esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Seasonal Energy Yields  
Investors in solar energy installations are mostly interested in knowing the minimum and maximum possible energy received by their energy systems. This is interpreted as the solar potential during the winter (December-January-February, hereafter DJF) and summer (June-July-August, hereafter JJA) months. Therefore, this section is devoted to analyzing the energy yields during these two seasons at all 33 locations in Greece. The analysis was concentrated on the energy received on a surface tilted to the south at the selected angles of \(25^{\circ}\) or \(30^{\circ}\).  
Figure 9a,b presents the total solar energy received on a flat solar convector inclined at \(25^{\circ}\) or \(30^{\circ}\) to the south under all-sky conditions during summer and winter, respectively. The energy values are the sums for each season (summer (JJA) and winter (DJF)) in every site. It is interesting to see that the dispersion of the seasonal energy sums among the 33 locations was great. Moreover, there were sites that belonged to a lower \(\mathrm{PV_{cts}}\) zone but showed solar availability corresponding to a higher \(\mathrm{PV_{cts}}\) zone. Nevertheless, in both cases, a decreasing trend was observed from \(\mathrm{PV_{cts}}\) zone A to \(\mathrm{PV_{cts}}\) zone D sites. Because  
Figure 8: Intra-annual variation of the monthly mean \(G_{e,255-305}\) and \(G_{e,q5}\) sums and their monthly differences.  
of this great dispersion, no specific thresholds could be applied to the summer and winter seasons, as was done with the annual energy sums.  
Figure 10a,b shows the seasonal sum values of \(G_{e,255}\) or \(G_{e,305}\) for every \(\mathrm{PV_{ctts}}\) zone averaged over all sites that belonged to the same zone. It is easy to conclude that the hierarchy of higher to lower seasonal energy yields was fulfilled in the summer, but this was not the case in winter. Here, the energy totals in zones B and C were interchanged (i.e., the winter mean \(G_{\mathrm{e}}\) value in zone C was higher than that in zone B). Besides this  
Figure 9: Summer (**a**) and winter (**b**) \(G_{\mathrm{e}}\) values across all sites on inclined flat plates of 25\({}^{\circ}\) S or 30\({}^{\circ}\) S angles. The sites are cited in alphabetical order within the same \(\mathrm{PV_{ctts}}\) zones.  
discrepancy, this winter abnormality between zones B and C seemed to not have an effect on the annual solar availability at the sites belonging to these two zones, as far as the decreasing trend from B to C is concerned. Additionally, the spring and autumn mean \(G_{\rm e}\) values (not shown here) followed a decreasing trend from zone A to zone D.  
Contrary to the above conclusions, [PERSON] and [PERSON] (2012) showed that the optimum summer tilt angle of PV panels to the south is much lower than ours (i.e., 15\({}^{\circ}\)\(\pm\) 2.5\({}^{\circ}\)). The discrepancy focused on the PV (hardware) efficiency considered by the authors, while the present study did not make such an assumption.
# Solar Potential in Saudi Arabia for Southward-Inclined Flat-Plate Surfaces
## 3 Results
### Monthly Energy Sums  
The intra-annual variation of H\({}_{\text{g,opt. }\beta\text{S/pg}}\) in each specific SEZ region is shown in Figure 5. Figure 5a refers to the sites in SEZ-A (optimal \(\beta\) = 20\({}^{\circ}\)), Figure 5b to the sites in SEZ-B (optimal \(\beta\) = 25\({}^{\circ}\)), and Figure 5c to the sites in SEZ-C (optimal \(\beta\) = 30\({}^{\circ}\)). The expressions for the lines that best-fit the means and their coefficient of determination, R\({}^{2}\), are given in Table 3. It is seen that the R\({}^{2}\) statistic obtains high values; this allows a solar energy user or investor in Saudi Arabia to estimate the monthly energy production in any of the 3 SEZs in an accurate way by applying the regression equations. The graphs also contain curves for the mean \(\pm\) 1 standard deviation (\(\sigma\)).  
Figure 4: Distribution of the 82 sites in different colours and symbols: red circles refer to sites with optimum \(\beta\) = 20\({}^{\circ}\) S (SEZ-A), orange circles to sites with optimum \(\beta\) = 25\({}^{\circ}\) S (SEZ-B), and green circles to sites with optimum \(\beta\) = 30\({}^{\circ}\) S (SEZ-C). The numbers in the symbols refer to the sites (column 1, Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SEZ** & \multicolumn{2}{c}{**Regression Equation**} & \(\mathbf{R^{2}}\) \\ \hline A (months) & H\({}_{\text{g,opt. 205/\text{pg}}}=0.0069\;t^{6}-0.2697\;t^{5}+4.0473\;t^{4}-29.198 \;t^{3}+102.1\;t^{2}-146.91\;t+257.22\) & 0.90 \\ A (seasons) & H\({}_{\text{g,opt. 205/\text{pg}}}=11.873\;t^{3}-111.04\;t^{2}+270.02\;t+469.06\) & 1 \\ B (months) & H\({}_{\text{g,opt. 258/\text{pg}}}=0.0074\;t^{6}-0.2807\;t^{5}+4.0844\;t^{4}-28.789 \;t^{3}+99.611\;t^{2}-140.6\;t+238.64\) & 0.95 \\ B (seasons) & H\({}_{\text{g,opt. 258/\text{pg}}}=-3.5278\;t^{3}+14.665\;t^{2}-29.675\;t+676.42\) & 1 \\ C (months) & H\({}_{\text{g,opt. 305/\text{pg}}}=0.076\;t^{6}-0.2856\;t^{5}+4.1495\;t^{4}-29.402 \;t^{3}+102.8\;t^{2}-143.99\;t+214.14\) & 0.98 \\ C (seasons) & H\({}_{\text{g,opt. 305/\text{pg}}}=16.004\;t^{3}-148.64\;t^{2}+367.43\;t+399.25\) & 1 \\ all (months) & H\({}_{\text{g,opt. \beta S/\text{pg}}}=0.0073\;t^{6}-0.2775\;t^{5}+4.0829\;t^{4}-29. 057\;t^{3}+101.12\;t^{2}-143.61\;t+241.55\) & 0.95 \\ all (seasons) & H\({}_{\text{g,opt. \beta S/\text{pg}}}=24.366\;t^{3}-217.36\;t^{2}+524.17\;t+288.48\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Regression equations for the best-fit curves to the monthly/seasonal mean H\({}_{\text{g,0.85/\text{pg}}}\) sums averaged over all respective sites in the period 2005–2016, together with their R\({}^{2}\) values; \(t\) is either month in the range 1–12 or season in the range 1–4 (1 = spring, 2 = summer, 3 = autumn, 4 = winter).  
Figure 5: _Cont._
## Materials and Methods
### Experimental Set-Up  
#### 2.2.1 Thermal and Thermochemical Pretreatment  
Thermal pretreatment was performed on both food waste slurry and excess sewage sludge at three different temperatures: 70, 90, and 100 \({}^{\circ}\)C. Each different pretreatment case was operated at different durations between 30 min to 4 h. A quantity of 12.5 mL of the substrate (food waste slurry or excess sewage sludge) was added to a 100 mL glass bottle and tap water added to have a final volume of 50 mL. A water bath shaker was used to set the temperature at the designated point. For the thermochemical pretreatment of both substrates the following conditions with different ranges were tested: alkali NaOH/Ca(OH)\({}_{2}\), temperature 70/90 \({}^{\circ}\)C, and duration 30 min to 4 h. A total of 12.5 mL substrate was added to a 100 mL glass bottle and the required dose of alkali was added to have pH level 12; then, tap water was added to have the final volume of 50 mL. For the pretreatment, adequate amounts of 4M NaOH solution and Ca(OH)\({}_{2}\) powder were used in respective experiments to reach a pH level of 12. A water bath shaker was used to have the desired temperature. The soluble chemical oxygen demand (sCOD) of ESS and FWS were measured after each pretreatment and compared with the initial sCOD values. The increase in sCOD was reported as percentage of the initial sCOD values. The desirable pretreatment for subsequent acidogenic fermentation was selected based on an increase in sCOD of substrate after pretreatment.
# Impedance-Based Stability Analysis of Grid-Connected Inverters under the Unbalanced Grid Condition
## 1 Introduction
The analysis results provide the theoretical basis for the parameter design of the DSOGI-PLL-based inverter. In this paper, the methodology is illustrated in Figure 1. As shown in Figure 1, we first derive the frequency-coupled impedance model of the inverter based on the multi-harmonic linearization method and symmetric component method. Then, to verify the accuracy of the derived impedance model of the inverter, we establish a time-domain simulation model in the MATLAB/Simulink R2023a platform and carried out a frequency sweeping simulation. In Section 3, we propose a simplified stability analysis method based on the GNC and matrix theory to analyze the stability of higher-order systems. We establish the time-domain simulation model and hardware experimental platform to validate the effectiveness of the proposed analysis method. In Section 4, we analyze the influence of pivotal parameters on the system's stability and discuss the parameter design recommendations for practical engineering. Based on the methodology shown in Figure 1, the low-frequency oscillations in the DSOGI-PLL-based inverter under the unbalanced grid condition are investigated. The main conclusions are drawn in Section 5. Figure 1: The flowchart of the analysis steps in this paper.
## 4 Classification of MAC Protocols Based on Channel
### Reliable MAC  
The reliable MAC protocol scheme was proposed to avoid collision in high-density VANET scenarios via proper channel access. An adaptive byte-level HARQ was used for error control and to provide reliable communication. Via this methodology, the bit error rate and burst bit rate of the channel were calculated [98]. The frame error control redundancy size was checked, which was added to the MAC frame structure; along with the FEC bit, a checksum calculator was also added to the MAC frame. Frame segmentation and fragmentation were performed, and each frame consisted of MAC service data units. The virtual token approach performs the packet delivery mechanism (allocates each node with the specified channel access time) and forward node selection approach (selects efficient forwarders for message broadcasting) for on-time delivery of safety messages. The node that wants to transmit data initiates the virtual token formation containing the nodes present in its transmission range. The node initiator keeps track of nodes leaving and entering the token ring. An efficient forwarder node is selected that restricts other nodes to broadcast messages, thus avoiding collision.  
The use of AB-HARQ shows better error recovery probability even upon increasing the channel error. The reliable MAC provides better throughput and packet delivery ratio. Hidden terminals, energy consumption, and communication overheads are not addressed or analyzed, turning out to be the major demerits.
## 3 Modeling and Design Optimization
### Optimization Design
#### 3.2.3 Acceleration  
For the moving-magnet actuators, the mass of magnets is a major proportion of the total moving mass. Although increasing the quantity of magnets can increase the motor force, it obviously will also increase the moving mass and reduce the dynamic performance of the motor. Therefore, the acceleration can be seen as a main optimization criterion for the proposed planar motor.  
Basically, most of the three-degree-of-freedom planar motors are expected to be supported by air bearings. Ignoring the mechanical friction, the equation of acceleration can be written as  
\[a=\frac{F}{m} \tag{24}\]  
where \(m\) is the mass of mover.  
According to Figure 7, the equation for the mover mass is  
\[m=32\rho_{M}V_{M}+2\rho_{j}V_{j} \tag{25}\]  
where \(\rho_{M}\) and \(V_{M}\) are the density and volume, respectively, for one magnet, and \(\rho_  
\[a=\frac{9K_{G}X(\lambda,\gamma)}{8\rho_{M}\gamma+18\rho_{j}[\frac{\sqrt{2}B_{\
u} \lambda}{4\sigma_{0}B_{j}(1+\frac{\mu_{r}k_{z}}{8\sigma_{0}\alpha_{\gamma}^{2} \gamma})}]} \tag{29}\]  
Figure 9 shows the acceleration characteristic versus parameters \(\gamma\) and \(\lambda\). In Figure 9a, when the parameter \(\lambda\) is invariant, the acceleration decreases with the increase of parameter \(\gamma\). Additionally, the decreasing trend is obvious when \(\gamma<0.5\). In Figure 9b, when the parameter \(\gamma\) is invariant, the acceleration increases first and then decreases with the increase of parameter \(\lambda\). There is an optimal value of \(\lambda\) around 3 at which the acceleration reaches the maximum value. To obtain the optimal parameters for a maximum acceleration, the follow equation must be satisfied  
\[\frac{\partial a}{\partial\lambda}=0 \tag{30}\]
# Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks
## 3 The Proposed Mechanism
### Mathematical Moel and Factor Formulization
#### 3.4.4 Density of Nodes  
The importance of a node in the network can be determined by taking the number of nodes closely located with it. The densely deployed nodes usually sense similar data and forward to their CHs. Eliminating one or more nodes from a dense collection usually does not affect the regular operations of a network. For calculating the importance of a node, we must calculate the sets of closed neighbors (CNs). A set of CNs is the set of those nodes that are located very close to each other. The threshold distance for CNs can be set as \(\mathrm{CNDist}^{\mathrm{Thres}}\). The following procedure can be used to calculate the distance among nodes:  
\[d_{i,j}=\sqrt{\left(x_{i}\ -x_{j}\right)^{2}-\left(y_{i}\ -y_{j}\right)^{2}}. \tag{8}\]  
\(d_{i,j}\) can be used to calculate a simple distance between two nodes, \(i\) and \(j\). The propagation model \(P\left(d_{i,j}\right)\) can be defined to get the set of closed neighbors as:  
\[P\left(d_{i,j}\right)=C_{t}\frac{P_{t}}{d^{4}}. \tag{9}\]  
In the above equation, the constant \(C_{t}\) denotes the transceiver characteristic, and \(P_{t}\) the transmitting power of two connected nodes. A set of CNs can be calculated for node \(i\) as:  
\[CN_{i}=\left\{j:P\left(d_{i,j}\right)\leq CNDist^{\mathrm{Thres}}\right\}. \tag{10}\]  
While manipulating the nodes for CH selection, it is important to consider the importance of each node in the cluster. For calculating the importance of a node we can consider its CNs with their energy levels. A node having more CNs with higher energies will be considered as more suitable for being CH. The importance of node \(i\) is denoted by \(\lambda_{i}\) in the following equation.  
\[\lambda_{i}=\left\{\begin{array}{c}\frac{\sum_{j=1}^{cn}E_{CNj}^{t}}{cn}\ \text{$cn\
eq 0$}\\ 1\ \text{$cn=0$}\end{array}\right. \tag{11}\]  
The value of \(\lambda_{i}\) can be obtained by taking the number of all CNs denoted as \(cn\) and the sum of all CNs energies.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
###### Abstract  
Latest remote sensing sensors are capable of acquiring high spatial and spectral Satellite Image Time Series (SITS) of the world. These image series are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth's surfaces. More specifically, current SITS combine high temporal, spectral and spatial resolutions, which makes it possible to closely monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied to create land cover maps from SITS, these algorithms do not make the most of the temporal domain. This paper proposes a comprehensive study of Temporal Convolutional Neural Networks (TempCNNs), a deep learning approach which applies convolutions in the temporal dimension in order to automatically learn temporal (and spectral) features. The goal of this paper is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification, as compared to RF and Recurrent Neural Networks (RNNNs) --a standard deep learning approach that is particularly suited to temporal data. We carry out experiments on Formosat-2 scene with 46 images and one million labelled time series. The experimental results show that TempCNNs are more accurate than the current state of the art for SITS classification. We provide some general guidelines on the network architecture, common regularization mechanisms, and hyper-parameter values such as batch size; we also draw out some differences with standard results in computer vision (e.g., about pooling layers). Finally, we assess the visual quality of the land cover maps produced by TempCNNs.  
time series; Temporal Convolutional Neural Network (TempCNN); satellite images; remote sensing; classification; land cover mapping 2019
# A Numerical Study on Computational Time Reversal for Structural Health Monitoring
## 4 Applications with Numerical Examples
### Optimal Placement of Sensors
#### 4.3.2 Two-Dimensional Frame Structure  
In the case of the 2D beam assembly, the 12 possible positions and monitored DOFs are shown in Figure 5. The main aim of this investigation was to define the optimal set of receivers from the available fixed number of 12 existing sensors. Figure 8a shows the SNR for 31 random receiver sets. SNR is defined as the ratio of the max peak value at the source point and the next max peak value, less than the global maximum one. In order to determine the optimal solution for a specific number of receivers, the higher SNR is chosen from Figure 8a at the abscissa corresponding to that number of receivers. In Table 1 (original signals), one may observe that for 4 (\(y_{3},y_{2},y_{8},x_{8}\)) and 10 (\(y_{3},y_{2},y_{8},x_{8},x_{7},y_{6},x_{4},x_{2},y_{7},x_{6}\)) receivers, the optimal solution belongs to the same receiver set (see Figure 8b, optimal set B). Moreover, the receiver sets for two, six and eight receivers are different, as shown in Figure 8b as the optimal sets A, C and D, respectively.  
Figure 7: Signal-to-noise ratio (SNR) for various sets of receivers (**a**) using recordings of axial motion and (**b**) using recordings of transverse and rotational motion. The number of receivers is indicated on the horizontal axis.  
Considering Gaussian noise, optimal receiver sets for various numbers of sensors differ from each other, except for the scenarios with 8 and 10 receivers. In Figure 9a, the SNR is plotted after randomly placing 31 receiver sets. Specifically, in Table 1 (noise considered), the optimal receiver sets for various numbers of sensors are shown. In Figure 9b, the SNR evolution with consecutive placement of receivers for these optimal sets is depicted.  
Figure 8: (a) SNR plot for 31 random receiver sets; and (b) optimal receiver sets. A is for 2, B is for 4 and 10 receivers; C is for 6 receivers; while D is for 8 receivers. On the horizontal axis, the number of receivers is indicated.
## 1 Introduction  
The hippocampus is considered a strong epileptogenic area within the human brain [1]. High-resolution MRI of the hippocampus can identify and potentially exclude morphological abnormalities of the hippocampus such as hippocampal sclerosis or limbic encephalitis and has thus emerged as a valuable diagnostic modality in patients with epileptic seizures [1,2].  
The MRI anatomy of the hippocampus is complex and intricate [1,3,4,5,6]. T2-weighted (T2w) imaging plays an important role in comprehensively visualizing the fine anatomical structures of the hippocampus and in potentially detecting the subtle changes indicative of pathology.  
To this extent, high-resolution, thin-slice T2w TSE images are routinely required and acquired in clinical practice. However, such sequences are time-consuming, easily requiring more than five minutes of scan time, presenting economic implications due to limited scanner availability but also placing a burden on patients, who must remain motionless in an uncomfortable environment--making scan time reduction a clinical and logistical priority. With recent advances in MRI technology, including compressed sensing/compressed SENSE (CS) [7], image acquisition can be shortened considerably without compromising image quality or diagnostic accuracy. Specifically, these MRI acceleration technologies can be leveraged to either reduce scan time or improve spatial resolution without prolonging scan time [8].  
Recently, artificial intelligence (AI)-powered deep learning (DL) MRI reconstruction technologies have been proposed to further enhance sequences acquired by CS techniques. Specifically, deep neural networks have shown promise for the efficient reconstruction of images from highly undersampled k-space data, as is the case for CS-accelerated MRI [9,10,11]. Exemplarily, an approach using a convolutional neural network, called adaptive-CS-Net [12,13,14], has shown potential to improve imaging across a variety of anatomical regions relative to standard compressed sensing [10,15,16,17,18]. By combining this approach with a second denoising DL neural network (Precise-Image-Net), removal of ringing artifacts and upscaling of image resolution can be achieved [12,19,20,21,22,23]. In the following, this combination is referred to as the super resolution reconstruction (SRR) algorithm. While this algorithm has successfully improved imaging of the prostate [12,24], breast [25], knee [23,26], and heart [27], its application to the hippocampus remains unexplored.  
Thus, based on the considerations outlined above and the potential of the SRR algorithm, we sought to present and compare the clinical performance of a high-resolution coronal 2D T2w TSE sequence, reconstructed either with standard CS or SRR, for hippocampal imaging in patients with suspected epileptic seizures. Specifically, we sought to determine whether SRR offers advantages over conventional CS reconstruction by improving image quality at a fixed scan time or by enabling shorter scan times without compromising image quality--thereby enhancing the overall balance between acquisition time and diagnostic imaging performance.
## Materials and Methods
### Dispersion Method  
All dispersion protocols were performed using a laboratory dissolver device (Dispermat AE by Gentzman) with a double wall vacuum container in combination with a refrigerated thermostatic bath and circulator, provided by GRANT, capable for temperature control within \(\pm\)1 \({}^{\circ}\)C accuracy and dispersion under vacuum conditions. Shear mixing was selected among other dispersion techniques to avoid potential CNT length shortening and degradation of the epoxy polymer matrix. A 45-mm-diameter toothed impeller disk was selected, offering medium shear forces to dispersion, to reduce the agglomerates' size. The conditions of all dispersion protocols were rotary speed at 3000 rounds per minute (rpm) and temperature at 25 \({}^{\circ}\)C.