esa-sceva/satcom-chunk-collection huggingface dataset:
# Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study
## 5 Materials and Methods  
### Participants  
This study involved 34 young adults with ADHD, diagnosed by the public or private health service of Italy with different modalities and questionnaires (mean age in years = 25.75 (3.96), 16 males and 18 females), and 35 young adults without ADHD (mean age in years = 24.72 (3.39), 18 males and 17 females). All young adults involved had intelligence within the normal range (with ADHD: IQ > 85 at the WISC-IV [80], with M = 109.82 and DS = 12.93 [54]; without ADHD: z score > -1 SD at the SPM [81]). The two groups were comparable in terms of age (t= \(-\)1.15, \(p\) > 0.05) and gender (\(\chi^{2}\) = 0.13, \(p\) > 0.05). This study involved a sample of young adults (aged between 20 and 30 years) to ensure greater homogeneity in their cognitive development levels. This age group represents a stage in which cognitive abilities are fully developed, but not yet affected by age-related decline. Moreover, individuals in this age range are generally more proficient in the use of digital technologies, a relevant factor in the context of the present research, which is based on tele-assessment tools.  
No adults with ADHD had a comorbidity with a Specific Learning Disorder (SLD) or autism spectrum disorder (ASD). Nineteen of them had received treatment: thirteen received psychological therapy and six received both psychological and medication therapy, not for ADHD symptoms, but for emotional problems. In both groups, there were young adults with some symptoms of internalized disorder, but none of them had a psychiatric diagnosis. The exclusion criteria were comorbidities with neurodevelopmental, behavioral, or psychiatric disorders and an IQ < 85.
## 2 Materials and Methods
### Rotating Polarization-MPSK (RP-MPSK) Modulation
#### 2.1.4 Combining  
The previous works on RPW used selection combining (SC). We suggest using Equal Gain Combining (EGC) since it performs better than SC with constant-power envelope (CPE) modulations like MPSK. Both are presented here:  
\[\text{SC}:\ r_{p}:=\begin{cases}h_{p}&\left|h_{p}\right|^{2}\geq\left|v_{p} \right|^{2}\\ v_{p}&\text{otherwise}\end{cases} \tag{7}\]  
\[\text{EGC}:\ r_{p}:=\frac{1}{2}\big{(}h_{p}+v_{p}\big{)} \tag{8}\]  
Here, equal noise power spectral density is assumed for horizontal and vertical polarized signals in (8). The signals are co-phased before EGC. The structure of coherent RP-MPSK receiver with EGC is shown in Figure 4.  
Maximum likelihood detection (MLD) is performed on each \(r_{p}\) resulting in \(N_{p}\) replicas of the transmitted symbol \(s_{m}\), denoted by \(\hat{s}_{m,p}\):  
\[\hat{s}_{m,p}\triangleq\operatorname*{argmax}_{m}\Re\{r_{p}\cdot s_{m}^{*}\} \quad p\in\left\{0,1,2,\ldots,N_{p}-1\right\} \tag{9}\]  
Decision on the symbol estimate \(\hat{s}_{m}\) is made in favour of \(\hat{s}_{m,p}\) that has most frequently occurred:  
\[\hat{s}_{m}\triangleq\operatorname*{mode}\left(\left\{\hat{s}_{m,p}\right\}\right) \tag{10}\]
## 2 Materials and Methods
### Barometric Pressure Variations in Sao Paulo State, Brazil  
The barometric chamber was designed to reproduce small barometric pressure variations associated with weather changes in daytime periods. Therefore, before designing the barometric chamber and its control system, weather data from Sao Paulo state were analyzed to define the operation limits of the chamber.  
Figure 1 summarizes five years of barometric pressure data for some cities in Sao Paulo state, Brazil, where hourly barometric pressure data were available. Data were obtained from the Brazilian National Institute of Meteorology (INMET) database and from the weather station available at ESALQ/USP.  
Figure 2 shows barometric pressure data gathered every hour for Piracicaba (Sao Paulo state, Brazil) for five years (weather station at ESALQ/USP).  
Based on the deviations from the average (Figures 1 and 2), barometric pressure variations in Sao Paulo state did not exceed 13.0 hPa and, consequently, variations higher than that are not expected to occur in Sao Paulo state unless extreme weather events, such as hurricanes, occur. As a matter of curiosity concerning extreme weather events, Ref. [7] reports that barometric pressure may drop by 60 hPa from the outer edge of a hurricane to its center.  
Figure 1: Minimum, average, maximum, and deviations from the average values of barometric pressure (hPa) in cities of São Paulo state, Brazil, from January 2017 to December 2021 (**(a)**: location of São Paulo state in Brazil; **(b)**: location of the cities where barometric pressure data were analyzed; **(c)**: data of barometric pressure).  
Variations in barometric pressure are expected to occur very slowly in nature except in the presence of extreme weather events [5,7]. Based on the historical dataset analyzed, we determined that the barometric chamber must allow the barometric pressure to be changed by up to \(\pm\)15 hPa with respect to the current barometric pressure. In addition, since variations in barometric pressure occur very slowly in nature, the control system must make it possible to set the rise/fall time corresponding to the duration in which a change of pressure will be conditioned.
## 2 Materials and Methods
### Motion Compensation
#### 2.2.2 Attitude Compensation
Progressively, the attitude matrix of theA-system relative to the F-system is derived from the azimuth and elevation of the antenna beam, where the elements of the matrix are functions of azimuth and elevation angles. In this way, the attitude matrix of the A-system relative to the F-system is obtained. Finally, the antenna beam's actual azimuth angle and elevation angle are obtained by comparing the azimuth and elevation of the two adjacent moments. The advantages of the above attitude compensation algorithm are, on the one hand, the attitude and orientation data of the vehicle platform observed by the CNS are input to the signal processing system in real time; on the other hand, the algorithm as an algorithm module can be integrated into the signal processing system without the influence of any mechanical equipment, which is more efficient and stable compared to the adjustment platform of the antenna.
## 3 System and Threat Model
### Threat Model  
In thermal attacks, a malicious operation leaks the data and secret information by modulating the devices' temperature [39]. Except for a few studies, as reported in [36], which estimated the decay rate of the DRAM cells to determine the temperature of a TCC, most of the thermal covert channels need to have access to on-chip digital thermal sensors (DTS) to get the temperature information of the chip [33]. For dynamic thermal management, temperature sensors are essential and are deployed in chips in a wide range [40]. The accuracy and number of temperature sensors can be ever improved in the future [41]. The attackers (being the TCC transceiver) are able to read or reach the local thermal sensor data by calling the software interfaces such as MSR or Intel's processor Model Specific Register, with a normal resolution of 1 \({}^{\circ}\)C [42]. The accuracy of some of the latest digital thermal sensors can be up to 0.1 \({}^{\circ}\)C [43]. The defender is able to read the temperatures of all cores and distribute the detection and blocking programmes to each core [13].  
Our threat model for TCC have the following assumptions:  
* The attackers have direct access to the target device or systems in order to record and archive thermal traces of the executed decryption. Many key recoveries attacks use a common set of methods or techniques for analysing thermal traces. Simple thermal analysis (STA) includes directly inspecting traces to deduce sensitive data or information when measurements can be recorded to certain data properties [32];
* The attackers can acquire the ciphertext. In the transmission process, the ciphertext data will be employed as default storage parameters for the specific transaction. So, during this process, it is most likely the attackers can access the ciphertext [44];
* The attackers have access to a similar system or device. For example, using off-the-shelf parts such as AVR, ARM, etc;
* The attackers have the capability to slow and cool down the target operating systems. This assumption can be reached by manipulating the external crystal, using some external fan, or by forcing the target system to compute a wide range of tasks in parallel [32; 3];
* Since the signal on the transmitter side is already distorted by adding noise, it would not be possible to detect it correctly at the receiver side by increasing the frequency of thermal sensors, because the information received would be different from the original information. Further, for proper synchronization of data, it is better for both transmitter and receiver thermal sensors to operate at the same frequency. The transmitter and the receiver also need to reach an agreement on the frequency of transmission for the thermal covert channel [3].
# Type Hierarchy Enhanced Event Detection without Triggers
## 2 Related Work
### Event Detection with Triggers  
Event detection has been used as a fundamental core technology in NLP. Early approaches are feature-based. Researchers construct rules manually or automatically and then use them as templates to guide event detection [19; 20; 21; 22]. However, rules and features greatly affect the overall performance of the ED model and require a high level of human resources and expertise. With the gradual improvement of deep learning theory and technology in recent years, more and more approaches use deep learning methods to complete ED. Convolutional neural networks capture semantic associations between consecutive words by convolutional operations. [PERSON] et al. [8] proposed a dynamic multipooling convolutional neural network, which uses a dynamic multipooling layer based on event triggers and arguments, to keep more crucial information. [PERSON] et al. [23] used a joint framework with bidirectional recurrent neural networks to perform event extraction. The framework can learn hidden feature representations automatically from data according to the continuous and generalized representations of words. [PERSON] et al. [24] constructed a hybrid neural network to capture both sequence and chunk information from specific contexts.  
On the other hand, there are some approaches used to enhance semantic features during event detection. A hierarchical and supervised attention model is proposed by [PERSON] et al. [25], which pays word-level attention to triggers and sentence-level attention to sentences containing events. Moreover, the use of ontology in various fields is increasing [26; 27; 28; 29], and some studies obtain satisfactory results with ontology. For example, [PERSON] et al. [29] formulate event detection as a process of event ontology population, which associates an event instance with a predefined event type. Most recently, [PERSON] et al. [9] proposed a framework that uses event types as queries to extract candidate trigger words. Nevertheless, this framework formulates the query based on its event type name and the top K of prototype triggers.  
A common problem with the above approaches is that they rely too heavily on the annotated triggers. However, annotating is usually resource-intensive and may cause error propagation if incorrectly annotated. Unlike previous work, our framework accomplishes event detection without triggers.
## 2 Background: Existing Domain-Specific Modeling Language and Data Acquisition Domain
### Domain-Specific Modeling Language Sequencer  
This paper will concentrate on a specific part of the Dewesoft software package, the Sequencer language [27] and its descendant RT-Sequencer. It is used to define measurement procedures, more specifically, the measurement sequence of a tested product. The main idea of Sequencer, a DSML, was to shift programming (modeling) to end-users, e.g., domain experts who know the product under test, as well as measurement systems. However, these domain experts do not have to be knowledgeable in GPL programming.  
In Sequencer, visual blocks can be placed using the drag and drop method, and the domain experts select their corresponding properties (see Figure 2). The visual blocks, defined in a model, represent the actions being executed during the product testing. The model starts from the initial visual block (marked with a circle) and continues on to the following visual block, where the arrow points. Visual blocks can also contain local and global variables designed to store current values from measurement equipment (sensors, etc.). Because visual programming can become unmanageable with a large number of visual blocks, the modeling language also enables custom blocks, which allow us to combine visual blocks into a sub-model.  
Figure 2 shows the Sequencer's development environment. On the left side of the Figure 2, we observe the visual blocks of the language. In the middle is the working panel, where domain experts model the measurement procedure. On the right side of the screen we can find the properties that the domain experts can choose for the currently selected visual block. The sequence in Figure 2 shows a part of the program where a simple acceleration measurement is modeled. The model starts by storing the data file (block \"StartStoring\") for 60 s. After that, the measurement procedure checks if the acceleration is within the range of 1.5 g and 2.0 g (channel \"Acc1\"). The stored data file is renamed and analyzed if the condition is true. Otherwise, the file is deleted.  
Figure 2: The DEWESoft’s modeling environment.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 3 Results  
In this section, some simulations are given to verify the effectiveness of our proposed method. In the following simulations, we assume that all the nodes are equipped with Uniform Linear Array (ULA), as well as with \(M\) transmit elements and \(N\) receive elements that are separated by half a wavelength. Other specific simulation parameters are shown in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline node antennas & \(M\) \\ relay antennas & \(N\) \\ \(K\) & 3 \\ taps & 3 \\ Number of channels & \(M\times N\) \\ frequency & 2.4 GHz \\ FFT Length & 64 \\ modulation & 16 QAM \\ band width & 20 MHz \\ Roll off factor & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Parameters.  
Figure 3: Distribution change of equivalent channel spanned space of nodes. (**a**) Traditional SSA (\(N<2M\)), (**b**) SSA (\(N\geq 2M\)), (**c**) SSA after space compression (\(N\geq 2M\)).
# Detail-Preserving Shape Unfolding
## 2 Related Work
### Shape Unfolding with Detail Preservation  
To preserve more details during shape unfolding, some researchers try to obtain feature-preserving canonical forms. [PERSON] et al. [3] first calculated the standard MDS canonical form for each model. Then the original model was segmented into individual parts by using random walk [21]. Finally, these parts were assembled according to the resulting MDS canonical form. The performance of their method, however, largely depends on the segmentation's accuracy. Unlike the method proposed by [PERSON] et al. [3], [PERSON] [33] utilized volumetric shape deformation technique based on landmarks MDS [15] to preserve initial geometry details. [PERSON] et al. [28] used the canonical form of mesh skeleton as guidance. They first extracted the curve skeleton [34] from a given model. Then curve skeleton was deformed into a canonical form based on the standard MDS algorithm. Finally, the skeleton driven shape deformation method [20] was used to generate the canonical pose. Nevertheless, the performance of their method depends on the accuracy of skeleton extraction to some extent. In addition, the tedious operation process mentioned above may limit its scope of application in practice.  
Some other researchers try to directly obtain the canonical form with details. [PERSON] et al. [35] calculated the canonical form of a shape by stretching out its limbs. They maximised the Euclidean distances between feature points on the extremities of the mesh while preserving the original edge lengths. [PERSON] and [PERSON] [24] solved this problem by a mass-spring system. This method tried to move each vertex as far away from each other as possible while maintaining the length of finite element. Interior-point method [36] is used to solve their model which is composed of a non-linear objective function and hard volume constrains. In [25], [PERSON] et al. proposed an automatic mesh unfolding method which is solved by semidefinite programming. They first evaluated an approximate pairwise Euclidean distance matrix with unfolding property by maximizing the total variance of the vertex set. Then, the standard MDS or least square MDS [11] was implemented to obtain the final canonical form. However, the above methods only focus on how to preserve edge lengths, but ignore the edge directions of the mesh. In addition, they use sophisticated non-linear optimization algorithm [24; 25] which highly affects the applications of their methods. In contrast, our approach imposes constraints on both the edge lengths and directions. Furthermore, the proposed method only needs to solve linear systems and parallel implementation further promotes its practicality.
## 3 Results and Discussion
### Selection of Nine Best Performing ANN Models Based on R\({}^{2}\), MAE and RMSE  
From 36 models of ANN, nine models were selected based on nine different input combinations as mentioned in Table 2. One best model was selected from every input combination. ANN1-ANN4 came under the LW combination of inputs. ANN2 showed the highest R\({}^{2}\) value (0.96) and lowest MAE (0.86) and RMSE (1.13 cm\({}^{2}\)) in the testing phase. ANN5-ANN8 came under the LW\({}^{2}\) combination of inputs. ANN6 showed highest R\({}^{2}\) value (0.96) and lowest MAE (0.84) and RMSE (1.18 cm\({}^{2}\)) in the testing phase. ANN9-ANN12 came under LW\({}^{3}\) combination of inputs. ANN10 showed highest R\({}^{2}\) value (0.97) and lowest MAE (0.87) and RMSE (1.18 cm\({}^{2}\)) in the testing phase. ANN13-ANN16 came under L\({}^{2}\)W combination of inputs. ANN15 showed highest R\({}^{2}\) value (0.97) and lowest MAE (0.86) and RMSE (1.11 cm\({}^{2}\)) in the testing phase. ANN17-ANN20 came under the L\({}^{2}\)W\({}^{2}\) combination of inputs. ANN18 shows the highest R\({}^{2}\) value (0.96) and lowest MAE (0.77) and RMSE (1.11 cm\({}^{2}\)) in the testing phase. ANN21-ANN24 came under the L\({}^{2}\)W\({}^{3}\) combination of inputs. ANN22 showed the highest R\({}^{2}\) value (0.97) and lowest MAE (0.86) and RMSE (1.19 cm\({}^{2}\)) in the testing phase. ANN25-ANN28 came under the L\({}^{2}\)W combination of inputs. ANN26 showed the highest R\({}^{2}\) value (0.88) and lowest MAE (1.35) and RMSE (2.06 cm\({}^{2}\)) in the testing phase. ANN29-ANN32 came under the L\({}^{3}\)W\({}^{2}\) combination of inputs. ANN31 showed the highest R\({}^{2}\) value (0.94) and lowest MAE (1.01) and RMSE (1.47 cm\({}^{2}\)) in the testing phase. ANN33-ANN36 came under the L\({}^{3}\)W\({}^{2}\) combination of inputs. ANN35 showed the highest R\({}^{2}\) value (0.96) and lowest MAE (0.97) and RMSE (1.31 cm\({}^{2}\)) in the testing phase. Finally, the selected nine best performing ANN models which were to be compared with the other three models viz., MLR, SVR, and GBR includes ANN2, ANN6, ANN10, ANN15, ANN18, ANN22, ANN26, ANN31, and ANN35. Table 6 enumerates the performance metrics (R\({}^{2}\), MAE, and RMSE) of 36 ANN models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{8}{c}{**Input Combinations**} \\ \cline{2-10}  & **L, W** & **L, W\({}^{2}\)** & **L, W\({}^{3}\)** & **L\({}^{2}\), W** & **L\({}^{2}\), W\({}^{2}\)** & **L\({}^{2}\), W\({}^{3}\)** & **L\({}^{3}\), W** & **L\({}^{3}\), W\({}^{2}\)** & **L\({}^{3}\), W\({}^{3}\)** \\ \hline Variance & & & & & &
## 2 Methods
### Theory Analysis of Combiner Branch
#### 2.1.3 Insertion Loss Analysis  
In the working frequency range, the microstrip line loss is the sum of the conductor loss and the dielectric loss. The conductor loss is governed by the transmission line geometry (length/width), while the dielectric loss depends on substrate properties, which can be expressed as follows [16; 17]:  
\[\text{Totalloss}=8.686(\alpha_{c}+\alpha_{d})\cdot l=8.686(\frac{R_{s}}{Z_{0} \cdot\omega}+\frac{\pi f\varepsilon_{r}\tan\delta}{c\sqrt{\varepsilon_{eff}}})\cdot l \tag{10}\]  
where \(\varepsilon_{r}\) is the relative dielectric constant; tan\(\delta\) is the loss tangent (frequency-dependent); C is the speed of light; \(\varepsilon_{eff}\) has the same definition as Equation (9); Rs is the surface resistance, determined according to the vacuum magnetic permeability (\(\mu_{0}\)) and the conductivity of the metal (\(\sigma\)); and \(Z_{0}\) is the characteristic impedance of the microstrip line.  
To achieve high-power, low-loss performance, the design of a combiner that reduces the line length, increases the line width, and utilizes a substrate material with lower tan\(\delta\) is critical in Equation (10). The design detailed in the model in Figure 1 was achieved via Equations (5) and (10), assuming that the microstrip substrate material was FR4(\(\varepsilon=4.4\), tan\(\delta=0.016\)), its thickness was h, the copper thickness was 35 um, and the microstrip width was w. As a result, the conductor losses could be reduced by utilizing parallel capacitors, widening the line width, and shortening the line length. With \(Z_{s}=50\), the parameters \(\beta\mathbb{I}_{1}\), \(\beta\mathbb{I}_{2}\), \(Z_{0}\), Z\({}_{l}\), w, h, and C could be calculated, as summarized in Table 1. Increasing the shunt capacitance reduced the total microstrip electrical length (e.g., the total angle from 90\({}^{\circ}\) for \(\lambda/4\) lines in Group 1 to 60-75\({}^{\circ}\) in Groups 2-5). Lowering \(Z_{0}\) (e.g., 70.7 \(\Omega\to 49\)\(\Omega\)) widened the line width, improving power handling. Concurrently, shorter microstrip lengths minimized conductor losses, enabling synergistic low-loss and high-power performance.