esa-sceva/satcom-chunk-collection huggingface dataset:
# Luminescence Materials for Dye-Sensitized Solar Cells: Advances and Directions
## 2 Fundamental Principles  
### Working Operation  
DSSCs consist of several key components such as a photo-anode, a dye sensitizer, an electrolyte, and a counter electrode. The photo-anode is composed of fluorine-doped tin oxide (FTO), coated with a mesoporous semiconductor layer, such as titanium dioxide (TiO2), which provides a sufficient surface area for the dye molecules to be adsorbed. The operation of DSSCs relies on a series of electrochemical processes that collectively convert light into electricity. When sunlight strikes the dye molecules adsorbed on the surface of the TiO2 photo-anode, it excites electrons to a higher energy state. These excited electrons are rapidly injected into the conduction band (CB) of the TiO2, where they diffuse through the nanoporous structure to the back contact and eventually flow through the external circuit, generating an electric current [45]. Meanwhile, the oxidized dye molecules are regenerated by electrons from the electrolyte, which undergoes a redox reaction. The iodide ions (I\({}^{-}\)) in the electrolyte donate electrons to the oxidized dye, converting back to tri-iodide ions (I\({}^{-}_{3}\)), which diffuse to the counter electrode. At the counter electrode, electrons from the external circuit reduce the I\({}^{-}_{3}\) back to I\({}^{-}\), completing the cycle. The working operation is presented in Figure 1a. The seamless flow of electrons through the various components of the DSSC forms the basis of its photovoltaic operation [46; 47]. The performance of DSSC relies on factors such as the sufficient surface area of the coated TiO2 for dye adsorption, effective absorption of light by the dye, the efficient injection of electrons into the TiO2, and the rapid regeneration of the dye and electrolyte. It can be fabricated on flexible substrates and operate efficiently under low-light conditions, making it suitable for both indoor and outdoor applications.
## 3 Communication and Channel Modeling
### Communication Performance  
Communication performance plays a crucial role in the optimization of UAV navigation and trajectory design, and it is integrated into the objective function and optimization problems. Key performance indicators for communication systems in UAV-based networks are QoS, secrecy, reliability, and efficiency in terms of energy and spectrum utilization. These factors ensure that the UAVs can maintain reliable, secure, and efficient communication links with UEs or other UAVs, thereby enabling seamless operation in dynamic environments.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 2 Basic Theory of CNN and Capsule Network
### Capsule Network  
Traditional CNNs mainly extract the main features to the next layer through the pooling mechanism--that is, selecting the regional maximum or regional average. However, this operation leads to the loss of valuable spatial information between layers, which further reduces the network recognition rate. To solve the above problems, [PERSON] et al. [24] proposed a novel network-capsule network, and this model used dynamic routing mechanism to support transmission between different capsule layers.  
In capsule networks, capsules partially replace neurons in traditional neural networks to more effectively identify features. Specifically, a capsule is a vector composed of multiple neurons, and its length indicates the probability of entity existence. Each neuron in the capsule represents various properties of a specific entity, such as the pose (position, size, direction), deformation, speed, albedo, tone, texture, and other characteristics of the object [24].  
Just as different layers of deep CNN learn different semantic attributes of images, capsules can also be organized into different layers. The low-layer capsules learn low-level characteristics and output to the \"more similar\" high-layer capsules through dynamic routing.  
The flow of the dynamic routing algorithm is shown below. Among them, \(b_{ij}\) represents the intermediate weight coefficient of the capsule \(i\) in layer \(l\) and capsule \(j\) in layer \(l+1\), which is set to 0 before the first iteration; \(\hat{\mathbf{u}}_{ji}\) represents the intermediate prediction vectors of the capsule \(i\) in layer \(l\) and capsule \(j\) in layer \(l+1\), which is obtained by multiplying the output of capsule \(\mathbf{u}_{i}\) and the corresponding weight matrix \(\mathbf{W}_{ij}\). \(\mathbf{W}_{ij}\) encodes very important spatial relationship between low-level features and high-level features. Its size is \(\mathbf{W}_{ij}=\left(D_{i},\,D_{j}\right)\), where \(D_{c}\) represents the dimension of capsule \(c\). \(\mathbf{W}_{ij}\) is calculated by a BP algorithm, like the parameters of a traditional neural network. During the first iteration, since \(b_{ij}\) is set to 0, all the prediction vectors \(\hat{\mathbf{u}}_{ji}\) have the same weight coefficient \(c_{ij}\). With the process of iteration, the prediction vector \(\hat{\mathbf{u}}_{ji}\), which is more similar to the high layer capsule \(j\), has a larger dot product result, which increases the corresponding weight coefficient \(c_{ij}\) and finally makes the low-layer capsules \(i\) tend to output to more similar high-layer capsules \(j\). The process of dynamic routing is similar to the idea of automatic clustering. Through dynamic routing, the automatic aggregation of low-level features is completed.
# Crowdsourcing Framework for QoE-Aware SD-WAN +
## 2 Related Works
### In-Network QoE Models
In [23], a method was proposed for predicting QoE using machine learning algorithms leveraging SDN. An architecture was designed that uses previously measured MOS values from users, collected during different network conditions. These data, along with objective measures, are supplied to machine learning algorithms to predict MOS values for the current network conditions. The SDN QoE Monitoring Framework (SQMF) [24] is a monitoring application that aims to preserve QoE for both video and VoIP applications in real time, regardless of unexpected network issues, by continuously monitoring network  
Figure 1: In-Network QoE Measurement Framework [21]. parameters and using QoE estimation models. In [25], a new QoE-Aware management architecture over SDN was proposed, which was able to predict MOS by mapping different parameters of QoS into QoE. The proposed framework was designed to autonomously control and allocate the infrastructure of underlying network resources with the ability to avoid QoE degradation, optimize resource use, and improve QoS performance.
## 6 Simulation Results and Discussion
### Sensitivity Analysis
#### 6.2.1 Global Horizontal Irradiance (GHI)  
Based on the data taken from National Renewable Energy Laboratory Database, the minimum GHI (5.05 kWh/m\({}^{2}\)/day) and the maximum GHI (6.69 kWh/m\({}^{2}\)/day) were the ranges on which the sensitivity variables varied. Considering \(\pm\)7% changes of the average  
Figure 14: Yearly power delivering status. (**a**) PV system, (**b**) diesel generator, (**c**) battery.  
value (5.94 kWh/m\({}^{2}\)/day), the sensitivity variables of GHI were 5.05, 5.46, 5.94, 6.35, 6.68 kWh/m\({}^{2}\)/day. As we can observe from the optimal plot (Figure 15), maintaining the scale average load (76.96 kWh/day) when diesel price is minimum (USD 0.6/L) and GHI is maximum (6.68 kWh/m\({}^{2}\)/day), the minimum LCOE is USD 0.170/kWh. On the contrary, when the diesel price is maximum (USD 1/L) and GHI is minimum (5.05 kWh/m\({}^{2}\)/day) the maximum LCOE is USD 0.198/kWh. The effect of GHI variation on LCOE, NPC and RF can also be observed from Figures 16 and 17. When we set the load to 100.01 kWh/day and the diesel price USD 0.62L, the LCOE was lowered to USD 0.168/kWh and RF increased to 79.4%, as depicted in Figure 16. Keeping the minimum diesel price and load but increasing GHI (from 5.05 to 6.68 kWh/m\({}^{2}\)/day) will reduce the NPC from USD 124,086 to USD 116,059 (Figure 17), the LCOE from USD 0.182 to USD 0.170/kWh (Figure 15), and increase the RF from 82.7% to 87.3%.
# Ultrasound-Guided Procedures in Common Tendinopathies at the Elbow: From Image to Needle
## 3 Common Elbow Pathologies
### Distal Triceps Tendinopathy
#### 3.4.1 Essential Anatomy  
The triceps brachii muscle (TBM) is formed from three heads. The long head originates in the infraglenoid tubercle of the scapula. The lateral head originates in the posterolateral aspect of the humeral shaft, proximally to the radial sulcus. The medial head originates in the posteromedial aspect of the humeral shaft, distally from the radial sulcus. The TBM tendon inserts into the olecaronon (Figure 11). The attachment is approximately 12-14 mm distal to the olecaronon tip with an average width of 40 mm [84]. The lateral and long heads of the TBM converge distally and form the superficial part of the triceps tendon attachment. This part attaches to the olecaronon's medial aspect, where it may converge with the anconeus muscle's fascia [38]. The medial head forms the deep portion of the triceps inserted into the olecaron [85] and is mainly muscular at its insertion [86]. Some authors describe the space filled with fatty tissue between these attachments like \"distal pretricipital space\" on MRI sections and cadaver specimens [87]. The triceps muscle is innervated by the radial nerve.  
Figure 11: The tricipital tendon (right elbow). (**a**) The tricipital tendon is attached to the olecaronon. The tendon has a bipartite arrangement. The medial head has separate insertion from the common tendon of lateral and long heads. The dashed line indicates the border between them. (**b**) Attachment of the common and medial heads on the sagittal section. The medial head’s insertion is more muscular and more profound than the common head insertion. (**c**) Location of muscular and ligamentous attachments on proximal ulna from the posterior aspect. Black asterisks: distal pretricipital space filled with fat and connective tissue, white asterisk: the posterior fat pad inside the joint, white arrowhead: ulnar nerve, AL: annular ligament, An: anconeus muscle, Br: brachialis muscle, Br: bracchioradialis muscle, CFT: common flexor tendon, ECRL: extensor carpi radialis longus muscle, ECRB: extensor carpi radialis brevis muscle, EDig: extensor digitorum muscle, ECU: extensor carpi ulnaris muscle, FCU: flexor carpi ulnaris muscle, FDS: flexor digitorum superficialis muscle, FDP: flexor digitorum profundus muscle, JC: joint capsule, ME: medial epicondyle, Ole: olecaron, Pte: pronator teres TriMe: triceps brachii—medial head, Tri-CoH: triceps brachii–common head (lateral and long head).
# Virtual Network Function Embedding under Nodal Outage Using Deep Q-Learning
## 6 Critical Analysis  
This section will present a summary of the challenges that occurred during the construction of the DRL model. As mentioned earlier, the DRL model was constructed only with one NN, i.e., primary NN, which learns both the behaviour policy and the target policy to solve the NFV-RA problem. The usage of the primary NN's weights and biases (parameters) for determining two distinctive tasks causes highly unstable results. That is, the target values obtained from the primary NN constantly wavered over the iterations; this results in unstable learning.  
Thus, to provide stability in the learning, the DRL model's performance was improvised using the target NN and Experience Replay method. To enhance the performance further, we have used a heuristic model. The model's foremost purpose is to prioritize the nodes based on the available nodal capacity in the topology; by doing so, we have increased the chance of successfully embedding of the arriving services. For example, in the case of 2 core CPU and 16 VNF per core for 3 VNF per service, the average SAR for a single NN DQL model was 69.83, whereas, with the improved version of the DRL model, the average SAR was escalated to almost 74 for Netrail topology. The single-NN-DRL model's SAR over each episode ranged from 65 to 73 SAR (i.e., [69; 69; 68; 72; 72,..., 69; 72; 69; 70; 70]), showing high placement instability; however, this trend of high fluctuations over the episodes is not shown in the adapted DRL model, making an assurance of stability in learning.  
DRL models have a tendency to encounter the overfitting or underfitting problem. These issues or problems are avoided by incorporating techniques like, firstly, the \(e\) Greedy method to estimate the action per state. Based on the probability of \(e\), the model will either choose the action randomly or based on the maximum Q value-secondly, introducing a regularization method called Dropout. Here, the neurons are randomly assigned as inactive neurons in the hidden layers. This randomness is based on the probability of dropout (p); in our model, we have considered p as 0.2. This makes learning in a sparse NN, reducing any overfitting. Moreover, experience replay also lessens any overfitting problem as it breaks the correlation between the transitions.
# Physico-Mechanical Properties of Alkali-Activated Based Composites Using Recycled Fire Fibers
## 3 Results
### Dry Bulk Density and Water Absorption  
The water absorption (Abs, as a percentage) and dry bulk density (\(\rho_{\mathrm{d}}\)) for the control mortars and the mortars with a partial substitution of siliceous and limestone aggregates for TFs are depicted in Figure 4. It depicts how the \(\rho_{\mathrm{d}}\) lowered when increasing the substitution of aggregates for TFs because the density of the siliceous and limestone aggregates was twice that of the TFs. In the mortars with 100% TFs without mineral aggregate, the \(\rho_{\mathrm{d}}\) lowered due to the reduction in the amount of the cementing binder. In all cases, the \(\rho_{\mathrm{d}}\) values were lower than those obtained for the control mortars [32].  
Figure 3: Box (thermally insulated sides) used for the thermography test.  
The absorption value increased with the larger amount of TFs because TFs are capable of trapping air and causing an increased porosity of the matrix. These results align with previous research works, which indicate a higher air content in the rubber mortars compared with regular mortars [21, 33]. This can be attributed to the rough surface of TFs and their ability to trap air [26].  
The microstructural characterization (Figure 5) observed by FESEM allowed us to analyze the cementing matrix and the interfacial transition zone for mortars TF2/1, TF SIL25, and TF CAL25 after 7 curing days at 65 \({}^{\circ}\)C. Figure 5a,b shows smaller porosity in the cementing phase (geopolymer) and a thinner paste/TF gap compared with the 100% TF sample (Figure 5c), which showed that more pores were present in the matrix and had a thicker gap. This behavior would explain the greater absorption and lower density of these mortars compared with those made with 100% mineral aggregate. Obviously, this behavior would directly affect the mortar strength [19].  
Figure 4: Physico-mechanical properties of mortars: (**a**) dry density for the control mortars and for those with 15% or 25% volume replacement with TFs; (**b**) dry density for the composite mortars without aggregate; (**c**) water absorption for the control mortars and the mortars with 15% or 25% volume replacement with TFs; (**d**) water absorption for the composite mortars without aggregate. In (**a**,**c**), SIL (green columns) corresponds to mortars with the siliceous aggregate, CAL (red columns) corresponds to mortars with the limestone aggregate.
## 3 S-Box Construction
### Our Proposed Construction
exhibits the algebraic expression with simple terms, might expose ciphers using the S-box to an interpolation attack [6]. From our observation, column-wise rotation does not impact the cryptographic properties of the function. However, different to row-wise rotation, the cryptographic properties might change significantly. Our analysis found that, for row-wise rotations of 64, 128 and 192 places, the cryptographic properties of the S-box can preserved. Then, another round of iteration will be conducted in our algorithm. The next iteration contains a multiplication step of \(\mathbb{F}_{p}\) with a coefficient \(\alpha\) where \(\alpha\) ranges from 0 to 255; it then continues again with a redundancy analysis table. The stopping criteria that we fix in our iterations are based on the final S-box produced that meets our requirement or based on the cryptographic properties of \(\mathbb{F}_{p}\) that are found to worsen (i.e., the cryptographic properties exhibited are lower than the values stated in Table 1). In both cases, another set of \(\mathbb{F}=\mathbb{F}_{1}+\mathbb{F}_{2}\) will be selected for our next experiments. Table 7 shows the optimal S-box obtained from the proposed method and is represented as hexadecimal. The first column in Table 7 denotes the first four bits, while the first row denotes the remaining four bits of the 8-bit input to the S-box. For instance, the input 71 gives the output CE, (i.e., F(71) = CE. For input 71, element 7 is selected from the first column while element 1 is selected from the first row). The cryptographic properties exhibited by this S-box meet all the desired values stated in Table 1.
## 2 Tests and Methods
### Preprocessing of Gas Concentration Data
#### 2.3.1 Data Detrending Based on Wavelet Decomposition  
The gas concentration data series can be regarded as the superposition of the real signal and the abnormal signal (Equation (1)). That is,  
\[C(t)=A(t)+D(t) \tag{1}\]  
where C(\(t\)) is the original monitoring signal; \(A(t)\) is the real signal; \(D(t)\) is the abnormal signal.  
Wavelet decomposition is a fine purification method for non-stationary time series. According to the characteristics of different wavelet coefficients in the wavelet domain for the effective and abnormal signals in the gas concentration monitoring data, the expansion factor and the translation factor v are changed (Equation (2)), and the signals of different frequencies are decomposed into high-frequency and low-frequency information. Among them, \(C(t)\) is the original data information. \(A(t)\) array represents the low-frequency data part of each layer, corresponding to the general outline of original data. The \(D(t)\) array represents the high-frequency data part of each layer, corresponding to the original data details, and most of the abnormal signals are gathered here. In order to avoid the influence of low-frequency data offset, the analysis is focused on the fluctuation of the data trend itself, and the follow-up calculation is carried out for the high-frequency components.  
\[W_{f}(a,v)=\langle f(t),\psi_{a,v}(t)\rangle=|a|^{-1/2}\int_{R}f(t)\overline{ \psi(\frac{t-v}{a})}dt \tag{2}\]  
where \(a>0\) is the expansion factor, which mainly adjusts the position and shape of the window. \(v\) is the translation factor, which can be positive or negative. It mainly adjusts the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number Density** & **Samples Size** & **Sample Weight** & **Number Density** & **Samples Size** & **Sample Weight** \\ (\(\times 10^{4}\)**Mussels/m\({}^{2}\))** & **(Mussels/100 cm\({}^{2}\))** & **(kg/100 cm\({}^{2}\))** & **(\(\times 10^{4}\)**Mussels/m\({}^{2}\))** & **(Mussels/100 cm\({}^{2}\))** & **(kg/100 cm\({}^{2}\))** \\ \hline
0.5 & 50 & 0.0204 & 4.0 & 400 & 0.1465 \\
1.0 & 100 & 0.0409 & 4.5 & 450 & 0.1778 \\
1.5 & 150 & 0.0645 & 5.0 & 500 & 0.1973 \\
2.0 & 200 & 0.0840 & 5.5 & 550 & 0.2133 \\
2.5 & 250 & 0.0945 & 6.0 & 600 & 0.2319 \\
3.0 & 300 & 0.1146 & 6.5 & 650 & 0.2533 \\
3.5 & 350 & 0.1270 & 7.0 & 700 & 0.2803 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Density of _Limnoperna fortunei_.  
position of the plane time axis window. \(f(t)\) is the analysis signal function; \(\psi_{a,v}(t)\) is called the fundamental wavelet.
# Space Charge Accumulation and Decay in Dielectric Materials with Dual Discrete Traps
## 4 Results and Discussion
### Discussion  
The coupling charge trapping/de-trapping dynamic processes increase the time needed for the relative deeper trap centers to reach steady states. This will prolong the periods for the energy distributions of charges to reach the thermodynamic equilibrium. Consequently, the coupling dynamics will increase the response times of transient polarization/depolarization currents, and space charge accumulation/decay processes. Therefore, the experimental results in a relatively narrow time window cannot fully interpret the charge transport behavior of polymeric dielectric materials. [PERSON] et al. measured the transient current densities of LDPE, XLPE, and HDPE samples at 20 \({}^{\circ}\)C. It was found that the transient current densities decayed for a very long time, and the steady states were not reached even after applying an electric field of \(6\times 10^{7}\) Vm\({}^{-1}\) for about 24 h [36]. Accordingly, the conductivities of the  
Figure 8: Surface potential decay curves of low-density polyethylene samples after charging by corona discharging for different times (**a**) and the de-trapped charge density as a function of trap energy calculated from surface potential decay experimental results (**b**).  
Figure 7: Space charge decay properties at the same position in the dielectric material charged for various times under the voltages of 6.0 kV (**a**), 7.5 kV (**b**), and 9.0 kV (**c**).  
dielectric materials calculated from the experimental results at short poling durations would be larger than that calculated from the experimental results at long poling durations. In addition, the energy distribution of traps of XLPE evaluated from the space charge accumulation/decay experimental results at various poling durations are different.  
The coupling dynamic processes will establish a dynamic equilibrium rather than a thermodynamic equilibrium in the dielectric materials [11]. It means that the traps in dielectric materials are not filled from the deepest levels upwards [22]. The traps are filled simultaneously. When the material is not in thermodynamic equilibrium, we cannot use Fermi-Dirac function (or Boltzmann function) to calculate the trapped charges density in each trap centers. Consequently, we cannot easily estimate the effective carrier mobility at transient states in the dielectric materials containing multiple discrete trap levels, the exponential energy distribution of traps, and Gaussian energy distribution of traps, etc. This means that we need to establish a novel charge transport and trapping model for dielectric materials with different types of the energy distribution of trap centers.
## 4 Modeling and Validation
### Ensemble Modeling  
The top 3 most accurate models (_logistic regression, neural network MLP, and support vector machine_) were combined into an _ensemble_ model. The _ensemble_ model was run 10\(\times\), with a new random 80:20 training/test dataset combination each time. This methodology of training different learning algorithms on the original training set to generate the base classifiers is known as a heterogeneous ensemble. The accuracy for each run was computed through majority voting by selecting the class label predicted most frequently between the ensemble models. Then, the run accuracies were averaged for an overall combined accuracy. Figure 6 shows the ensemble model flow scheme.