esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Statement
### Line-of-Sight (LoS)  
In addition to NFZ, the tall buildings may block the wireless channels. Moreover, they may create some shadow regions where the solar-powered UAV cannot harvest energy from the sun. Thus, it is necessary to have an evaluation model to verify whether the LoS condition is available between the UAV and the ground node, and the UAV and the sun.  
Suppose that the two entities locate at \(p\) and \(q\), respectively. The straight line segment between \(p\) and \(q\) can be written as follows:  
\[\begin{cases}x=x_{q}+\alpha\tau,\\ y=y_{q}+\beta\tau,\\ z=z_{q}+\gamma\tau,\\ \min\{x_{p},x_{q}\}\leq x_{q}+\alpha\tau\leq\max\{x_{p},x_{q}\},\end{cases} \tag{8}\]  
where \(q=(x_{q},y_{q},z_{q})\), \(p=(x_{p},y_{p},z_{p})\), and \(\frac{\overrightarrow{p}}{\lVert\overrightarrow{p}\rVert}=(\alpha,\beta,\gamma)\). The last inequality specifies the range of the free variable \(\tau\).  
Whether the LoS between \(p\) and \(q\) is blocked by a prism depends on whether the line segment (8) and corresponding the prism (7) have any intersection points. To this end, we can solve Equations (7) and (8) for each prism. If there are solutions to any prism, the LoS is blocked (see Figure 2); otherwise, the LoS between \(p\) and \(q\) are not blocked.  
Note that, to verify if the LoS between the UAV and the sun is blocked, we need to have the sun's location. Let \(V\) be the unit vector representing the sunlight direction. With the azimuth angle \(a_{z}\) and the  
Figure 2: Prisms.  
elevation angle \(\alpha_{e}\) of the sun, the vector \(V=[\cos\alpha_{e}\cos\alpha_{z},\cos\alpha_{e}\sin\alpha_{z},-\sin\alpha_{z}]^{T}\)[32]. We can imagine that the sun locates at \(q_{sun}=p-V\tau\), where \(\tau\) takes a large value so that the sun is very far from \(p\). Let \(b(t)\) be a binary variable: \(b(t)=1\), if the UAV and the sun have LoS; \(b(t)=0\), otherwise. With the symbol \(b(t)\), the residual energy of the UAV should be calculated according to the following equation:  
\[\dot{Q}(t)=P^{sun}b(t)-P(t). \tag{9}\]  
For the safety of operation, at any time, the residual energy of UAV \(i\) cannot be smaller than the threshold \(Q^{min}\):  
\[Q(t)\geq Q^{min}. \tag{10}\]
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 1 Introduction
(**b**) MediaPipe H holistic [13], with partial improvement but persistent errors. (**c**) IPN HandS, refined using our annotation tool, accurately captures the active finger’s position. * We introduce a practical annotation tool that combines automatic initialization, interactive refinement, and temporal editing to efficiently generate high-quality hand skeleton annotations from video frames. * We present IPN HandS, an extended version of the IPN Hand dataset [5], enriched with over 800,000 frame-level hand skeleton annotations and refined temporal gesture boundaries. * We provide a detailed experimental evaluation showing that models trained on IPN HandS significantly outperform those trained on MediaPipe-based annotations, highlighting the impact of annotation quality on LSTM-based HGR performance. In addition, the annotation tool and the IPN HandS dataset will be made publicly available at Github [14] to support reproducible research. This aims to provide the community with practical resources for developing more accurate and efficient HGR models, as well as for annotating other datasets that require frame-level hand skeletons. The remainder of the paper is structured as follows. Section 2 reviews related work on annotation tools, HGR datasets, and skeleton-based recognition methods. Section 3 presents the IPN HandS annotation tool. Section 4 introduces the IPN HandS dataset, describing the annotation process and dataset statistics. Section 5 evaluates the impact of our refined annotations on HGR performance, outlining the LSTM-based model, experimental setup, and accuracy improvements. Section 6 discusses the broader implications of our tool and the role of high-quality annotations in HGR. Finally, Section 7 concludes the paper and outlines future directions.
# A Cross-Layer Optimized Opportunistic Routing Scheme for Loss-and-Delay Sensitive WSNs
## 4 System Model
### Reliability Model  
Same as the reliability model proposed by [PERSON] [56], communication between nodes can cause errors. Consider the communication link between two nodes having a distance separation of \(d\). The propagation path loss can be expressed as [56]:  
\[PL(d)=PL(d_{0})+10n\cdot\log_{10}\left(\frac{d}{d_{0}}\right)+X_{\sigma} \tag{2}\]  
Here, \(d_{0}\) is a reference distance and \(PL(d_{0})\) represents the path loss at that distance. \(n\) is the path-loss exponent; \(X_{\sigma}\) is the shadowing component Obeying the Gauss distribution with zero mean and standard deviation. In most situations, \(X_{\sigma}\) is a random process, which means a function of time. Since not assuming the dynamic environment, we use it as a constant random variable to model a specific link with time.  
Considering a transmit power of \(P_{t}\) and an additive white Gaussian noise (AWGN) power of \(P_{n}\), the \(SNR\) at the receiver is obtained as [56]:  
\[\gamma(d)_{dB}=P_{dB}-PL(d)_{dB}-P_{ndB} \tag{3}\]  
According to [29], \(P_{n}\) depends on wireless signal and environment, and its value is given [56]:  
\[P_{n}=(F+1)kT_{0}B \tag{4}\]  
Here, \(F\) is the noise factor, \(k\) is the Boltzmann constant, \(T_{0}\) is the environment temperature, and \(B\) is the equivalent bandwidth. According to [61], the common network environment have a background noise values \(-115\) dBm.  
For the reliability performance evaluation, we adopt the communication link model reported in [29]. In this paper, the Packet Acceptance Rate (\(PAR\)) is used to measure the quality of the communication link, and it can be calculated by the following equation [29]:  
\[par_{(d)}=\left(1-\frac{1}{2}exp^{-\frac{SNR_{(d)}}{2}\times\frac{B_{N}}{R_{D} }}\right)^{8f} \tag{5}\]  
Here, \(d\) is the node spacing, \(R_{D}\) is the data rate, \(f\) is the size of a data packet (in bytes), and \(B_{N}\) is noise bandwidth.  
According to [47], the end-to-end reliability \(\psi\) is the product of all the one-hop transmission reliability on the routing path, which can specifically be expressed as:  
\[\psi=\zeta_{1}\cdot\zeta_{2}\cdot\zeta_{3}\cdot\cdot\cdot\cdot\zeta_{k} \tag{6}\]  
where \(\zeta_{i}\) is the reliability of the \(i^{th}\) hop.
## 7 Simulation Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Optimal Power Allocation [11] in Downlink NOMA Clusters without IRS**} \\ \hline
**Cluster Size** & **User1** & **User2** & **User3** & **User4** & **User5** & **User6** \\ \hline
**n = 3** & 5.5925 & 0.9613 & 0.9274 & - & - & - \\ \hline
**n = 4** & 4.7940 & 0.9456 & 0.9468 & 0.9147 & - & - \\ \hline
**n = 5** & 3.9800 & 0.9146 & 0.9286 & 0.9346 & 0.9031 & - \\ \hline
**n = 6** & 3.1276 & 0.8584 & 0.8888 & 0.9117 & 0.9220 & 0.8960 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Normalized IDR analysis in bits per sec per Hertz of Optimal power allocation [11] in downlink NOMA clusters without IRS. Figure 6: Data-rate comparison for different cluster size highest channel gain user (user1). as we increase the number of meta-atoms, the change in the data rate of the corresponding user is negligible and we can say that the user has reached the upper limit of the IDR. The data rate of the highest CG user is not saturated with respect to its effective CG. The rest of the users belonging to the same NOMA cluster are saturated to a predefined value \(R_{il_{max}}\), which is dependent on the power coefficients of the users. Simulation results for \(R_{il_{max}}\) are in agreement with (14).
## 3 System Model  
Figure 1 shows the system architecture of two-tier MLTNs and an example of fleet cyclists forming three ad-hoc (low-tier) communication groups. We consider cyclists in a fleet moving along a common cycling route. All cyclists use smartphones with both 3G/LTE and IEEE 802.11 interfaces, which form the high-tier network and the low-tier (ad-hoc) network, respectively. A backend server manages sensing data exchange. Current individual sensing and global fleet information are frequently uploaded to the server and downloaded from the server by cyclists, respectively. The high-tier network is assumed to be always connected. However, the use of the high-tier network has to be limited to save bandwidth and reduce latency. Therefore, the use of the low-tier network has to be maximized to encourage local communication. To facilitate sensing data collection, the cyclists are divided into different groups, where each has a gateway node. Cyclists in the same group can communicate with  
each other via the low-tier network. Only the gateways transmit collected sensing data to the backend server via the high-tier network. Sensing data distribution occurs via the gateways, which use the high-tier network to receive sensing data from the server and the low-tier network to forward sensing data to members.  
An undirected graph \(G=(V,E)\) is used to represent the low-tier network, where \(V\) is the set of all fleet members and \(E\) is the set of all links. Because the cyclists have a common cycling path, \(G\) is divided into a number of long-thin groups that can be sorted in order with its low-tier links. During the sensing data collection process, the members of each group transmit their sensing data to the gateway using the low-tier network. The gateway then transmits the collected sensing data to the server via the high-tier network. During the sensing data distribution process, the server sends global fleet information to all gateways (via the high-tier network), and they in turn broadcast the received fleet information to group members (via the low-tier network). Due to the different speeds of cyclists, a group could split separately and two groups could merge together. The following issues are addressed to minimize the high-tier communication cost in two-tier MLTNs:  
1. _Cyclist Grouping_: How should cyclist groups be managed as cyclists join or leave, to ensure that the gateway can receive the sensing data of all members and transmit it to the server?
2. _Group Merging and Splitting_: How should groups be merged and split according to cycling speeds so that an existing gateway can be removed and a new gateway can be selected, respectively?
3. _Transmission Reordering_: How should the transmission order of members for sensing data collection be determined with the aim of minimizing packet collision probabilities in the low-tier network?
4. _Data Aggregating_: How should the sensing data of each member be aggregated to minimize the member sensing data uploaded and global fleet information downloaded via the high-tier network?
###### Abstract  
New hybrid nanosystems containing the antibacterial substances dioxide or gentamicin sulfate with bioactive metal (Ag, Cu) nanoparticles have been obtained by a cryogenic freeze-drying method and incorporate further the nanocomposites thus obtained into the cryogenically structured biopolymeric matrices based on gelatin, calcium alginate, and chitosan. FTIR, UV-visible, and NMR spectroscopy, TEM and SEM microscopy data show that the resulting systems consist of wide-porous polymer sponges (pore diameters, 10-200 \(\upmu\)m) that contain antibacterial drugs and silver (2-30 nm) or copper (1-5 nm) nanoparticles. The investigation showed that these systems ensure a gradual release of dioxidine (from 40 min up to 3 days), depending on the nature of the matrix and its microstructure. The higher activity of hybrid composites based on nanometals and dioxidine or incorporated into cryostructured biopolymer matrices against the bacterial strains of _Escherichia coli_ 52, _Staphylococcus aureus_ 144 is demonstrated as compared to the individual components in the same matrices.  
c 2020 2019 24 December 2019 2019  
the incorporation of a pharmacologically active component into a biopolymeric matrix, which provides gradual release of the drug and prolonged maintenance of the desired concentration of the active drug in the body or locally in a target organ. Systems based on polysaccharides (starch, cellulose, chitosan, hyaluronic acid, calcium alginate) and proteins (such as albumin, gelatin, transferrin, lactoferrin, silk fibroin, collagen, and \(\beta\)-casein) are used as carriers for drugs [8]. Biopolymers are preliminarily modified, fixing their dimensions and inner spatial network to optimize their biocompatibility and ability to control release of the bioactive compounds, which are incorporated into the matrix before their use. Biopolymers are used as nano- and microparticles (for oral and intravenous insertion) and bulky hydrogel matrices (cryogels if the matrix is formed at low temperatures) with controlled drug release for local application [9,10]. The systems based on chitosan, gelatin, and calcium alginate could be also used as possible biopolymer depots for drug substances with their elongated and controlled release [11, 12, 13, 14].  
The goal of this work was to synthesize new hybrid nanocomposites based on the antibacterial substance dioxide and gentamicin sulfate, each combined with the bioactive metal nanoparticles (silver or copper), and to incorporate the obtained nanocomposites into highly porous cryostructured polymer matrices of calcium alginate, gelatin, and chitosan capable for prolonged drug release. The antibacterial activity of these hybrid antibacterial nanoforms was evaluated against bacterial strains of the _Escherichia coli_ 52, _Staphylococcus aureus 144_.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### State-Space Model of the Structure with Current-Flowing Circuit
\begin{bmatrix}\mathbf{0}_{N\times N}&\mathbf{I}_{N\times N}&0&0&0&0&0\\ -\mathbf{M}_{u}^{-1}\mathbf{K}_{0}&-\mathbf{M}_{u}^{-1}\mathbf{C}_{d}&-\mathbf{M}_{u}^{-1}\mathbf{ \Theta}_{1}\mathbf{C}_{p}^{-1}&0&0&0&0\\ \mathbf{0}_{1\times N}&\mathbf{0}_{1\times N}&0&0&0&0&1&1\\ \mathbf{0}_{1\times N}&\mathbf{0}_{1\times N}&0&0&0&0&1&0\\ \mathbf{0}_{1\times N}&\mathbf{0}_{1\times N}&0&0&0&0&0&1\\ \mathbf{0}_{1\times N}&\mathbf{0}_{1\times N}&0&0&0&0&1&1\\ -\frac{1}{1\cdot\mathbf{C}_{p}}\mathbf{\Theta}_{1}^{T}&\mathbf{0}_{1\times N}&-\frac{1}{ L_{1}\mathbf{C}_{p}}&-\frac{1}{L_{1}\mathbf{C}_{1}}&0&-\frac{R_{1}}{L_{1}}&0\\ -\frac{1}{L_{2}\mathbf{C}_{p}}\mathbf{\Theta}_{1}^{T}&\mathbf{0}_{1\times N}&-\frac{1}{ L_{2}\mathbf{C}_{p}}&0&-\frac{1}{L_{2}\mathbf{C}_{2}}&0&0&-\frac{R_{2}}{L_{2}} \end{bmatrix}  \tag{49}\]  
and  
\[\mathbf{B}^{sh}= \begin{bmatrix}\mathbf{0}_{N\times N}&\mathbf{0}_{N\times 1}\\ \mathbf{M}_{u}^{-1}&\mathbf{M}_{u}^{-1}\mathbf{\Theta}_{2}\\ \mathbf{0}_{1\times N}&0\\ \mathbf{0}_{1\times N}&0\\ \mathbf{0}_{1\times N}&0\\ \mathbf{0}_{1\times N}&0\end{bmatrix} ,\,\mathbf{w}_{sh}= \begin{Bmatrix}\mathbf{F}_{m}\\ v_{2}\end{Bmatrix}  \tag{50}\]  
where \(N\) denotes the total number of degrees of freedom. In this work, the output of interest is usually tip displacement, so the matrices \(\mathbf{C}^{sh}\) and \(\mathbf{D}^{sh}\), corresponding to that output, are written as:  
\[\mathbf{C}^{sh}= \begin{bmatrix}\mathbf{I}_{0}&\mathbf{0}_{1\times N}&0&0&0&0&0&0\end{bmatrix} ,\,\mathbf{D}^{sh}\!\!=\!\mathbf{0} \tag{51}\]  
where \(\mathbf{I}_{0}\) is the \(1\times N\) matrix with all its elements set to zero except the \(N-1\) element which corresponds to the transverse displacement degree of freedom. The FE model, as well as the state-space representation of the piezoelectric shunt-damping system, is implemented in MATLAB.
## 6 Implementation Experiments  
This section focuses on the experiments conducted to implement our proposed framework. First, we present the results of empirical experiments on the degree of cumulative error caused by spatial tracking with the camera. The final cumulative error is extremely important in the design of indoor AR navigation because it determines the radius and location settings of geofences and the calibration timing. Second, the prototype introduced in Section 5.3 is tested in the actual application field to demonstrate the functionality of indoor AR navigation and the feasibility of the proposed framework. Third, we emphasized the realization of UI for periodical calibration and tested its effectiveness for accumulative error correction. All these experiments were conducted by the members of our research team.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 3 Spacecraft Overview
### Communication  
The main focus of the communication system is reliability and simplicity, as the technological demonstration of the satellite shall not be endangered by a failure of the communication link. The scientific data generated by the satellite consists of telemetry data and positional data, therefore high data rates are not necessary.  
As first step of the design process, the frequencies for uplink and downlink are defined. The mission consists of education and technology demonstration. Consequently, amateur frequencies, which offer a number of advantages of a financial and organizational nature, are being used.  
For simplicity and budget reasons, only one antenna can be included. As a result, only one band will be used as well: UHF frequencies in the range of 435-438 MHz.  
The communication is performed by commercially available transceivers. A tradeoff lead to the selection of the RadioBro, MiniSatCom UHF transceiver. Although lacking extensive flight heritage, its size, power consumption, and configurability and comparably  
Figure 14: Telecommand, telemetry and anomaly flow between apps via the Rodos middleware.  
low cost lead to its use in the mission. With this product, two identical flight units can be integrated into the satellite.  
A redundant configuration is implemented, with two identical transceiver units on separate boards. As both radios share a single antenna, a radiofrequency (RF) switch is used to change between the units. Multiple layers of internal identification are integrated to determine whether a transceiver is operable or not. Each of the transceiver boards additionally consist of two SKITH nodes, eliminating any single-point-of-failure.  
The satellite design utilises only a single frequency antenna to avoid two antennas interfering with each other. As a result, the channel is capable of half duplex communication. Different configurations of antennas have been analysed, with regard to commercially available products and the UHF antenna module of EnduroSat has been selected. It represents a trade-off between cost, radiation pattern and deployment mechanism. The maximum gain is about 0 dBi, although the omnidirectional pattern is more important than its peak gain.
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
## 4 Data Retrieval and Results
### Air Quality Overview over Paratba Valley  
As mentioned earlier, in order to provide an air quality overview of the Paraiba valley region, monthly mean particulate matter concentrations were retrieved from four CETESB stations installed in three different cities, Sao Jose dos Campos (S. J. dos Campos and S. J. dos Campos-Satellite stations), Taubate and Guaratingueta, as can be seen in Figure 1. Concentration values of \(PM_{10}\) were used since it is the single common product for all stations.  
The monthly mean particulate matter concentration, taking into account four CETESB air quality stations spread across the metropolitan region of Paraiba valley, shows an increase in air pollutant episodes during dry seasons, between June and October of the subsequent years (2015 to 2020), reaching peaks of pollution during September for all years, except 2019, as can be seen in Figure 2. The period of high values of particulate matter concentration coincides with the dry season in the Southeastern region of Brazil, when there is low precipitation and several episodes of biomass burning events that can be detected all over South America [32; 33; 34; 35], especially in September. These results show a pattern in the aerosol concentration values and demonstrate that there is a homogeneity in the Metropolitan Region of Paraiba valley.
## 10 Typical Characteristics of Pulsed Operation
As the pump power is further increased, a small increase in ML power can be observed, but the CW power catches up quickly. When the CW output power becomes larger than the pulse output power, the excess energy excites a CW mode, which oscillates in the cavity along with the pulse, generating an output spectrum of a broad bandwidth with a narrow CW spike attached to it. The peak power \"quantization\" effect is manifested again when the pump is further increased, until the threshold for a double pulse is crossed. In this regime, there is sufficient energy to sustain two pulses in the cavity, and the pulse prefers to split. The onset of a double pulse is usually (but not always) observed as a fringe pattern on the pulse spectrum, as plotted in Figure 10d, due to spectral interference between the two pulses [84]. Analysis of multi-pulse operation and single-pulse stabilization can be found in [85].
# An Analysis of ML-Based Outlier Detection from Mobile Phone Trajectories
## 5 Performance Analysis and Evaluation
### LOF and DBSCAN Comparison  
#### 5.6.1 GEO Dataset Results  
A global perspective on the performance (precision, recall, accuracy) of LOF, when applied to the GEO dataset, is provided in Figure 7, while the same performance perspective for DBSCAN is provided in Figure 8. For each chart, the X-axis represents the number of individual trajectories available. LOF (refer to Figure 7) reaches a stable precision across all trajectories, while the accuracy varies still within a good level. The recall (how well outliers are detected) results show, however, that LOF exhibits some difficulty in detecting outliers.  
Figure 5: Correlation of DBSCAN and LOF to parameters.  
Figure 6: Detection of outliers with LOF in the GEO and PTM datasets.  
DBSCAN (refer to Figure 8) results in more variability in terms of the three evaluation dimensions (precision, accuracy, and recall). Therefore, LOF is the algorithm that performs best in terms of outlier detection for the GEO dataset.