esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Loitering Detection Methods
### The Ellipse Loitering  
An ellipse trajectory is defined as a trajectory contained within an ellipse. We use the following steps to determine whether the pedestrian's active area is in an ellipse area. Firstly, the Graham scan [51] method is used to find convex hulls for all track points in time \(T_{i}\), then ellipse fitting is performed. Figure 8 shows the process of finding a convex hull of a set of point.  
Figure 7: The rectangle trajectory and corresponding calculation.  
In order to fit the ellipse, we use the general elliptic equation in Equation (9).  
\[Ax^{2}+Bxy+Cy^{2}+Dx+Ey+F=0 \tag{9}\]  
We used some constraints [52] so that we can directly use the coordinates of the trajectories to do the least square. The coefficients of the equation can be obtained using Equation (10).  
\[f=\sum_{i=0}^{n}\left(Ax^{2}+Bxy+Cy^{2}+Dx+Ey+F\right)^{2} \tag{10}\]  
Each coefficient is determined by its minimum value. Equation (11) can be obtained by the principle of extreme value.  
\[\frac{\partial f}{\partial A}=\frac{\partial f}{\partial B}=\frac{\partial f} {\partial C}=\frac{\partial f}{\partial D}=\frac{\partial f}{\partial E}=0 \tag{11}\]  
Thus, we can get \(A,B,C,D,\) and \(E\) values, so that we can perform ellipse fitting on the given coordinates, calculate the optimal elliptic equation with least squares method. The detailed algorithm is described in the Algorithm 2.  
```
Input: the trajectory coordinates \(P(x,y)=\{(x_{1},y_{1}),(x_{2},y_{2}),\cdots(x_{n},y_{n})\}\) Output: loitering detection result
1. Extract the trajectory coordinates \(P_{i}(x_{i},y_{i})\).
2. when \(T_{i}>T_{0}\), find convex hull, input point set \(|Q|\geq 3\), \(P=(P_{1},P_{2},\cdots,P_{n})\); calculate the distance between \(P_{min}\) and \(P_{i}\).
3. Sort the clockwise direction through the polar angle \(\theta\) and distance \(D_{i}\), then cycle points; select six points in the calculate coordinates, \(T_{i-t}<T<T_{i}\); calculate parameters: \(f(A,B,C,D,E)=\sum_{i=0}^{n}\left(Ax^{2}+Bxy+Cy^{2}+Dx+Ey+F\right)^{2}\)
4. Record num-match and num-index, when \(num_{match}>num_{index}\), update ellipse parameter.
5. Calculate the size of activity area: \(S=\pi AC\), \(A>0\), \(C>0\), compare area, if \(S<S_{threshold}\), an ellipse loitering is detected.
```  
**Algorithm 2** Ellipse loitering detection
## 3 Results and Discussion
### Agronomic Quality of the Final Composts
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Composst 1** & **Composst 2** & **Composst 3** & **Composst 4** & **Average SD** \\ \hline EC (dS m\({}^{-1}\)) & 8.9 \(\pm\) 0.1 & 8.4 \(\pm\) 0.7 & 8.1 \(\pm\) 0.1 & 8.3 \(\pm\) 0.2 & 8.4 & 0.3 \\ TOC/TN ratio & 14.1 \(\pm\) 0.2 & 13.4 \(\pm\) 0.1 & 12.0 \(\pm\) 0.1 & 12.6 \(\pm\) 0.1 & 13.0 & 0.9 \\ TN (g kg\({}^{-1}\)) & 28.8 \(\pm\) 0.4 & 33.1 \(\pm\) 0.4 & 33.2 \(\pm\) 0.3 & 35.1 \(\pm\) 0.3 & 25.1 & 14.9 \\ P (g kg\({}^{-1}\)) & 12.1 \(\pm\) 0.2 & 8.9 \(\pm\) 0.1 & 14.7 \(\pm\) 0.3 & 9.2 \(\pm\) 0.1 & 11.2 & 2.7 \\ K (g kg\({}^{-1}\)) & 19.4 \(\pm\) 0.1 & 21.6 \(\pm\) 0.2 & 21.4 \(\pm\) 0.4 & 15.0 \(\pm\) 0.2 & 19.4 & 3.1 \\ Na (g kg\({}^{-1}\)) & 12.8 \(\pm\) 0.1 & 9.3 \(\pm\) 0.1 & 11.3 \(\pm\) 0.2 & 5.6 \(\pm\) 0.1 & 9.7 & 3.1 \\ Ca (g kg\({}^{-1}\)) & 44.6 \(\pm\) 0.5 & 20.8 \(\pm\) 0.1 & 53.3 \(\pm\) 1.1 & 47.2 \(\pm\) 0.5 & 41.5 & 14.2 \\ Mg (g kg\({}^{-1}\)) & 3.0 \(\pm\) 0.0 & 2.4 \(\pm\) 0.0 & 3.5 \(\pm\) 0.1 & 2.5 \(\pm\) 0.0 & 2.9 & 0.5 \\ Fe (mg kg\({}^{-1}\)) & 870 \(\pm\) 9 & 619 \(\pm\) 6 & 659 \(\pm\) 9 & 896 \(\pm\) 15 & 761 & 143 \\ Mn (mg kg\({}^{-1}\)) & 67.0 \(\pm\) 0.5 & 69.5 \(\pm\) 0.6 & 81.6 \(\pm\) 1.7 & 54.6 \(\pm\) 0.7 & 68.2 & 11.1 \\ \hline \hline \end{tabular}  
* EC: electrical conductivity; TOC: total organic carbon; TN: total nitrogen. SD: standard deviation. Data values reported as mean value \(\pm\) standard error:  
\end{table} 
Table 5: Main agronomic and quality characteristics of final compost (data expressed on a dry weight basis). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **Composst 1** & **Composst 2** & **Composst 3** & **Composst 4** & **Average SD** \\ \hline EA (\%) & 4.7 & 5.9 & 5.8 & 7.2 & 5.9 & 1.0 \\ FA (\%) & 2.7 & 2.9 & 2.6 \(\pm\) 0.2 & 3.2 & 2.9 & 0.3 \\ HR (\%) & 18.3 & 19.9 & 21.3 & 23.6 & 20.8 & 2.2 \\ HI (\%) & 11.6 & 13.3 & 14.7 & 16.3 & 14.0 & 2.0 \\ Pha (\%) & 63.5 & 67.0 & 69.0 & 69.2 & 67.2 & 2.6 \\ Polymerization rate & 1.7 & 2.0 & 2.2 & 2.3 & 2.1 & 0.3 \\ GI (\%) & 101 & 75.2 & 77.8 & 104.5 & 89.7 & 15.4 \\ CEC (meq 100 g\({}^{-1}\) OM) & 55.0 & 125 & 113 & 76.7 & 92.4 & 32.3 \\ Stability test & V, Stable & V, Stable & V, Stable & V, Stable \\ \hline \hline \end{tabular}  
* HA: humic acid-like carbon; FA: fulvic acid-like carbon; HR: humification ratio; HI: humification index; Pha: percentage of humic-acid-like carbon; CEC: cation exchange capacity; OM: organic matter; GI: germination index; SD: standard deviation.  
\end{table} 
Table 6: Parameters of compost maturity and stability (data expressed on a dry weight basis).
## 5 Simulation Results and Analysis  
To analyze the GDOP of multiple TDOAs and single TOA based hybrid positioning and the positioning accuracy of the proposed closed-form localization algorithm, we have built a simulation scenario according to the real scene of the zone 1, underground parking lot, Beijing University of Posts and Telecommunications. The real scene is shown in Figure 2 and the top view of the developed simulation scenario is shown in Figure 3.  
Figure 3: Simulation scenario. Dark area is the simulation area.  
Figure 2: The real scene of the underground parking lot. ANT: antenna.  
In Figure 3, a Cartesian coordinate system is constructed in meters. The ANTs of the indoor distribution system are referred to the real antennas' location in the underground parking lot.  
In the following parts of this section, we compared the GDOP of the proposed hybrid positioning and the GDOP of TDOA-only positioning. Then, we analyzed the positioning accuracy of the proposed localization algorithm.
# Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera
## 3 System Configuration
### Drone Hardware Details
#### 3.1.3 Flight Controller (Pixhawk 2.1)  
Pixhawk 2.1 was used as the flight controller (FC) for the hexacopter drone system. Pixhawk is an open source project that has gained a lot of interest in the recent years. It is expected to secure a lot of investment and lead the fourth industrial revolution. It consists of a 32-bit ARM Cortex-M4F with floating-point unit (FPU), MPU6000 as main accelerometer and gyroscope, MEAS barometer, 5x UART, etc. The Pixhawk flight controller is more advanced than the PX4 flight control system and consists of PX4-IO and PX4-FMU controllers all integrated into a single chip along with additional IO, memory, and many other features. It supports the Unix/Linux programming environment and provides strong development capabilities by using autopilot function and multithreading. In addition, it can automatically detect and configure most peripherals. Moreover, it can support faster response, accurate positioning, and open source codes.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 6 Classifier Maintenance
### Concept Drift  
The topic of changes in streaming data is an integral part of stream classification applications. It should be noted that in addition to the term concept drift, alternative formulations can also be found in the literature. Other terms are, e.g., concept change, distribution, or data shift [36].  
Different types of concept drift can be distinguished. Due to the many strands of research, various terminologies have developed over the years. Table 6 lists the work cited in drift detection and the distinctions of concept drifts. Combining the findings within those works, four different concept drift types can be differentiated. Within Figure 9, we present a graphical comparison of them. Within a sudden (also known as abrupt) concept drift, the data distribution changes suddenly at a certain point in time. In other words, the new concept replaces the old one completely. Whereas during an incremental drift, the data distribution changes progressively over a period of time. In contrast to the sudden drift, the old concept vanishes smoothly within an incremental drift. A sudden drift occurs, for example, when a new sensor type is included in an IoT application. An incremental one is comparable to a setting where a sensor becomes slightly more and more inaccurate over time [4]. Some researchers also mention the category of gradual concept drift, where first a new concept appears within the old one for some periods in time [4,152]. At some point, the new concept replaces the old one completely. Please note that the terms sudden/abrupt and gradual are sometimes related to the speed or duration of a drift [153].  
So-called reoccurring (or seasonal) concept drifts, follow a periodic pattern, where the old and the new concept alternate [4; 36].  
Researchers also differentiate regarding the subject of the respective drift [153]. The first category is the class changes, called class drift, prior probability shift, or more prominent real drift. Here, Class probabilities \(P(\mathcal{Y}|\mathcal{X})\) change over time. The second category is called covariate drift, or virtual concept drift, where the distribution of features \(P(X)\) shifts. Of course, a combination of both, where the distribution of features and classes occur, is possible [36].  
A particular case of drift is the appearance of novel classes [153]. Within the scenario of the novel or emerging classes, a new and previously unknown class is added to the stream scenario. Sometimes, this is also denoted as a concept evolution [154]. Some works make other distinctions in the context of concept drifts. These, e.g., cover the severity, predictability, and frequency of changes in the data stream [4]. We recommend the work of [153] for the interested reader. The authors discuss the different terminologies extensively and relate them to each other in a broad taxonomy.
# A Multi-Robot Formation Platform based on an Indoor Global Positioning System
## 4 Experiments of Leader-Follower Formation Control
### The Experiment of Trajectory Tracking of Leader-Follower Formation under Deterministic Environment
#### 4.1.2 Experiment on Scalability Formation Control  
To validate adaptability to complex formations and the scalability of the multi-robot formation platform, we also consider two robots forming a typical circle formation and four robots forming a diamond formation.  
Figure 9(a) shows the leader robot performing a circle path and the follower robot tracking the leader robot to form a typical line formation. Tracking the circle trajectory for the follower robot is a challenge because of the changing orientation. The follower robot tracked the circle trajectory smoothly, which demonstrates the adaptability of our multi-robot platform to complex formations. Figure 9(b) shows the leader robot guided the three follower robots F1, F2, and F3 forming and maintaining a desired diamond formation. The experiments have been performed in a different number of robots, which validates the scalability of the multi-robot formation platform. The experiment video of circle trajectory of the leader robot with one follower robot can be found at Supplementary Materials [[https://youtu.be/4](https://youtu.be/4) caScl5 PF_U]([https://youtu.be/4](https://youtu.be/4) caScl5 PF_U). The experiment video of the line trajectory of leader robot with three follower robots to realize a diamond formation can be found at Supplementary Materials [[https://youtu.be/NYSVTKw46](https://youtu.be/NYSVTKw46) vU]([https://youtu.be/NYSVTKw46](https://youtu.be/NYSVTKw46) vU).  
Figure 8: Snapshots of the video about line trajectory of a triangle formation switching.  
Figure 9: The formation tracking errors of the follower robots \(F_{1}\) and \(F_{2}\) in formation-switching experiment; (**a**) is the distance error of follower robots; (**b**) is the angular error of follower robots.
## 3 Methodology
### Formal Modeling  
As a first phase in our framework, we employ formal modeling to identify potential theoretical transitions that could introduce issues and vulnerabilities within a protocol specification or its implementation. This approach involves translating the protocol specification into a hierarchical diagram that describes valid communication channels, device roles, and the states each device may assume in compliance with the protocol specification. While a protocol specification typically outlines required transitions between states, it may not account for transitions that involve error handling, such as those triggered by erroneous packets or timeouts, which are not always explicitly stated. To address this, formal modeling is performed using the Construction and Analysis of Distributed Processes (CADP) framework [25], a widely used toolset for process algebra and formal modeling of concurrent systems. Our previous work has identified critical transition states within the Modbus protocol, both in serial and TCP communication contexts [23], thereby illustrating the efficacy of this approach. This modeling is conducted using the Lotos New Technology (LNT) specification language [26], which facilitates the translation of the protocol specification into a hierarchical diagram that identifies communication channels and individual node states. LNT employs rendezvous synchronization communication processes, which makes it particularly effective for modeling communication protocols. This is especially useful for IIoT processes, as they may run multiple concurrent processes, yet they must still synchronize and communicate when a packet is received.
## 2 Related Work
### SENet  
SENet is a convolutional neural network structure proposed by [PERSON] et al. [2017] in 2017. The structure consists of Squeeze, Excitation, and Reweight. Different from the traditional method of introducing a new spatial dimension for the fusion of feature channels, the network structure adopts a \"feature recalibration\" strategy to explicitly construct the interdependence between feature channels.  
Figure 3 is the structural diagram of the SENet. The number of characteristic channels of input \(x\) is c\({}_{1}\), and a feature of the feature channel number c\({}_{2}\) is obtained by a general transformation such as a series of convolutions. Different from the traditional CNN, the following three functions are used to \"recalibrate\" the previously obtained features.  
The first operation is Squeeze. Feature maps obtained by the previous operations are aggregated to produce a channel descriptor. Through this operation, each two-dimensional feature map is compressed by a global average pooling to become a real number, and the output of the c feature maps is a real number column of \(1\times 1\times c\), which is expressed as in Equation (3) given in [18]:  
\[\mathrm{z}_{c}=F_{sq}(u_{c})=\frac{1}{W\times H}\sum_{i=1}^{W}\sum_{j=1}^{H}u_ {c}(i,j) \tag{3}\]  
The next operation is excitation also known as adaptive recalibration. [PERSON] et al. [18] employ a simple gating mechanism with a sigmoid activation to fully capture channel-wise dependencies. This operation makes the model obtain a collection of per-channel modulation weights which are vital for SENet. As follows given in [18]:  
\[s=F_{\alpha}(z,W)=\sigma(g(z,W))=\sigma(W_{2}\delta(W_{1}z)) \tag{4}\]  
where \(\sigma\) refers to the Rectified Linear Units (ReLU) [21] function, \(W_{1}\in R^{\frac{C}{\tau}\times C}\) and \(W_{2}\in R^{C\times\frac{C}{\tau}}\).  
Firstly, the feature dimension is reduced by a fully connected layer and activated by ReLU. Then it is brought back to the original dimension by a fully connected layer.  
The final operation is to re-scale the feature maps. The \(s_{c}\) obtained through excitation is weighted to the corresponding feature map by multiplication and then the model gets the final output of the block, that is c\({}_{2}\) in Figure 3. The re-calibration of the original feature is completed as in Equation (5) given in [18]:  
\[\tilde{x}_{c}=F_{scale}(u_{c},s_{c})=s_{c}\cdot u_{c} \tag{5}\]  
where \(\tilde{x}=[\tilde{x}_{1},\tilde{x}_{2},\ldots,\tilde{x}_{c}]\) and \(F_{scale}(u_{c},s_{c})\) refers to channel-wise multiplication between the scalar \(s_{c}\) and the feature map \(u_{c}\in R^{H\times W}\).
# Pathophysiologyiology and Treatment of Soriasis: From Clinical Practice to Basic Research
## 2 Clinical Types of Psoriasis  
Psoriasis presents in several distinct clinical types, each characterized by unique lesion morphology, affected body regions, and varying responses to treatment. As the most prevalent form of psoriasis, plaque psoriasis is characterized by round or flat, erythematous plaques with a distinctive pink base covered by silvery-white scales. These plaques commonly occur on areas such as the elbows, knees, scalp, and lower back [23]. The condition is often accompanied by varying degrees of pruritus, which can range from mild to severe, significantly affecting patient comfort and quality of life.  
Guttate psoriasis is identified by small, discrete red papules, typically ranging from 1 to 10 mm in diameter, with minimal scaling [24]. Guttate psoriasis often appears suddenly, often triggered by a streptococcal infection, such as strep throat [25].  
Pustular psoriasis is further divided into generalized pustular psoriasis (GPP), localized forms such as palmoplantar pustulosis (PPP) and acrodermatitis continua of Hallopeau (ACH) [26]. GPP is marked by a rapid eruption of sterile pustules on a background of red, inflamed skin, often accompanied by systemic symptoms like fever, fatigue, and muscle weakness, which can necessitate urgent medical attention due to potential complications [27]. PPP manifests as sterile pustules on the palms and soles, associated with erythema, scaling, and thickening of the skin (keratoderma) [28]. ACH is a condition characterized by recurrent pustules on the tips of the fingers. Patients may experience osteolysis, which has a significant impact on quality of life [29].  
Erythrodermic psoriasis is a rare but severe form of psoriasis, characterized by extensive erythema and desquamation covering more than 75% of the body's surface area [30]. Erythhydermic psoriasis often arises as an exacerbation of another psoriasis subtype or in response to triggers such as infection, withdrawal of systemic psoriasis treatment, or stress. Due to its widespread inflammation, this form can lead to systemic complications, including fluid and electrolyte imbalances, thermoregulation disturbances, and increased risk of infection [31].  
Psoriatic arthritis (PsA) is a chronic inflammatory arthritis associated with psoriasis. PsA commonly involves the small joints of the hands and feet, but larger joints such as the hips, knees, and spine may also be affected. Symptoms include joint pain, swelling, and stiffness, which can be disabling and may occur either in conjunction with or independently of skin lesions [32].
## 3 System Model  
This section establishes the foundational framework for analyzing the interference radiated from MVDS towards GEO satellites. First, an overview of the potential interference mechanisms will be presented. To facilitate a comprehensive investigation, a representative system model is defined (as shown in Figure 1b), encompassing the geographical scenario, the operational frequency bands of the involved systems, and the potential interference pathways.  
MVDS is a wireless television distribution system designed to deliver high-quality, multi-channel programming directly to households. It aggregates signals from various sources, including satellites, terrestrial feeds, and IP-based streams. Operating in the Ku-band, MVDS systems typically use high-power transmitters (often above 10 dBW) to provide direct-to-home (DTH) television services. The MVDS transmission infrastructure includes the following two primary types of microwave links.  
Repeater link: This link connects transmission sites via intermediate repeater stations. It uses high-power Ku-band uplinks (ranging from 13.75 to 14.75 GHz) with transmission power levels typically between 40 and 50 dBm.  
Direct-to-home (DTH) broadcast link: This link transmits content directly to end-users using Ku-band downlink frequencies (10.75 to 13.75 GHz), also with transmission powers in the 40-50 dBm range.  
These two components work together to ensure wide coverage and reliable service delivery, especially in areas where traditional cable infrastructure is limited or absent.  
Figure 1b illustrates the spatial relationship between the MVDS transmission site and the target satellite (Intelsat1002 at 35\({}^{\circ}\) W). The magenta line represents the interference radiated from MVDS toward the satellite transmission link from the MVDS site in Beirut to the satellite.
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 3 Materials and Methods
### Real-Time Video Processing  
The system continuously processes video frames in real time. It captures frames from the camera, detects poses using the YOLOv7 model, evaluates fall conditions, sends alerts if necessary, and displays the results. This real-time processing loop ensures immediate responses to potential falls, enhancing the system's safety and monitoring capabilities. The loop runs until manually terminated, providing continuous surveillance and fall detection.
## 3 Dynamic Characteristics and Test Cases
### Shake Table Test Cases  
When subjected to longitudinal earthquakes, CSBs respond to the significant internal force of the tower and large displacement of the girder [24]. It is usually one of the control cases of the seismic design of cable-stayed bridges [25]. Although lateral and vertical ground motions will also have a non-negligible effect on the seismic response of the CSB. However, when considering multi-directional seismic excitation, the seismic responses of the CSB are more complicate as multi-directional excitations maybe coupled. It is unfavorable to study the influence law of velocity pulse effect or wave passage effect. In order to avoid the complexity caused by multi-directional seismic excitations, this experiments only focuses on seismic responses of CSB subjected to longitudinal seismic excitation. The schematic diagram of input excitations and sensors are shown in Figure 10.  
Besides, the seismic response of the CSB subjected to non-uniform excitation are studied. It should be noted that the non-uniform excitation in this paper only considers wave passage effects, while local site effects and incoherence effects are not considered. In the test, the wave passage effect is realized by the time delay (\(dt\)) between two tables, shown in Figure 10.  
Figure 10: Schematic diagram of input excitations and sensors.  
Figure 9: Input ground motions (**a**) Acceleration time histories; (**b**) Acceleration Fourier Amplitude; (**c**) Velocity time histories; (**d**) Displacement time histories;The delay time \(dt\) can be calculated by following formula:  
\[dt=\frac{L}{v_{app}}\cdot S_{t} \tag{1}\]  
\(L\) stands for the distance of two points; \(V_{\text{app}}\) stands for the apparent wave velocity. In this paper, different \(V_{\text{app}}\) is studied, such as 480 m/s, 240 m/s and 120 m/s; \(S_{\text{t}}\) stands for the similitude ratios of time, that is 0.0707 in table 2. As the main span of the CSB is 680 m, delay time \(dt\) in the test is calculated as 0.1 s, 0.2 s and 0.4 s, listed in Table 4.  
As mentioned above, the ScEL and ScTCU are adopted as input motions for comparing the different seismic responses due to the NNF the PNF motion. Table 4 shows the shake table test cases.