Hooke's law, the total strain increment, \(d\boldsymbol{\epsilon}\), is obtained as:  
\[d\boldsymbol{\epsilon}=d\boldsymbol{\epsilon}^{e}+d\boldsymbol{\epsilon}^{p}= \mathbf{c}^{-1}d\boldsymbol{\sigma}+d\lambda\frac{\partial F}{\partial \boldsymbol{\sigma}} \tag{16}\]  
where \(\mathbf{c}\) is the elastic constitutive matrix of material properties. Using (14) and (16), one obtains:  
\[d\lambda=\frac{1}{\left[A+\mathbf{a}^{T}\mathbf{c}\cdot\mathbf{a}\right]} \mathbf{a}^{T}d\mathbf{D}d\boldsymbol{\epsilon}\quad with\quad d\mathbf{D}^{ T}=\mathbf{a}^{T}\mathbf{D} \tag{17}\]  
Substituting Equation (17) in Equation (14) allows one to obtain:  
\[d\boldsymbol{\sigma}=\left(\mathbf{c}-\frac{\mathbf{c}\cdot\mathbf{a}\cdot \mathbf{a}^{T}\mathbf{c}}{\mathbf{a}^{T}\mathbf{c}\cdot\mathbf{a}+A}\right)d \boldsymbol{\epsilon}=\mathbf{c}^{p}d\boldsymbol{\epsilon} \tag{18}\]  
where \(\mathbf{c}^{p}\) is the elastoplastic matrix. For an isotropic hardening rule, the yield stress is a function of the plastic work,  
\[\sigma_{Y}(\kappa=W_{p}) \tag{19}\]  
where \(W_{p}\) and, consequently, \(d\kappa=dW_{p}\) are:  
\[ \begin{split}\kappa=W_{p}=\int\sigma_{Y}d\boldsymbol{\epsilon}_{p }=\int\boldsymbol{\sigma}d\boldsymbol{\epsilon}_{p}=\int d\lambda\boldsymbol{ \sigma}^{T}\mathbf{a}\\ dx=dW_{p}=\sigma_{Y}d\boldsymbol{\epsilon}_{p}=\boldsymbol{ \sigma}^{T}d\boldsymbol{\epsilon}_{p}=d\lambda\boldsymbol{\sigma}^{T}\mathbf{ a}\end{split}
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 2 Background on SRv6 Network Programming
### SRv6 Network Programming
#### 2.2.1 H.Encaps.Red  
This behavior is applied by the source node in the case of the SRH-encapsulated version. When the source node receives a packet whose destination address matches with a locally instantiated BSID associated with the H.Encaps.Red behavior, it does the following. Firstly, it encapsulates the packet into an outer IPv6 header. Then, if the segment list to be entered is more than one element long, it embeds the SRH into the IPv6 outer header. The first segment to be reached is not included in the SRH; instead, it is just set as the destination address. Based on that, the Segment Left field of the SRH will be the index of the last element in the list plus one, because the active segment is not included in the SRH. In addition, the header length is 128 bits shorter. If, instead, the segment list to be entered contains just one element, the SRH is not needed: the element will be placed in the destination address of the outer IPv6 header. Finally, the outer IPv6 header fields are properly set, and the properly set, and the packet is forwarded.
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 5 Results
### Scenario I: LTE at 3.5 GHz No Carrier Aggregation
As seen in the UABS altitude evaluation, the maximum allocated users were 45 (Figure 4a). Moreover, results show how the network usage was reasonably stable and 80 m flight-altitude outcast other altitudes, showing that our tool enabled a stable backhaul connection.
## 2 Experimental
### User Needs Hierarchy Analysis Based on the Kano Model
#### 2.3.3. Kano Model Demand Weight Calculation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \begin{tabular}{c} **Feature** \\ \end{tabular} & **A (\%)** & **O (\%)** & **M (\%)** & **I (\%)** & **R (\%)** & \begin{tabular}{c} **Better** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Worse** \\ **Ratio** \\ \end{tabular} & \begin{tabular}{c} **Attribute** \\ \end{tabular} \\ \hline \begin{tabular}{c} Display the design and layout of the \\ RV interior \\ \end{tabular} & 13.91 & 9.2 & 35.78 & 9.26 & 4.94 & 40.14\% & \(-\)59.86\% & M \\ \hline Show all details inside the RV & 42.79 & 24.06 & 13.81 & 8.62 & 2.17 & 40.37\% & \(-\)61.70\% & A \\ \hline \begin{tabular}{c} Display the internal structure of the RV \\ \end{tabular} & 13.18 & 4.92 & 43.29 & 16.17 & 4.87 & 41.97\% & \(-\)61.70\% & M \\ \hline \begin{tabular}{c} Provide detailed information on interior \\ materials and accessories \\ \end{tabular} & 13.91 & 7.88 & 34.72 & 17.02 & 4.94 & 38.76\% & \(-\)59.86\% & M \\ \hline \begin{tabular}{c} Display internal functions of the RV \\ \end{tabular} & 22.48 & 4.92 & 34.72 & 15.6 & 4.63 & 40.37\% & \(-\)59.86\% & M \\ \hline \begin{tabular}{c} Freedom to switch perspectives inside the RV \\ \end{tabular} & 22.01 & 21.26 & 43.59 & 5.46 & 4.94 & 39.91\% & \(-\)60.32\% & M \\ \hline \
\begin{tabular}{c} Click interaction with items inside the RV \\ \end{tabular} & 35.53 & 21.67 & 43.16 & 4.32 & 2.17 & 40.83\% & \(-\)60.78\% & M \\ \hline
\begin{tabular}{c} Custom RV design \\ \end{tabular} & 18.5 & 21.1 & 43.98 & 4.17 & 1.16 & 38.76\% & \(-\)55.50\% & M \\ \hline \hline \end{tabular}
\end{table} 
Table 5: KANO model questionnaire data collation. Based on the EI values, the hierarchy of service needs is as follows:  
1. Must-Have Needs. Increasing or decreasing RV facilities > Displaying the internal structure of the RV > Interactive clicking on items inside the RV > Free switching of views inside the RV > Displaying the internal functions of the RV > Real-time changes in RV internal layout under different configurations > Providing RV usage tips > Show the design and layout of the interior of the RV > Selecting RV interior decoration styles > Providing detailed information on RV interior materials and accessories > Adjusting internal layout > Custom design of the RV
2. Expected Needs. Providing information on RV functions and features
3. Attractive Needs.