esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Questionnaire Study  
A questionnaire was delivered online to the occupants who were working in the investigated part of the buildings during the monitoring week, and included personal data, work data, psychosocial environment/characteristics, physical effects (IAQ perception, comfort and health related symptoms). The questionnaire was also used in our previous studies [6; 21; 34] and the detailed description is mentioned elsewhere [35]. More specifically, occupants were asked to evaluate aspects of IEQ, such as the overall comfort, temperature (overall, too hot/cold, variation), air movement, air quality (overall air quality satisfaction, humid or dry air, stuffy or fresh air, odor), light (overall light satisfaction, natural, artificial, glare), noise (overall noise satisfaction, outside noise, noise from building systems, noise within the building), vibration, amount of privacy, office layout, office decoration, and view from the windows in a 7-point scale from \"1, dissatisfied\" to \"7, satisfied\"). Regarding their control perception of the IEQ, occupants were asked to evaluate five controlling parameters: temperature, ventilation, shade from the sun, light and noise from \"1, not at all\" to \"7, full control\", and then they reported the \"exercise control\" [36; 37] to determine how many times they had the ability to use the available control types that were installed in their offices (if applicable). The questionnaire asked for the following symptom groups: eye irritation (dry eyes, watering or itchy eyes, burning or irritated eyes), respiratory (runny nose, blocked or stuffy nose, dry/iritated throat, cough, sneezing, wheezing, chest tightness, phlegm/mucus or breathing difficulty), heart (tachycardia, irregular heartbeats, bradycardia), skin (dry skin, rash, or irritated skin), and general symptoms (headache, lethargy, unusual tiredness, flu-like symptoms). An extra, final, part consisted of a set of questions related to the occupant's opinion on the COVID-19 pandemic countermeasures which were implemented in office environments. Furthermore, occupants were asked to evaluate their perception concerning the IEQ before the lock-down period.  
The study was anonymous, and the participants gave their consent prior to participation. The study was approved by the UOWM's (University of Western Macedonia) ethics committee. Occupants in offices, in which IAQ measurements were in progress, were invited to complete the questionnaire. When answering the questionnaire, occupants were not informed about the results of the measurements.
## 3 Results
### Properties of the NiO and PTAA/NiO Films
As shown in Figure 6, the contact angles of the two substrates with water were 55.9\({}^{\circ}\) and 85.6\({}^{\circ}\) for NiO and PTAA/NiO, respectively, and the measured contact angles for the PTAA/NiO surface were significantly increased compared to those of the NiO surface. As is well known, large contact angle values such as for the PTAA film indicate that the surface properties are hydrophobic. These HTL surface properties affect the crystal formation of perovskite, and in particular, hydrophobic surface properties can form large crystals of perovskite film [39]. Therefore, the surface properties of HTL not only affect the film quality of the perovskite, but also contribute to charge transport and extraction at the interface with the perovskite. Figure 6 shows the SEM top view images (c and d) of the perovskite films on NiO layers without and with the PTAA layer. The perovskite on the PTAA/NiO bilayer showed a much larger particle size (\(\sim\)550 nm), thus indicating improved crystallization even with the hydrophobic properties of the substrate. The use of a hydrophobic substrate, which is known to be beneficial for crystallization of the perovskite layer, suppresses non-uniform nucleation of the perovskite and improves the grain boundary expansion rate of the perovskite to form a high-quality perovskite film [45]. These properties enhance the charge extraction ability of the perovskite and PSCs and contribute to the low series resistance of the interface to form an excellent FF.
# Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach
## 4 System Model
### Profit of CHP  
CHP produces electric power as well as thermal power leading to a high overall efficiency, its profit function is represented as [11]:  
\[P_{CHP}(t)=p_{e}(t)P_{i}(t)+p_{h}(t)H_{i}(t)-F_{i}(P_{i}(t),H_{i}(t)) \tag{10}\]  
\[F_{i}(P_{i}(t),H_{i}(t))=\phi+\beta P_{i}(t)+\gamma P_{i}(t)^{2}+\delta H_{i}(t )+\theta H_{i}(t)^{2}+\varphi P_{i}(t)H_{i}(t) \tag{11}\]  
where \(\phi\), \(\beta\), \(\gamma\), \(\delta\), \(\theta\), \(\varphi\) are the cost coefficients of CHP system.  
The heat-to-electric rate of CHP may be different due to the variable load conditions. CHP can operate either in the Following Thermal Load (FTL) mode or in the Following Electric Load (FEL) mode. For CHP \(i\), \(i\in V_{CHP}\), the coupling model of thermal and electric is denoted by [34]:  
\[P_{i}(t)=K_{CHP}H_{i}(t)=\frac{\eta_{e}}{(1-\eta_{e})\eta_{r}}H_{i}(t) \tag{12}\]  
where \(\eta_{e}\) is the electric efficiency, \(\eta_{r}\) is the heat recovery rate of heat recovery boiler, and \(K_{CHP}\) is the coupling coefficient between heat and electricity.  
Here, we can rewrite Equations (10) and (11) when the cost of CHP is denoted by \(H_{i}\):  
\[P_{CHP}^{H}(t)=p_{e}(t)P_{i}(t)+p_{h}(t)H_{i}(t)-F_{i}(H_{i}(t)) \tag{13}\]  
\[F_{i}(H_{i}(t))=\phi+\beta(K_{CHP}H_{i}(t))+\gamma(K_{CHP}H_{i}(t))^{2}+\delta H _{i}(t)+\theta H_{i}(t)^{2}+\varphi H_{i}(t)(K_{CHP}H_{i}(t)) \tag{14}\]  
When the load rate of CHP is less than 30%, its electric efficient will be much lower, so we only start the CHP unit when its load rate is greater than 30%:  
\[P_{i}(t)=\left\{\begin{array}{c}P_{i}(t),\ P_{i}(t)\geq 0.3 Cap\\ 0,\ P_{i}(t)<0.3 Cap\end{array}\right. \tag{15}\]  
where \(Cap\) is the rated power of CHP unit and it is assumed that the electric efficient is a constant when the load rate of CHP is over 30%.
# Enhanced Pathological Element-Based Symbolic Nodal Analysis
## 1 Introduction  
Nullor and mirror pathological elements have been proven to be very valuable components for network analysis, synthesis and design [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. The main reasons for the popularity of the nullor elements are their ability to model active circuits independently of the particular realization of the active devices. The symbolic circuit analysis can be carried out by only applying nodal analysis (NA). The pathological grounded voltage mirror (VM) and current mirror (CM) were defined in 1999 [14]. They can be used to represent active devices with current or voltage reversing properties in concise circuit structures compared to their nullor-equivalent counterparts. The grounded mirror (GM) elements have been further extended to include the floating mirror (FM) elements [15]. The FM elements were used to derive pathological sections to ideally represent various popular analog signal processing properties that involve differential or multiple single-ended signals. Therefore, many new active devices with such characteristics can be modeled using nullor-grounded mirror-floating mirror (NGMFM) elements in more compact forms compared with their grounded pathological equivalences [15; 16; 17; 18; 19; 20; 21].  
The symbolic NA using RLC-grounded pathological equivalences instead of their RLC-nullor representations of circuits was presented in [20] and computational algorithm was reported in reference [21]. With a smaller number of nodal equations compared to previous method [22], it achieves a considerable reduction in the order of the system of equations and in the generation of nonzero coefficients into the nodal admittance matrix. As a result, the computational complexity during the solution of the system of equations is reduced. However, a limitation of the formulation methods inreferences [20; 21] is that the floating pathological elements cannot be included into the formulation process. Thus the compact NGMFM equivalent circuits cannot be used for NA. Some attention had been paid to the finding of nullor-grounded mirror (NGM) equivalences of the popular active devices with differential or multiple single-ended signal properties [23] and the addition of extra equations during performing symbolic NA [24]. Nevertheless, both approaches incur the increment on the order of the built nodal admittance matrices which complicate their solution process. Besides, in all of the symbolic NA articles in literature, the voltage signal sources are replaced by their nullor equivalences before applying NA.  
In this paper, a systematic analytical procedure is proposed that performs efficient NA by using lower-complexity RLC-NGMFM networks. This proposed method makes good use of the compactness of floating mirror models and treats input voltage signal source as a grounded norator to avoid using its equivalent circuit. The effect on the reduction of the order of the system of equations matrix when performing symbolic NA is demonstrated by practical examples.
## 3 Materials and Methods
### Nonstandard State Detection  
We can see a long-term trend in railway transport to implement a unified European Train Control System (ETCS) within all the EU member countries. However, the complex Europe-wide systems are primarily designed for main railway corridors with high traffic intensities. The application of the above-mentioned control systems on regional and single lines is currently not counted on (regarding their low capacities). The continuing occurrence of crisis operational situations has proved that the lower frequency of regional and single lines does not significantly reduce the risk of train collision or nonstandard situation occurrence. Generally, nonstandard or crisis situations within single lines can be considered as situations when two rail vehicles (trains) are located within an under-the-threshold distance. The easiest solution seems to be testing of the air distance; nevertheless, this solution has certain limits, e.g., in line crossings or on a non-linear line course (see Figure 13).  
The above-indicated example clearly shows an unsuitable access of air distance in relation to the line profile. The real distance (grey curve) is 2.251 km, while the air distance of the monitored rail objects (red interrupted line) corresponds to 1.514 km, which is almost 0.737 km less than the real length.  
For a more realistic calculation of the distance between two rail vehicles, a multilayer rail network model reflecting an undirected graph combining the data-micro and data-meso layers was used. The data-meso layer was used for the detection of nonstandard situations that were divided into three groups:  
* Opposite direction train driving;
* Parallel train driving;
* Moving vs. non-moving trains.  
It was vital to differentiate situations when two vehicles were located on:  
* One or an identical edge,
* Two neighbouring edges when the incidental vertex of the two edges did not dispose of any track branching or vehicle crossing (e.g., stations).  
Both situations are demonstrated in Figures 14 and 15.  
Figure 13: Real vs. air distance.  
The data-micro layer was then used for the calculation of the physical distance between two monitored rail vehicles. This data layer had the lowest degree of abstraction, where each edge corresponded to a length of 100 m.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 3 System Model
### Computation Model  
For the sake of simplicity, each satellite terminal generates only one computation task at a time, and the set of tasks is denoted as \(\mathcal{W}=\{W_{n}|W_{n}\triangleq\{D_{n},C_{n}\},n\in\mathcal{N}\}\). \(D_{n}\) represents the amount of data that the task \(W_{n}\) needs to upload in bytes. \(C_{n}\) represents the required CPU computing capacity in cycles per second. In space, onboard devices require additional protection from the space environment, especially from particle radiation. On the other hand, satellites are strictly limited in size, power consumption and weight. Therefore, the performance of spaceborne MEC servers is much worse than that of ground-based edge computing servers, and its computing capacity is very limited. Assume that the total computing capacity of each satellite is equal, and the maximum value is expressed as \(F_{m}\) in cycles per second. A MEC server on a single satellite may not be able to meet the computing requirement of a user's task. It is reasonable to assume that the computing task can be divided, and the variable \(x_{n,m}\) is used to represent whether the computing task \(n\) of the ground terminal is offloaded to satellite \(m\). Then, there are the following constraints:  
\[\sum_{m\in\mathcal{M}}x_{n,m}=1,\forall n\in\mathcal{N} \tag{3}\] \[\sum_{n\in\mathcal{N}}x_{n,m}C_{n}\leq F_{m},\forall m\in \mathcal{M}\] (4) \[x_{n,m}\in[0,1] \tag{5}\]
# A Swarm-Based Approach to Generate Challenging Mazes
## 3 Swarm Movements to Generate Challenging Mazes
### Problem Statement  
Let our objective be to generate a challenging maze of \(n\)-by-\(m\) cells, in which there is a path from the entry point to the exit point lying at the other end of the maze. First, we define the top left corner as the entry point and the bottom right corner as the exit from the maze. We assume that our maze is a typical model, i.e., there is only one solution to reach the goal location. With the defined number of rows and columns, we select such a cell wall arrangement that will allow us to obtain the most challenging maze possible. The difficulties of moving within the maze involve dead ends and winding paths. All the corridors between the partitions are cell-wide. The maze does not contain any cycles, which means that one can arrive only once at one forking-path point, since multiple passing through the same corridor is not admitted. Note that the corridor is defined as a path between two cells, each of which is either the beginning, the end, a fork, or a dead end in the maze. Therefore, we can solve the path finding problem for example by [PERSON]'s algorithm [49].  
Now, we talk about the complexity of mazes. Note that the complexity measure of a maze M may be different and the measurement of complexity in mazes is a difficult problem [50; 51]. Hence, it is of value to be aware of factors affecting the complexity of the maze, as illustrated in Figure 1.  
Figure 1: Factors effecting the measure of the complexity of mazes.  
The complexity of the maze depends on the number of cells that comprise the maze (related to the length of the path between the start and end points), the density of the obstacles, the number of corridors, and their complexity.  
It is well note that the measure of the complexity of a corridor depends on existing turns. When a corridor alters quickly its direction, the measure of its complexity will be the higher. Therefore, complexity depends to a significant degree on topological and geometric properties of a given maze such as the length of the solution path, twistiness, and the number and length of the branches. In [20], new metrics for classifying the complexity of mazes based on agent-independent and agent-dependent characteristics were introduced. As we can see, there are different ways in which the measure of the complexity can be calculated. A more in-depth discussion on conducting a complexity analysis can be found in [50; 51].  
Now we turn to the complexity of regular mazes used in our approach. Let \(h\) be a corridor with two endpoints and \(T\) be a solution of a maze \(M\) as the path of an individual moving between the entry and exit points. If the direction differs from the current direction by \(90^{\circ}\) (either to the left or to the right), then we say that a corner point belonging to the corridor \(h\) exists.  
As mentioned above, depending on factors affecting the complexity of the maze, different maze complexity metrics can be depicted. Therefore, we consider two different variants for complexity measures.
## 4 PAU-RAD Design, Development and Integration
### PAU-RAD Hardware Description
One part is a self-designed Arithmetic-Logic Unit (ALU), where the DBF and radiometric data process takes place, and a Control Unit (CU), using a NIOS2 from ALTERA [27] (a 32 bit VHDL-embedded microprocessor). The ALU (Figures 8 and 11) has tree sub-systems:  
Figure 11: ALU–CU-interface PC data flux sketch. Figure 10: Controlled Noise Source (CNS) block diagram. The core of this sub-system is the noise source Noise Com NC346. 1. Correlation radiometer: Once the beam signals are synthesized (one for the upper channel and another for the lower channel), the correlation radiometer part performs the complex correlation between them. The main tasks of this part are the digital down-conversion to the baseband, low-pass filtering the signals, synthesis of the beams, and correlation of the resulting signals to obtain the Stokes parameters. 2. Numerically Controlled Oscillators (NCOs): Is a set of oscillators, each channel has its own NCO. In this case, every individual oscillator consists of a simple look-up table accessed periodically and sequentially. The CU updates the NCO's amplitude and phase according to the requirements. This is a crucial sub-system of the design, since the NCO set not only down-converts the signals to baseband, but it also calibrates the amplitude and phase errors, and steers the beam for all the receivers. The content of each _RUT_ look-up table is: \[\mathit{Table}_{\mathit{RUT}}\mathit{(n)}\mathit{=}\mathit{(A}_{\mathit{Broom }}\cdot\mathit{A}_{\mathit{cal-RUT}}\cdot\mathit{A}_{\mathit{ph}}\ )\cos\bigl{(}2\ \ \cdot\Omega\cdot\ \ +\ \phi_{\mathit{Baum}}-\phi_{\mathit{cal-RUT}}\ -\phi_{\mathit{ph}}\ \ \bigr{)},\] (10) where: \(\bullet\)\(\Omega\) and \(n\) are the digital frequency and digital time, respectively; \(\bullet\)\(A_{\mathit{Beam}}\) and \(\phi_{\mathit{Beam}}\) are the modulus and phase of the complex weight, assigned to each _RUT_ to steer the beam in the desired direction; \(\bullet\)\(A_{\mathit{Cal-RUT}}\) and \(\phi_{\mathit{Cal-RUT}}\) are the modulus and phase of the complex weight, assigned to each _RUT_ for hardware calibration purposes; and \(\bullet\)\(A_{\mathit{ph}}\) and \(\phi_{\mathit{ph}}\) are the modulus and phase of the complex weight, to correct any antenna error such as center phase or different path lengths. These values were measured at the UPC anechoic chamber and set as a system parameter. The sub-system for the hardware error estimation (Figure 12) is an 8 bit complex correlator. Figure 12: Error estimation and calibration sketch. The ALU performs the complex correlation and the error correction (NCO) and the CU performs the error retrieval and the 1D- Kalman filter. The complex correlation between a reference receiver signal and a _RUT_ signal gives information about the amplitude and phase differences among them (Equations (5,6)). Thereafter, this value is sent to the CU, where the mathematical algorithms to derive the amplitude and the phase differences are implemented. This information is used to update the corresponding _RUT_'s NCO look-up table.
## 3 The Proposed Method
### PolSAR Data Pre-Processing
#### 3.1.3 Patching the Images with Fixed Size  
It is required that the CNN processes the data with a fixed size. However, different targets usually have different sizes, so it is difficult to use a generalized size for all target slices. Thus, some researchers propose to process the patches via stretching or filling the bounding of the image with 0 pixels. Although these methods can solve this problem to a certain extent, they will bring some unexpected errors. For example, if the boundary of small objects is filled with 0, the detection accuracy of the targets in complex environments will be limited, which thereby influences its feature learning. The details of our patching method are as follows.  
First, based on the sliding window with a fixed size, we traverse the entire dataset to obtain a set of fixed size slices for each channel of the 6 Ch and Pauli RGB image, which is shown in Figure 3.  
By this way, the maximum number of slices for each channel can be obtained by:  
\[n=(w-s+i)(h-s+i)/i^{2} \tag{10}\]  
where \(w\) and \(h\) are the width and height of each channel data respectively, \(s\) is the size of the sliding window, and \(i\) is the span while sliding. It should be noted that \(s\) and \(i\) are relevant to the size of the target. If the target is small (e.g., several pixels), then \(i\) is minor and \(s\) should be carefully selected. The selection of both parameters will be detailed in the experiment section.  
Second, we assign a label for each slice. Specifically, each pixel in ground-truth is assigned a label per the category it belongs to, and then we choose the location of the center pixel of the slice as the index to search the category label in the ground-truth. The selected label is finally assigned for the slice.  
In this manner, all samples are obtained with labels attached, and we can ensure that the CNN can process the small target in the complex environment.  
Using a sliding window can get enough training sets, but the samples will be more or less repeated. Therefore, before feeding the data into the CNN, we need to reduce the data redundancy by principle component analysis (PCA).
## 3 Materials and Methods
### Neural Network Architectures for Emotion Analysis
#### 3.2.1 Emotion Estimation from Video  
In order to obtain the impression created on the volunteers by the GS utterances, two different neural network architectures, VGG-16 and ResNet18 (inspired by the architecture proposed in [48]), were used to estimate the volunteers' valence and arousal, respectively, from the videos of their facial expressions. This hybrid system was chosen because VGG-16 performed better than ResNet18 for valence, while ResNet18 performed better for arousal. Both networks were trained on the AffectNet data set [49]. This method of engagement and preference estimation was chosen because it is not an invasive method, does not require very expensive additional hardware for volunteers (given most laptop computers, tablet computers and smartphones have front cameras nowadays), and it does not pause the experiment, allowing one to capture the immediate emotional change caused by the speech sound. The decision for capturing the immediate reaction stems from previous research findings that the candid reaction of research subjects differs substantially from their opinion after being given some time to think and rationalize their own feelings and opinions about an experiment [50; 51]. However, since it is also important to know the attitude of volunteers towards [PERSON]'s gibberish speech, towards [PERSON] and the experiment itself after having some time to think, this approach is coupled with the Likert scale questionnaire proposed in Section 3.1.2.  
However, since the aforementioned neural networks estimate human emotions from still images, and the collected data consist of video samples, it was necessary to choose a metric capable of representing the impact of the gibberish speech on the listener. Thus, it is necessary to obtain the initial emotional state \(E_{t}\) of the subject and the emotional state \(E_{t+1}\) after listening to the utterance.  
The chosen metric is then the difference between the emotion estimation from the initial (just before [PERSON] starts speaking) and the last frames of each video sample. This metric is called \(\overrightarrow{\delta_{E}}=(\delta_{v},\delta_{a})\), where \(\delta_{v},\delta_{a}\) is the change in displayed valence and arousal.  
It is possible to see how a few utterances (randomly selected from the data set) have affected the subjects in the valence-arousal space shown in Figure 3.