esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Abstract Concept Modeling with RANs
### Step 4: Upwards Activation Propagation (UAP) Process
#### 4.5.2 Similarity Translation Function (STF)--Stage 2  
In the second phase the calculated normalized distance is transformed to obtain a similarity relation such that following requirements are fulfilled:  
* \(f(d=0)=1\), i.e., when distance is 0 similarity is 100%.
* \(f(d=1)=0\) i.e., when distance is 1 similarity is 0%.
* \(f(d=x)\) is continuous, monotonous, and differentiable in the \([0,1]\) interval.  
In RANs modeling Equation (3) is used as the Similarity Translation Function to determine the similarity relation of the previously calculated distance. The non-linearity of STF is depicted in Figure 5, indicating that the similarity value reduces drastically when the normalized Euclidean distance is larger than 0.05 (or 5% dissimilar).  
The first three steps generate the RANs model (see Figure 4), later, in the fourth step, this model is used via UAP operation by propagating the input activation (\(a_{i}\)) upward and obtaining activation (\(A_{j}\)) at convex abstract concept layer (inspired by the theory of spreading activation see Section 3.3). Algorithm 1 describes the Upward Activation Propagation operation, showing how the inputs and interlayer learning weights \(W\) are used to calculate similarity relation to generating output activation at each abstract concept representative nodes. The activation \(A_{j}\) in newly created nodes N\({}_{j}\) also indicates the degree of confidence (DoC) of the identification of a class by its representative node in the new layer (for a given input data instance). For instance, in Figure 4, Step-2, at Layer-0 input vector is [0.1, 0.21] it signifies that the dimensions S\({}_{1}\) and S\({}_{2}\) has activation 0.1 and 0.21 respectively. For the, aforementioned, input vector, a [0.13, 0.32, 0.89, 0.22, 0.01] vector of activation is observed at all nodes (N\({}_{1}\),..., N\({}_{5}\)) respectively, at Layer-1. The observed activation vector itself describes that the input data belongs to Class-3 (Versicolor) with a DoC of 89%.
## 2 Problem Formulation
### System Model and Fault Model  
Consider a class of the nonlinear cascade systems [24; 25] encountering mismatched uncertainties and actuator faults [26; 27], and it can be depicted by  
\[\dot{x}_{1}(t) = f_{1}(x_{1}(t))+g_{1}(x_{1}(t))x_{2}(t)+\triangle f(x_{1}(t)), \tag{1}\] \[\dot{x}_{2}(t) = f_{2}(x(t))+g_{2}(x(t))(u(t)+f_{a}), \tag{2}\]where \(x_{1}(t)\in\mathbb{R}^{n_{1}},x_{2}(t)\in\mathbb{R}^{n_{2}}\) are system states, \(u(t)\in\mathbb{R}^{m}\) is the control input, and \(f_{i}(\cdot)\) and \(g_{i}(\cdot)\) (\(i=1,2\)) are differentiable in their arguments with \(f_{i}(0)=0\) (\(i=1,2\)), \(\triangle f(x_{1})\) is the unknown perturbation with \(\triangle f(0)=0\), \(f_{a}\) is the actuator fault vector.  
For ease of the description and the following RADP design, nominal systems of above systems (1) and (2) are given as  
\[\dot{x}_{1}(t) =f_{1}(x_{1}(t))+g_{1}(x_{1}(t))x_{2}(t), \tag{3}\] \[\dot{x}_{2}(t) =f_{2}(x(t))+g_{2}(x(t))u(t). \tag{4}\]  
In order to simplify the expression, some function arguments are omitted in the description below. Then, for convenience of later analysis, the following assumptions and definition are given.  
**Assumption 1**.: _It is assumed that the control input matrix \(g_{1}(x_{1})\) is bounded i.e., \(\parallel g_{1}(x_{1})\parallel\leq\lambda_{g}\), where \(\lambda_{g}\) is a positive constant. The \(g_{2}(\cdot)\) is a full row rank matrix and the function \(g_{2}^{+}(\cdot)\) is defined as the Moore-Penrose pseudo-inverse of \(g_{2}(\cdot)\), i.e., \(g_{2}^{+}(\cdot)=g_{2}^{T}(\cdot)(g_{2}(\cdot)g_{2}^{T}(\cdot))^{-1}\)._  
**Assumption 2**.: _It is assumed that the uncertainty is bounded by a function \(\lambda_{f}(x_{1})\), i.e., \(\parallel\triangle f(x_{1})\parallel\leq\lambda_{f}(x_{1})\) with \(\lambda_{f}(0)=0\), and \(f_{a}\) is the unknown constant bias actuator fault vector._  
**Definition 1** ([7]).: _The equilibrium point denoted \(x=0\) of a dynamical system is said to be uniformly ultimately bounded (UIUB) if there exists a compact set \(\Omega\subset R^{n}\) so that for all state variable initial value \(x_{0}\in\Omega\) there exists a bound \(B\) and a time \(T(B,x_{0})\) such that \(\parallel x-x_{0}\parallel\leq B\) for all \(t\geq t_{0}+T\)._
## 2 Methods
### The Proposed Method  
Based on the above discussions, a novel hybrid model which combines the TVF-EMD model and LSSVM model can be developed to improve the forecasting accuracy. First, the TVF-EMD method is presented to deal with the non-stationary and nonlinear traffic volume series. After that, multiple subsequences called narrow-band signals are obtained. Then, the LSSVM model is established for each subsequence. Finally, the prediction results of the subsequences are accumulated to generate the lasted forecasting results. The specific process of TVF-EMD-LSSVM model is shown in Figure 1, and the steps are shown as follows:  
**Step 1**: Preprocess the original traffic volume data with the errors data and missing data to get the experimental data;  
**Step 2**: Decompose the data into several subsequences \(\left(\left\{c_{j}(1),\ldots,c_{j}(k)\right\},j=1\ldots M+1\right)\) by TVF-EMD algorithm;  
**Step 3**: Divide each subseries into two parts, including training parts \(\left\{x^{\prime}(1),\ldots,x^{\prime}(k)\right\}\) and test parts \(\left\{x^{\prime}(k+1),\ldots,x^{\prime}(k+N)\right\}\);  
**Step 4**: Establish the LSSVM model to predict the \(k+1\)th data \(\hat{c}_{j}(k+1)\) of subsequences, and sum up to get the forecasting value \(\hat{x}(k+1)\);  
**Step 5**: After updating the training set data to \(\left\{x^{\prime}(2),\ldots,x^{\prime}(k+1)\right\}\), repeat step 2 to step 4 to obtain the prediction results. Continue to predict one step ahead until the prediction task is completed.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 6 Application to a Drone Cloud with Ring Topology
### Communication Infrastructure  
In a ring topology, each drone has two communication links, one to the preceding drone and another to the following one. This setup ensures redundancy and can facilitate continuous data flow even if one link fails. The direct data links between drones make efficient use of the available bandwidth and minimize latency.  
Each data link operates independently with its own frequency hopping sequence. This configuration helps in avoiding interference between links, and improves the overall resilience of the network to jamming and interception attempts.
## 2 Main Dynamic System  
### Footbridge with Precise Dynamic Sensitivity  
A reference footbridge (RF) with precise dynamic sensitivity in its fundamental frequency content, close to the human gait frequency (lateral and vertical frequency of 1.0 Hz and 2.0 Hz, respectively), was designed and proposed as an analytical reference structure. This structure employed structural steel with \(f_{y}=210.0\) GPa and \(\rho_{g}=7500.0\) kg\(\cdot\)m\({}^{-3}\) and an equivalent cross-section, with an effective area \(A_{g}=533.40\) cm\({}^{2}\) and inertial moment (\(I\)) specified for each direction in Table 1. The dynamic properties of the RF were evaluated using two methods of analysis: numerical evaluation and a finite element (FE) model. Both dynamic assessments used the same cross-sectional properties, physical properties of the materials, and structural damping of 1.0% for all modes. The numerical evaluation used Equation (1) to calculate the natural frequencies of the RF based on their properties, where \(E=f_{y}\), \(I^{\text{(*)}}\), \(m_{l}=400.05\) kg\(\cdot\)m\({}^{-1}\), and \(l=36\) m represent the elastic modulus, inertial module (specified in Table 1 for each analysis direction), mass per unit length, and length of the structure, respectively. In addition, simple support conditions were considered. Similarly, the FE analysis modeled the dynamic behavior of the RF using SAP2000 V.25.3.0. The natural frequencies of the RF are reported in Table 1 for both analysis cases, which generated a maximum frequency error of less than 2.04% in all directions assessed.  
\[f_{n,l}=(n\times\pi)^{2}\cdot\sqrt{\frac{f_{y}\cdot I}{m_{l}\cdot I^{4}}} \tag{1}\]  
In addition, the first three dynamic modal shapes were determined by the FE model analysis in each assessment direction, as shown in Figure 1. These principal modal shapes reached a cumulative modal mass of more than 95.0%, developing a representative dynamic performance of the RF in all analysis directions. The dynamic behavior of the RF is closely related to the dynamic performance of the human gait, which positioned this structure with a precise dynamic sensitivity to the influence of human-induced gait loads as an international benchmark to evaluate HSI dynamic effects.
## 3 Results and Discussion
### Thermal Analysis  
Figure 6 shows the TGA and DTG plots of hirudin, CS/PCL MPs, and rH/CS/PCL MPs3, which were used to analyze the thermal stability of the bowl-shaped particulate scaffolds. The TGA curves in Figure 5a show that the thermal degradation behavior of hirudin, CS/PCL MPs, and rH/CS/PCL MPs3 increased from room temperature to 200 \({}^{\circ}\)C, and the weight loss at this stage was mainly due to the evaporation of free and bound water in the scaffolds, as well as dehydration of the macromolecular groups; then, the thermogravimetric curves reached a relatively flat state, and it can be seen that there was no significant weight loss in the three groups of samples in this temperature interval from the TGA curves. The TGA curves showed that there was no significant weight loss in this temperature range. The rapid weight loss of hirudin was 74% at 200-318 \({}^{\circ}\)C. CS/PCL MPs and rH/CS/PCL MPs3 had a small peak at about 260-300 \({}^{\circ}\)C, and the rate of weight loss was probably due to pyrolysis of some of the hirudin and chitosan in the particles. CS/PCL MPs and rH/CS/PCL MPs3 shared a common major loss step between about 350 \({}^{\circ}\)C and 450 \({}^{\circ}\)C [41]. When the temperature exceeded 415 \({}^{\circ}\)C, the cup-shaped particles began to carbonise and the rate of weight loss decreased, although the weight of the particles continued to decrease. However, when the temperature reached 600 \({}^{\circ}\)C, the weight loss of the particles was no longer significant, the pyrolysis rate was reduced to a minimum and the remaining mass was about 11%.
# Novel Resistive Sensor Design Utilizing the Geometric Freedom of Additive Manufacturing
## 2 Materials and Methods
### Experimental Set-Up  
Mechanical forces were applied using a uniaxial material test machine (Zwick Z0.5, Zwick GmbH & Co., Ulm, Germany). Force was measured using a 500 N Xforce P load cell (accuracy class 0.5). A custom actuator was used to compress the parts without touching the electrical contact points. The sensor was placed on a flat surface with double-sided tape to prevent unwanted slipping. To enable electrical measurements with low contact resistance, copper wires were attached to both ends of the conductive plastic using MG Chemicals 8331-14G silver epoxy adhesive. The electric signal was passed to an Arduino via a voltage divider and ADC 1115 (Adafruit Industries, LLC, New York City, NY, USA), allowing the logging of a voltage measurement from which resistance was easily derived. Figure 2a,b show the test set-up, and Figure 2c shows a close-up of a sensor with substructure variant 2.  
During measurement, sensor behavior was determined while compressing each sample by 2.0 mm, then retracting and waiting for 2 s, and repeating this five times. Compression and retraction cycles were performed at constant velocities of 5, 10, and 20 mm/min for each sample.
## 3 CubeSats and Inter-Satellite Links (ISLs)
### Future OISL Payloads
#### 3.5.1 Key Operational Features of ALIGN  
Figure 4 depicts the concept of operation for the link acquisition process of ALIGN in LEO. The PAT process is highly challenging when considering two moving satellites with the relatively large uncertainties in real time, which progressively worsen due to minor variations in atmospheric drag. The uncertainties are represented by yellow circles along the orbital path (black dash line) with respect to transmitter Tx and receiver Rx satellite. As shown in Figure 4, the transmitter satellite must spiral the laser beam to search for the receiver and at the same time the receiver must be correctly orientated such that the transmitter is within the field of view (FoV) of the receiver's detector (green and blue cones).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Corporation**} & \multirow{2}{*}{**Payload Name**} & \multirow{2}{*}{
\begin{tabular}{c} **Max. Data** \\ **Rate (Gbps)** \\ \end{tabular} } & \multirow{2}{*}{**Link Type**} & \multirow{2}{*}{**Volume (cm\({}^{3}\))**} & \multicolumn{2}{c}{**Power**} & \multicolumn{2}{c}{**Release**} \\  & & & & & **Consumption** & **Mass (kg)** & **(O:Operational,** \\  & & & & **(W)** & & **D:Development)** \\ \hline TESAT & LCT 135 & 1.8 & GEO & 252,000 & -150 & -53 & O \\ \hline Mynaric & CONDOR Mk3 & 2.5 & LEO & 32,422.5 & - & - & O \\ \hline TESAT & SCOT-80 & 10 & LEO & 54,109 & 60–86 & -12.5 & O \\ \hline OneWeb [73] & Gen 2 Terminal & 20 & LEO & 3750 & 70 & 25 & D \\ \hline UKSA, Northumbria Uni. & FOCUS & \(>\)1 & LEO & 3 U & 40 & 3.6 & D \\ \hline NASA, MIT, Uni. of Florida & CLICK B/C & \(>\)0.02 & LEO & 1.3 U & - & - & D \\ TESAT [12] & SCOT20 & 2.5, 0.1 & LEO & 1135 (1U) & \(<\)35 & \(<\)1.6 & D \\ \hline \hline \end{tabular}
\end{table}
Table 6: Planned future payloads that provide laser communications capability in LEO, ordered by mass (kg) from high to low.  
The PAT system of ALIGN adopt spiral scans to ensure optimal coverage of search areas, which reduces the link acquisition time. Attitude Determination and Control System (ADCS) errors, which can cause pointing inaccuracies due to satellite movement, are mitigated by the system's adaptability, improving PAT precision. This adaptability is crucial for maintaining stable links between fast-moving satellites, even in environments with fluctuating signal-to-noise ratios or external interferences.
## 2 Materials and Methods
### Definition of the Target Variable  
The first step was to define the target variable, in this case a classification label representing the expected scenario in terms of vehicles to be dispatched by the EMS department for respiratory and infective issues. The reference time window for prediction was set to 7 days: the classification label is computed on this time horizon. Considering:  
Figure 1: Data relevant to the epidemic phases of COVID-19 in Lombardy region, Italy, from 24 February 2020 (begin of measurements) to 1 December 2021, filtered with a 7-day moving average. The daily variations in the number of total laboratory-confirmed cases, of people recovered, of casualties, and of currently positive cases (people currently resulting positive to swab test, corresponding to total cases—recovered—casualties) is reported.  
\(\overline{t}=\) day of computation \(w_{p}=\) week preceding \(\overline{t}\) (included) \(w_{f}=\) week following \(\overline{t}\) (excluded) \(i\widehat{m_{w}}\) median value of daily incidence (\(n/100\),\(000\) residents) in the number of ambulances dispatched in week \(w\) Five scenarios were arbitrarily defined, considering the distinction between periods with no active diffusion and periods of active epidemic spreading as defined by our previous probabilistic model [38]. Scenarios are thus defined as:  
* Scenario 1: NO DIFFUSION for the whole week \(w_{f}\) (only classes 1 or 2 occurring in the output from the probabilistic model [38])
* Scenario 2: NO DIFFUSION with oscillations expected during week \(w_{f}\) (a class 3 occurring in the output from the probabilistic model [38])
* Scenario 3: ACTIVE SPREADING WITH DECREASING CURVE: \(\widehat{im_{w_{f}}}\leq\widehat{im_{w_{p}}}-(\widehat{im_{w_{p}}}\times 0.15)\) (median incidence value decreasing by at least 15%)
* Scenario 4: ACTIVE SPREADING WITH STABLE CURVE: \(\widehat{im_{w_{f}}}-\widehat{im_{w_{p}}}\big{|}<(\widehat{im_{w_{p}}}\times 0.15)\) (absolute variation of median incidence value lower than 15%)
* Scenario 5: ACTIVE SPREADING WITH INCREASING CURVE: \(\widehat{im_{w_{f}}}\geq\widehat{im_{w_{p}}}+(\widehat{im_{w_{p}}}\times 0.15)\) (median incidence value increasing by at least 15%) The aim of the machine learning algorithm is to classify all records (relevant to one day in one of the territorial districts) with one of these 5 labels.
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 5 Discussion
### Contributions and Implications for BIM Applications  
This study investigates the error distribution patterns of MLS point clouds, focusing on the direction and consistency of error occurrence, which have not been adequately addressed in previous related studies. The proposed analysis method adopts an approach that aims to preserve the inherent error patterns in MLS point clouds, a consideration often overlooked in earlier research. As a result, the findings include not only the magnitude of errors but also their directional characteristics. Compared to previous works, this study provides a more in-depth analysis of MLS point clouds, with specific consideration of their relevance to Scan-to-BIM applications.  
The identified error pattern has the potential to enhance the geometric accuracy of as-built BIM generated from MLS point clouds. Previous studies on Scan-to-BIM and reconstruction have commonly adopted robust fitting methods such as RANSAC [32; 33; 34; 35; 36; 37; 38; 39]. However, these approaches are proposed to extract representative lines or planes from the given point cloud. Therefore, if the input point cloud contains a biased error pattern, the resulting geometric primitives are likely to be generated at biased positions. In this regard, the error pattern identified in this study can play a critical role in improving geometry reconstruction accuracy in MLS-based Scan-to-BIM. This potential improvement is supported by the results presented in Section 4.4.  
Our study contributes by identifying the internal error patterns inherent in MLS point clouds, a topic that previous studies have overlooked. The experimental results show that these error patterns are device-specific and may negatively affect the dimensional accuracy of components when MLS point clouds are used in BIM applications. These findings provide practical insights into the integration of MLS in BIM applications.
# Pixel Circuit Designs for Active Matrix Displays
## 2 TFT-LCD Pixel Circuits  
### TFT-LCD Typical Pixel Circuits  
A TFT-LCD typical circuit consists of one TFT and one capacitor (Figure 4); it was used in early TFT-LCD displays. When the TFT is turned on, the data signal is added to both ends of the LCD pixel through the drain and stored in the capacitor at the same time. The liquid crystal is distorted at a specific angle to form a greyscale display [1]. LCDs are voltage-driven devices. TFT-LCD has low requirements for TFT driving capability. Thus, mature processes and low-cost a-Si TFT are the main choices for low PPI TFT-LCD [55; 56]. However, the mobility of a-Si TFT is too low, and it is difficult to implement more versatile displays. In addition, displays using a-Si TFT consume more power. To meet the development of the display industry, including low power consumption, high frame rate, high contrast ratio, etc., the choice of TFTs shifted to high-mobility TFTs.
## 1 Introduction
To facilitate the recovery of white shark populations in Australasian waters and to further elucidate how white sharks use coastal as well as pelagic environments, additional empirical data and a more detailed examination of their complex movement behaviours is needed. Here, we integrated multiple complementary tag technologies and conventional fishery-dependent tag/recapture data (recapture location, time, and day) to characterize space use, temporal trends, and fine-scale vertical behaviour in relation to white sharkecology. We employed Pop-Up Satellite Archival Transmitting tags (PSATs), Smart Position or Temperature Transmitting (SPOT) tags, and acoustic tagging technologies to: (1) examine timing and extent of broad-scale horizontal movements; (2) identify regional and diel patterns in vertical movement patterns; and (3) determine intra- and inter-individual differences in fine-scale vertical behaviour. Here we provide an initial, description of this information-rich dataset.
## 7 Conclusions  
In this paper, we proposed a novel dual separate paths algorithm, called DSP, to provide fault-tolerant communication for WSNs. The proposed DSP algorithm is a fault-tolerant technique based on the path redundancy-based mechanism. The DSP algorithm establishes two node-disjoint paths with an optimal path distance between a source and a destination in a network, then forwards unicast frames through the pre-established dual paths. Unlike some existing path redundancy-based techniques which discover redundant paths based on exchanging control messages, the proposed DSP algorithm establishes the paths based on network topology information. In addition, the proposed algorithm delivers unicast frames from a source to a destination through two pre-established paths instead of all possible paths between the source and the destination, as is done in existing algorithms, thus improving network traffic performance. The DSP algorithm can be applied to WSNs to provide seamless fault-tolerant communication for life-critical applications, such as fault-tolerant CPSs. For our case study, the DSP algorithm was applied to HSR networks in order to provide fault-tolerant communication, as well as reducing redundant unicast traffic for the networks. The DSP-based approach significantly reduces unicast traffic compared with the standard HSR protocol. The analytical and simulation results showed that, for the sample network, the DSP-based approach reduced network traffic by 80% to 88% compared with the standard HSR protocol and by 59% to 80% compared with other existing traffic reduction techniques, including QR and PL techniques. Therefore, the DSP-based approach is a very effective solution for reducing redundant unicast traffic in HSR networks for mission-critical applications such as substation automation systems.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: 2016R1D1A1B03933315),and the INNOPOLIS Foundation funded with a grant in aid from the Korean government through MPEES Co., Ltd. and Myongji University (No. A201602 DD0101).  
Authors [PERSON], [PERSON], [PERSON], and [PERSON] conceived and developed the ideas behind the research. [PERSON] carried out the performance analysis and simulations, and wrote the paper under supervision of [PERSON]. [PERSON] and [PERSON] supervised the research and finalized the paper.  
The authors declare no conflict of interest.