esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Matrix Converters in the Connection of Renewable Energies to the Grid
### Matrix Converter Applications for Integration of Photovoltaic (PV) Systems
#### 4.1.1 Power Quality Compensation in the Connection of PV Arrays to the Smart Grid (SG)  
In the connection of the PV arrays to the SG the converter should guarantee that the current harmonic limitations set by standard IEC 61000-3-2 are met [40; 41], while the distribution voltage quality must comply with standards IEC 61000-3-3 and EN 50160, in terms of voltage root-mean-square (rms) value and harmonic content [42; 43]. In particular, the voltage-related PQ issues such as voltage instability with voltage sags, swells or harmonics or permanent undervoltage or overvoltage are important for sensitive loads that often require a voltage supply with near constant rms value to avoid degradation and decrease in efficiency [40; 42; 44].  
Novel converter topologies and control methods have been proposed in the literature to act as active power filters (APFs) with reduced impact on the SG. In these works, the shunt connection of the PV converter to the grid allows compensating the low-order harmonics from the non-linear loads. Voltage-related PQ issues can be compensated to a certain extent using this shunt connection, with reactive power compensation and active power limitation [45; 46; 47; 48; 49; 50]. This helps avoiding the disconnection from the SG in case of severe issues, but the compensation is limited by the network impedance [45; 51; 52; 53]. In order to allow for a proper voltage compensation at the PCC, a series connection to the SG can be added, applying a voltage in series with the grid to control the voltage that supplies the sensitive loads. These APFs are either of the CSI [54] or VSI [55] type.
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 6 Conclusions  
This study investigates the use of anomaly detection techniques on open video data from the _Comunitat Valenciana_. Despite limitations in data availability due to external factors, two time series were created and expanded using probability distributions. Collective anomalies were identified by applying statistical thresholds to the data, while point anomalies were detected using the SESD algorithm. Validation of these results was conducted by comparing the most prominent anomalies across different time intervals. Findings demonstrate that anomaly detection techniques can effectively identify unusual patterns in temporal data, highlighting their value as tools for analyzing open video data in complex environments. The methodology prioritizes individual privacy by implementing measures that ensure anonymity, avoiding both trajectory tracking and person identification. Future work envisions an edge-computing approach, where privacy will be further reinforced by limiting video access solely to the camera itself, ensuring researchers handle only processed data rather than raw footage.  
Methodology, A.J.S.-L., J.V.-F. and A.D.-R.; Investigation, A.D.-R. and F.M.; Data curation, A.D.-R. and A.J.S.-L.; Writing--original draft, A.D.-R. and R.M.-B.; Writing--review and editing, F.M. and J.V.-F.; Supervision, F.M., R.M.-B., A.J.S.-L. and J.V.-F.; Project administration, J.V.-F. and A.J.S.-L. All authors have read and agreed to the published version of the manuscript  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy restrictions.  
The authors declare no conflict of interest.
# A Deep Multi-Frame Super-Resolution Network for Dynamic Scenes
## 3 Methods
### Temporal Dual-Weighted Module  
To eliminate the errors caused by optical flow and warping operation, we propose a DW module. Because we apply a weighted sum operation in the final fusion, as shown in Equation (1), DW gives the weight \(w_{i}\) of each frame according to its content and its difference to the target frame, as shown in Figure 4. It is worth mentioning that although one frame only computes one weight, this weight is applied to multiply each feature from this frame for fusion. The \(w_{i}\) is the multiplication of different weight \(w_{i}^{d}\) and the content weight \(w_{i}^{e}\) as shown in Equation (2):  
\[w_{i}=w_{i}^{d}\times w_{i}^{c} \tag{2}\]  
The warped features or images often suffer from distortion, which is attributed to the inaccuracy of optical flow and the warping itself. Therefore, we compute a weight \(w_{i}^{d}\) for each warped features, which measures the difference between the warped features and the target features. By doing so, the unreliable areas are not taken into consideration in the later fusion process. Difference weight \(w_{i}^{d}\) is generated by a small common convolutional network with five layers, as shown in Equation (3):  
\[w_{i}^{d}=\sigma(Net_{d}(F_{0}-F_{i}^{a})) \tag{3}\]  
where \(Net_{d}(*)\) denotes the inference process of the small network. It takes the difference between \(F_{0}\) and \(F_{i}^{a}\) as the input, and the output is followed by a sigmoid function \(\sigma(*)\), so that the range of \(w_{i}^{d}\) is limited in the range of 0-1.  
Moreover, we introduce content weight \(w_{i}^{e}\) to adjust the weights according to the content. The computation of content weight \(w_{i}^{e}\) can be formulated in Equation (4):  
\[w_{i}^{c}=\sigma(Net_{c}(cat(F_{0},F_{i}^{a}))) \tag{4}\]  
where \(Net_{c}(*)\) is the inference process of another small network with a similar architecture to \(Net_{d}(*)\). Differently, this small network processes a concatenation operation \(cat(*)\) in the beginning.  
The two subnets for computing the two weights have the same structure, as shown in Figure 4. They are simply a cascade of two convolutional layers and one resblock placed in the middle. Both subnets end with a sigmoid function. Differently, the channel number for content weight is twice that of distance weight.  
Figure 4: The architecture of the dual-weighting (DW) module.
# A Model-Based Investigation of the Performance Robustness of the Deflector Jet Servo Valve
## 6 Further Analysis and Discussion
### Tendency Analysis of DJV Robustness against Errors
#### 6.4.2 Robustness of Average Pressure Gain \(K_{p}\)  
According to Equation (31), the robustness tendency of the average pressure gain can be described by  
\[\frac{dK_{p}}{d\Delta l_{s}}=\frac{12.8\times 10^{3}\exp(-2558 SH)\left\langle k _{p}\right\rangle}{s_{r}\phi_{5}\sqrt{\pi}}\exp\left(-t^{2}\right)dt-\int_{0 }^{\frac{\eta(l_{ms}+2s_{r})}{2p}}\exp(-t^{2})dt \tag{37}\]  
Figure 27: Balance pressure robustness against width error of shunt wedge.  
Figure 26: Balance pressure robustness against the deflector installation error.  
As depicted in Figure 28, supposing that the deflector approaches the shunt wedge by 0.1 mm from the standard installation location, the pressure gain susceptibility will increase over 10 times. However, the opposite offset of the deflector has relatively little effect on the pressure gain robustness. Therefore, to stabilize the pressure gain of products or make them robust, the error of the jet distance should also be kept positive.  
Consider the pressure gain robustness against the shunt wedge width. The derivative of Equation (34) can be expressed by  
\[\frac{\frac{\Delta K_{p}}{\Delta M_{m}}}{\Delta M_{m}}=\frac{p_{m}c_{f}^{2}}{ \frac{\Delta K_{p}}{\Delta Y_{p}}}\left\{-\exp\left[-\frac{\eta^{2}\left(l_{m +}+\Delta_{m}-2x_{f}\right)^{2}}{\frac{\Delta K_{p}}{\Delta Y_{p}}}\right]\ +\exp\left[-\frac{\eta^{2}\left(l_{m +}+\Delta_{m}+2x_{f}\right)^{2}}{4\frac{\Delta K_{p}}{\Delta Y_{p}}}\right]\right\} \tag{38}\]  
which is illustrated in Figure 29 for different deflector displacements.  
Similarly, the shunt wedge error has a much more gradual effect on the pressure gain robustness than the deflector installation error. However, from Figures 28 and 29, it can be learned that when the deflector is not too close to the shunt wedge, the shunt wedge error dominates the pressure gain variation.
# TCNformer Model for Photovoltaic Power Prediction
## 3 Methodology  
### Transformer Based TCNformer Solution  
For the time series features of photovoltaic power data, this paper proposed a TCNformer prediction model. The structure of the model is shown in Figure 3. Based on the traditional Transformer architecture, the TCNformer model mainly includes four modules: a variable selection (VS) module, an long- and short-time series feature extraction (LSTFE) module, an Encoder, and a Decoder.  
The overall TCNformer network design follows the traditional Transformer structure, in which the Encoder module and the Decoder module are designed with a multilayer structure.
## 1 Introduction
### Contributions  
In this paper, we investigate the joint optimization of FeICIC parameters and PBS density in HetNets for network EE improvement. Initially, the system model for two-tier HetNets by using stochastic geometry is described. Then, an analytical expression of the network EE as a function of pico CRE bias, power reduction factor and PBS density is derived. At last, heuristic algorithms are proposed to obtain the optimal values of pico CRE bias, power reduction factor and PBS density to maximize the network EE. The main contributions of this paper are summarized as follows: (1) the closed-form expression of network EE as a function of pico CRE bias, power reduction factor and PBS density is deduced by stochastic geometry theory. (2) The equivalent relationship between pico CRE bias and the power reduction factor is obtained by an approximation calculation. Based on the equivalent relationship, an efficient optimization algorithm is designed to get the near-optimal values of pico CRE bias and power reduction factor at a given PBS density. (3) To achieve the global optimization of network EE, a low computational complexity heuristic algorithm is proposed to jointly optimize pico CRE bias, power reduction factor, and PBS density.
## 3 Model Formulation
### Declarative Model Description  
The considered problem could be described using the declarative modeling paradigm. **Parameters**:  
\(Z:\) set of tasks, \(Z=\{Z_{1},\ldots,Z_{i},\ldots,Z_{Q}\}\);  
\(EP:\) set of planned projects;  
Figure 4: Variants for the implementation of the project portfolio without task rotation (**a**), and with task rotation (**b**), with a new project commissioned \(E_{3}\). \(EX:\) set of additional orders (disruptions); \(E:\) portfolio of projects, \(E=EP\cup EX=\big{\{}E_{1},\ldots,E_{j},\ldots,E_{f}\big{\}}\), \(|E|=J\); \(E_{j}\): \(j\)-th project of portfolio \(E\), represented by the network of activities: \(E_{j}=\big{(}VE_{j},AE_{j}\big{)}\), where \(VE_{j}\subseteq Z\) is a set of tasks of the project \(E_{j}\) and \(AE_{j}\) is a set of arcs defining order relations \(AE_{j}\subseteq VE_{j}\times VE_{j}\); \(HE_{j}\): latest project completion date \(E_{j}\); \(SE_{j}\): earliest project start date \(E_{j}\); P : team of programmers, \(\text{P}=\big{\{}P_{1},\ldots,P_{k},\ldots,P_{K}\big{\}}\); \(G^{0}\): the initial structure of competences; \(SG^{0}\): the initial degree of the competence structure; \(\varphi\): function determining the level of competence of programmers \(\varphi(G^{n},X^{n})\); \(\tau_{j,i}\): function determining the time of task \(Z_{i}\) from project \(E_{j}\) depending on the competence of the employee assigned to it, \(\tau_{j,i}(G^{n},X^{n})\); \(SG^{*}\): the expected degree of competence structure. **Decision variables:**:  
\(Y_{j}\): schedule for \(j\)-th project execution, \(Y_{j}=\big{(}y_{j,1},\ldots,y_{j,i},\ldots,y_{j,Q}\big{)}\), where \(y_{j,i}\)--the start of the task \(Z_{i}\) from the \(E_{j}\) project; \(X^{n}\): assignment of team \(P\) employees to tasks \(Z\) at moment \(n\): \(X^{n}=\Big{[}x^{n}_{k,i,j}\Big{]}_{k=1\ldots K;i=1\ldots Q;j=1\ldots J}\); \(G^{n}\): competence structure at moment \(n\): \(G^{n}=\Big{[}g^{n}_{k,j}\Big{]}_{k=1\ldots K;i=1\ldots Q}\).
# Towards a Provably Secure Authentication Protocol for Fog-Driven IoT-Based Systems
## 2 Complexity Assumptions
### User Registration Phase  
Initially, user \(\mathcal{U}_{k}\) imitates the process to get himself registered with the cloud service provider \(\mathcal{CSP}\). For this purpose, \(\mathcal{U}_{k}\) performs the subsequent steps:  
1. \(\mathcal{U}_{k}\) randomly chooses \(r_{k}\in Z_{p}^{*}\), identity \(ID_{k}\) and computes \(RID_{k}=h(ID_{k}\oplus r_{k})\). \(\mathcal{U}_{k}\) sends \(ID_{k},RID_{k}\) to \(\mathcal{CSP}\) via a secure channel.
2. After receiving \(\mathcal{U}_{k}^{\prime}s\) request, \(\mathcal{CSP}\) computes the following values: \(X_{k}=h(ID_{k}\|x)\), \(Y_{k}=RID_{k}\oplus X_{k}\). Further, \(\mathcal{CSP}\) selects \(TID_{k}\) for the respective user \(\mathcal{U}_{k}\) and calculates \(Z_{k}=h(X_{k}\|ID_{k}\|TID_{k}\|RD_{k})\) and stores \(ID_{k}\) in its database corresponding to \(TID_{k}\) and using \(x\). \(\mathcal{CSP}\) then transmits \(\{Y_{k},TID_{k},Z_{k}\}\) to \(\mathcal{U}_{k}\) using a private channel.
3. After receiving the message from \(\mathcal{CSP}\), \(\mathcal{U}_{k}\) computes \(\overline{Y_{k}}=(Y_{k}\|r_{k})\oplus ID_{k}\) and stores \(\{\overline{Y_{k}},Z_{k},TID_{k}\}\) in its tamper-proof device.
## 6 Research Areas
### Data Transmission  
In this category, researchers have developed data compression and transmission techniques for faster and more robust data transfer from wireless robots over the Internet or local area network [253; 254; 255]. [PERSON] and [PERSON] [256] combined the Internet of Things (IoT) with robotics for wirelessly collecting data from multiple sensors inside the building for quality inspection. Wireless communication with robots usually occurs via radio frequency (RF) signals using protocols such as WiFi and Bluetooth [256; 257]. Other RF-based communication protocols have also been used, such as LoRa and ZigBee [256; 257]. However, in urban settings, where there is a large number of sources of RF signals, signal interference and/or loss of signal can severely affect the safe performance of the robot. Therefore, [PERSON] et al. [257] studied the effect of RF noise in urban settings in the operation of UAVs and recommended using redundant channels and faster frequency switching for seamless operations.  
A robotic inspection may generate a vast amount of data in the form of images or point clouds [255]. Depending on the fidelity of the data required, either lossless or lossy data compression techniques may be used. Lossless compression produces exact reconstruction, whereas lossy conversion has a higher compression ratio but loses some details during reconstruction [255].
## 4 Results
### Numerical Results
In order to validate the MFA composite bridge bearing model for practical use, Figure 13 shows the comparative results for vertical stiffness of the model with various Young's moduli, presented in Table 3, and a common bridge bearing under compression at a vertical displacement of 4 mm, comparing numerical predictions and experimental measurements, respectively. It is clear that there is a small difference in vertical stiffness between the numerical and observed experimental results on the curve of the figure, with the proposed material having a compressive stiffness close to that typical of the common bridge bearing used in the model (less than 0.78%). This means the proposed model acts as a typical bridge bearing which can possibly resist the weight of a superstructure of a common bridge. However, the model should be further investigated using static and dynamic shear analysis in order to ensure better performance in reality. Note that the proposed material is considered as a linear material and the proposed model would be fabricated using a 3D printed composite with a high stiffness to density ratio (Cyanate ester/HM carbon fibre) composed of polymer (30%) and carbon fibre (60%), as obtained from the CES Edupack 2019 (Granta Design, Cambridge, UK) [70] software and as given in Figure 14. The experimental measurements are based on the journal provided in [71]. Figure 11: Auxetic deformation behaviour of the meta-functional auxetic composite bridge bearing model in vertical and horizontal directions under compression at vertical displacement of 4 mm. Figure 12: The numerical auxetic deformation modes of this novel structure at different strain regions in the vertical and horizontal directions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Simulation** & **Young’s Modulus, E (GPa)** \\ \hline Model 1 & 200 \\ Model 2 & 300 \\ Proposed model & 350 \\ Model 3 & 400 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The various Young’s moduli used in the MFA composite bridge bearing model.
## 3 Proposed Method
### Overhead of Data Transmission between CPUI and GPU  
To perform encryption on a large amount of data, it is necessary to transfer the plaintext from the CPU to the GPU for encryption, which is very time-consuming. The problem is even more obvious in key exhaustive searches, wherein the guessed key values are transferred to the GPU and create a huge transmission delay. Tezcan [10] eliminates the process of transferring plaintext to the GPU by using the counter mode, in which the counter values are generated in the kernel as a key value, and increase by one in each thread. This idea was also adopted by us and applied to the bit-slicing technique. However, converting the counter value in normal representation to the bit-slicing representation introduces overhead that cannot be ignored. Hence, we propose a technique to create a counter that is already in a bit-slicing representation, thus avoiding the expensive conversion process. This is shown in the CUDA C/C++ code as below:  
uint32_t tid = blockIdx.x * blockDim.x + threadIdx.x;  uint32_t subkeys[80] = {0,;};  subkeys[0] = 0x5555555;  subkeys[1] = 0x333333333;  subkeys[2] = 0x00 FF00F;  subkeys[3] = 0x00 FF00 FF;  subkeys[4] = 0x000 FFFF;  for (uint32_t i = 0; i < 31; i++) {  subkeys[i+5] = ((tid & (1<i)) >> i) * 0 xffFFFFFFFF;  } ```  
The proposed method creates a counter in a packed state instead of packing it into a bit-slicing expression after generating a counter value. 32-bit blocks are processed simultaneously. Therefore, the same value is used for [0]\(\sim\)[4] of subkeys, and the counter is incremented by 32. In the next step, the thread uses the counter value incremented by 32 times its own thread number as the key value.
## 3 Methodology
### BLCC Creation
#### 3.2.1 Top Level: Review-Based \(p\)-Chart for Importance Monitoring  
For each attribute, \(i\), the \(p\)-chart plots the subgroup fraction of attribute appearance, \(p_{it}\), that is, the proportion of reviews mentioning the attribute regardless of the sentiment  
Figure 3: Procedure of sentiment analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Review \(j\)**} & \multirow{2}{*}{**Time \(t_{j}\)**} & \multicolumn{2}{c}{**Attribute 1**} & \multicolumn{2}{c}{**Attribute 2**} & \multicolumn{2}{c}{\(\ldots\)} & \multicolumn{2}{c}{**Attribute \(i\)**} & \multicolumn{2}{c}{\(\ldots\)} & \multicolumn{2}{c}{**Attribute \(I\)**} \\ \cline{3-10}  & & \(\mathbf{x_{1,j}}\) & \(\mathbf{y_{1,j}}\) & \(\mathbf{x_{2,j}}\) & \(\mathbf{y_{2,j}}\) & \(\mathbf{x_{i,j}}\) & \(\mathbf{y_{i,j}}\) & \(\mathbf{x_{i,j}}\) & \(\mathbf{y_{i,j}}\) \\ \hline
1 & 1 & 1 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
2 & 1 & 1 & 0 & 1 &type (positive, negative, or neutral). Here, a subgroup refers to a set of reviews posted over a unit period of time (e.g., a month). More specifically, \(p_{it}\) (corresponding to a point on the chart) is computed using Equation (3), where \(R_{t}\) denotes a set of reviews posted in a given time period, \(t\) (i.e., \(R_{t}=\left\{j|t_{j}=t\right\}\)), and \(\mid R_{t}\mid\) denotes the cardinality of set \(R_{t}\), or, in other words, the total number of reviews posted in the time period. The CL, UCL, and LCL of the \(p\)-chart are calculated using Equations (4)-(6), respectively.  
\[p_{i,t}=\frac{\sum_{j\in R_{t}}x_{i,j}}{|R_{t}|}\quad\text{for all $t$} \tag{3}\]  
\[\text{CL}=\overline{p}_{i,t}=\frac{\sum_{t=1}^{T}\sum_{j\in R_{t}}x_{i,j}}{ \sum_{t=1}^{T}|R_{t}|}=\frac{\sum_{j=1}^{J}x_{i,j}}{J} \tag{4}\]  
\[\text{UCL}_{i,t}=\overline{p}_{i,t}+3\sqrt{\frac{\overline{p}_{i,t}\left(1- \overline{p}_{i,t}\right)}{|R_{t}|}} \tag{5}\]  
\[\text{LCL}_{i,t}=\overline{p}_{i,t}-3\sqrt{\frac{\overline{p}_{i,t}\left(1- \overline{p}_{i,t}\right)}{|R_{t}|}} \tag{6}\]  
Notably, the subgroup size \(\mid R_{t}\mid\) can vary from month to month but should be large enough to establish accurate control limits. According to [PERSON] [44], the total number of reviews \(\mid R_{t}\mid\) should be greater than or equal to \(3/p_{it}\) and \(3/(1-p_{it})\). If \(\mid R_{t}\mid\) does not satisfy the minimum subgroup size, the corresponding subgroup for time \(t\) is excluded in the BLCC.  
If the level of attribute appearance (or attribute importance) is stable (in control), there should only be a common-cause variation, and the points of the \(p\)-chart should randomly vary around the CL. However, if a point is above the UCL or below the LCL, or if a non-random pattern or trend exists in the chart, a possibility of special-cause variation exists. In this regard, the proposed method applies the rules in Table 4 to identify whether special cause variations exist in the level of importance of an attribute or not [49; 50].
## 2 Results and Discussion
### Volatile Constituents in the Peel and Juice
The same three volatiles were observed at the highest amounts also in the juice with the following percentages: limonene at 35.7%, followed by \(\beta\)-phellandrene at 23.5% and \(\gamma\)-terpinene at 12.6% (Table 1). In the Faustrime peel, VOCs detected at a concentration >1% included p-cymene (6.3%), \(\beta\)-myrcene (4.5%), \(\alpha\)-phellandrene (4.4%), \(\beta\)-bisabolene (4.0%), \(\beta\)-citronellol (2.3%), bicyclogermacrene (2.1%), \(\alpha\)-pinene (2.0%), \(\alpha\)-caryophilene (2.0%), linalool (1.7%), sabinene (1.5%), \(\alpha\)-thujene (1.2%), and \(\alpha\)-terpinolene (1.1%) (Table 1). Conversely, in the juice, in addition to \(\alpha\)-pinene, \(\alpha\)-phellandrene, \(\beta\)-myrcene, p-cymene, \(\beta\)-citronellol, and \(\alpha\)-caryophilene, which were also present in the peel, four other constituents were found with a content >1%, including \(\alpha\)-bergamotene, terpinen-4-ol, and \(\alpha\)-terpinene (Table 1). Moreover, minor compounds, including 2-hexenal (0.5%), aromadendrene (0.4%), citronellal (0.3%), trans-carveol (0.2%), hexen-1-ol propionate (0.1%), cis-sabinene hydrate (0.1%), cis-limonene oxide (0.1%), and cis-carveol (0.1%) were identified only in the peel, while ledene (0.1%) was found only in the juice (Table 1). Finally, cis-limonene oxide and nerolidol were exclusively found in the peel of the Faustrime CV (Table 1). These molecules, which seem to be specific to one CV, could be considered volatile markers of this CV. It is worth mentioning that, as there is no authentic chemical standard for bicyclogermacrene, this compound has been putatively identified, based on its high percentage of similarity (>90%) and according to previous reports about the characterization of the peels of other finger lime CVs [1; 6].