esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Instead of using inner-outer loop structure, we directly design the torques by using the flatness theory, where the high-order cooperative errors are used. The formation controller is decentralized, since the quadrotors move based on the measurement to their neighbors (in special, positions. We suppose that the velocities and higher-order derivatives are not available). Then, the main contributions of this paper are three-fold. A flatness-based formation control with estimated feedforwards is proposed such that the aggressive formation is attained. In order to estimate the velocities and higher-order derivatives of the neighbors states, an extended finite-time observer is proposed. To avoid probably instability caused by the great initial deviation of the estimated states, the saturated feedforwards are developed to improve the formation controller, the convergence of the formation tracking error is then analyzed. We note that our proposed controller can be combined with the existing methods by adding some nonlinear compositions into the controller, for instance, to deal with the bounded nonlinearities (uncertainties), to achieve asymptotic convergence, to improve the formation speed. These methods can be listed such as robust compensation [20,22], composite nonlinear feedback [13], artificial neural network estimation [23]. The paper is organized as follows. Some preliminaries in graph theory are given in Section 2. The aggressive control of quadrotors with finite-time observer is presented in Section 3. The formation control design and the stability analysis are shown in Section 4. Some simulation and experimental results are given in Section 5. Finally, some conclusions are stated in Section 6. Some useful notations and definitions are given as follows. **Notations**: The real vectorial field is represented by \(\mathbb{R}^{m}\). Notation \(\mathbb{R}^{+}\) represents real positive numbers. The saturation function \(\sigma_{b}:\mathbb{R}^{n}\to\mathbb{R}^{n}\) is defined as follows, if \(n=1\), \(\sigma_{b}(a)=\text{sign}(a)\cdot\min\{\|a\|,b\}\), where \(\text{sign}(\cdot)\) represents the sign function. If \(n>1\) and \(a=[a_{1},\dots,a_{n}]^{T}\), then, \(\sigma_{b}(a)=[\sigma_{b}(a_{1}),\dots,\sigma_{b}(a_{n})]^{T}\). In special, if \(b=\infty\), the saturation function is equal to a linear function without saturation, i.e., \(\sigma_{\infty}(a)=a\), for any \(a\in\mathbb{R}\). The notation \(C^{m}(\mathbb{R},\mathbb{R}^{n})\) represents the ensemble of \(m-\)order continuous manifolds in \(\mathbb{R}\to\mathbb{R}^{n}\). The notations \(I_{n}\) and \(\mathbf{0}_{n\times n}\) represent identical and zero matrices in \(\mathbb{R}^{n\times n}\). In special, \(\mathbf{1}_{n}\) and \(\mathbf{0}_{n}\) represent the vectors with all entries equal to one and zero in \(\mathbb{R}^{n}\), respectively. Function \(\lambda_{i}(\cdot)\) represents the \(i\)-th eigenvalue of matrix inside the parenthesis, in special,\(\lambda_{\text{max}}(\cdot)\) and \(\lambda_{\text{min}}(\cdot)\) represent the maximum and minimum eigenvalue of matrix inside the parenthesis, respectively.
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 4 Simulation Results
### Scenario 1:
#### 4.1.2 Results and Analysis
If no route exists, packet forwarding failure is increased since at some point in data transmission, the current packet holder node cannot find any appropriate node with less depth than itself. We can also observe from Figure 8 that DBR has a better PDR than EEDOR because it uses a greedy mechanism to flood the network with data packets. Figure 8: Packet Delivery Ratio (PDR) vs. Number of Nodes. Figure 7: Mean Energy Consumption per Node vs. Number of Nodes. The redundant packets, which happen because the flooding mechanism and resulting multiple paths, increase the probability of successful packet delivery and, as a result, the PDR, while in EEDOR, the current node selects its forwarding set based on the neighboring node depths without identifying void/trapped nodes. If the next forwarder is a void/trapped node (and therefore cannot find any node with less depth than itself), that node drops the received packet, which decreases the EEDOR packet delivery ratio. Finally, we conclude that the higher PDR for EEDOR-VA comes at an extra expense of energy cost compared with EEDOR. However, it still achieves higher PDR using less energy consumption than DBR.
## 3 Method
### Model Architecture
#### 3.3.3 Spatial-Temporal Embeddings  
CeT employs a graph embedding technique called Eigenmaps and linear transformations to extract spatial and temporal embeddings from dynamic directed graphs and temporal labels provided by HD maps, respectively. Specifically, it performs eigen-decomposition on dynamic directed graphs representing the connectivity between lane segments at each time step to derive spatial feature embeddings. Simultaneously, it applies linear transformations to temporal labels to create temporal embeddings. These embeddings capture spatial and temporal features from historical traffic data, enabling CeT to learn variations in traffic evolution patterns across different periods, spatial connectivity between lane segments, and the impact of intersection signal phases on vehicle movement across specific lane segments.  
Figure 3c demonstrates the extraction and generation of spatial embeddings \(SE^{t}\) and temporally embeddings \(TE^{t}\) from spatial attributes \(TM^{t}\) and temporal attributes \(T^{t}\). Specifically, CeT applies a graph embedding technique called Eigenmaps, which constructs the normalized Laplacian matrix \(L^{t}\in\mathbb{R}^{N_{h}\times N_{h}}\) and conducts eigen-decomposition on \(L\) to generate eigenvalue matrix \(TM^{t}\in\mathbb{R}^{N_{h}\times N_{k}}\), where \(N_{k}\) represents the number of top neighbor nodes with the strongest spatial correlation to the node in dynamic directed graphs consisting of lane segments. In this way, CeT defines local neighborhoods for all lane segments treated as nodes in the dynamic directed graphs, thereby enhancing the accuracy of traffic evolution predictions. On this basis, \(TM^{t}\) is linearly transformed into \(SE^{t}\in\mathbb{R}^{N_{h}\times d_{model}}\). Meanwhile, \(T^{t}\) is transformed into \(T^{t}\in\mathbb{R}^{N_{h}\times d_{model}}\) and concatenated with positional encoding \(PE\), which can be calculated as follows:  
\[PE_{pos}(i)=\begin{cases}sin(\frac{pos}{10,000\frac{1}{2^{t}}}),&\text{if $i$ is even},\\ cos(\frac{pos}{10,000\frac{1}{2^{t}}}),&\text{if $i$ is odd},\end{cases} \tag{8}\]  
where \(pos\in[0,N_{h}]\) and \(i\in[0,d_{model}]\) denote the position and dimension in \(T^{t}\). The concatenated vector \(T^{t}\) is then linearly transformed into \(TE^{t}\in\mathbb{R}^{N_{h}\times d_{model},s}\).
## 3 Perception of the User in the Environment: Methodologies and Technologies for Localization
### Localization by Emerging IoT Technologies  
The traditional indoor location-based services are often associated with people positioning and tracking by exploiting the wireless signal transmitted from their personal devices; for this reason, they are becoming a more and more integral part of the wider Internet of Things (IoT) paradigm whose related technologies are of interest also for proximity detection and localization. In this perspective, the idea of connecting different \"things\" can encompass localization, enhancing the wide range of provided services. IoT are considered one of the six \"disruptive civil technologies\" by the US National Intelligence Council (NIC) [31]; in addition to expected services related, as for example, to security, marketing,health and so on, the fruition of cultural heritage can achieve attractive benefits taking into account also that a wide spreading on the market implies a decreasing of costs. The main emerging radio technologies conceived for IoT and related to localization are SigFox, LoRa, IEEE 802.11 ah, and Weightless [1]. They all characterized by a wide reception range in outdoor applications (from 1 km of IEEE 802.11 ah to 50 km of SigFox), and extremely low energy consumption. As concerns the indoor applications, SlgFox, LoRa and Weightless suffer of signal attenuation due to the walls implying a relevant outdoor-indoor attenuation whereas IEEE 802.11 ah due to its shorter-range network architecture and to significantly lower propagation loss through free space and walls/obstructions can be expected a good candidate for indoor localization [1].
## 3 Results and Discussion
The negative control shows no measurable ZOI, confirming that the observed effects are not due to the bioactive glass itself, while the positive control inhibited bacterial growth completely, confirming the effectiveness of vancomycin against bacteria. The well-defined circular shape of the ZOI reflects the consistent diffusion of vancomycin from the BG-V, suggesting homogeneity in drug loading and release from the BG-V matrix. Figure 9: Disc diffusion of BG-V against (**a**) _S. aureus_. It has a zone of inhibition of 2.7 \(\pm\) 0.2 cm; (**b**) _E. coli_ with a ZOI of 3.5 \(\pm\) 0.4 cm; (**c**) BG as negative control; and (**d**) vancomycin as positive control. Figure 10 shows the antibacterial efficacy of BG-V, which was evaluated against _S. aureus_ using a time-dependent bacterial killing assay. A bacterial culture of _S. aureus_ (10\({}^{6}\) CFU/mL) was treated with BG-V, and samples were withdrawn at 1 h, 5 h, 24 h, and 48 h for plating. The results show a significant reduction in colony counts for BG-V compared to the control. At 24 h, BG-V showed 1400 colonies compared to 5 \(\times\) 10\({}^{12}\) CFU/mL in control. By 48 h, BG-V exhibited a lower colony count, showing the effectiveness of BG-V in killing bacteria. A one-tailed \(t\)-test (\(\alpha\) = 0.05) comparing the means of the BG-V and control group revealed a statistically significant difference in bacterial growth after 1 h (\(p\) < 0.05). The same test shows that at 5, 24, and 48 h, BG-V treatment resulted in significant log reductions of 3.73, 9.55, and 10.18, respectively (\(p\) < 0.001), demonstrating its antibacterial effect over time. This time-dependent antibacterial activity highlights the potential of BG-V for localized antibiotic delivery in biomedical applications.
## 2 Methodology
### Property Experiment Overview  
In this study, a multi-task and multi-scene experiment utilizing panoramic video was adopted, building on previous research methodologies and informed by an extensive field investigation. The research design was guided by well-established approaches to visual attention and human factors in signage evaluation, integrating panoramic video to replicate realistic scenarios. We carefully selected evaluation variables that take into account the visual characteristics of travelers and introduced a novel evaluation approach for the guidance signage around the hub. Panoramic video was employed as the primary data source, enabling the creation of multi-task comparative experiments designed to reflect real-world conditions in integrated transportation hubs. This approach was selected due to its ability to simulate complex environments and capture pedestrian interactions with signage in a controlled manner. The evaluation framework integrates EWM and TOPSIS, both chosen for their effectiveness in multi-criteria decision-making and their proven application in similar signage evaluation studies.  
The next section provides a detailed description of the research methodology.  
Within comprehensive transportation hubs, the guidance signage is meticulously arranged, and the pathways for traveler flow are varied and abundant. Following field investigations, Pudong Airport was chosen for a preliminary experiment to assess the reliability of the proposed approach. Surveys, virtual reality (VR), and photo/video surveys were employed to capture travelers' visual evaluations of the signage. Ultimately, visual evaluation variables were refined, encompassing visual recognizability, setting situation, information expression, and visual attention.  
The formal experiment comprises both a panoramic video experiment and surveys. Prior to viewing the panoramic video, a pre-experiment survey was administered to collect demographic information and social attributes of the human subjects. Throughout the experiment, participants watched the panoramic videos while equipped with an eye-tracker. Following the experiment, a post-experiment survey was conducted to capture the written thoughts of the human subjects  
The proposed method should be validated for generalizability across various settings. In this study, Shanghai Pudong Airport and Xi'an North Railway Station were chosen for comparative analysis. Both large-scale comprehensive transportation hubs share similar passenger flow patterns. Additionally, their internal guidance signage systems are diverse in both type and function, making them ideal case studies for validation. The experimental design was validated using the EWM and TOPSIS methods. The study compared the significance of various evaluation indicators in integrated hubs across different cities. Through a comprehensive comparative analysis, the research delved into the strengths and limitations of the signage design, ultimately proposing targeted improvement strategies (Figure 1).
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 4 The Proposed ADDP Framework
### Framework Overview
First, the edge allocates resources to the task requests of each master node through FLP according to the received infer table, which is expressed as the decision of the horizontal splitting point of each model. The master node then inputs the infer table into the FSP and decides the number of tasks each device should compute in the cluster based on the received horizontal partitioning policy. The specific task cost is quantified by the size of the data that each device should process. Finally, the input size table required for each device in a single data inference is downlinked to all devices. **Distributed inference**. The free computing resources of terminal devices and edge servers are dynamic, and we applied the FLP computing model by a layer strategy after converging network and resource characteristics. The quantity of data changes dynamically, and the amount of computation per data unit is constant. Applying the feature map partitioning strategy computed by FSP, we achieved the computational partitioning on the end device by balancing the computation per unit of data. Each device divides the data collected by the device according to the input size table issued by the master node. The segmented input data are then transferred to the corresponding device. After a device finishes inference, it uploads the inference results to the edge for subsequent model inference. **Application in Video Stream**. Next, we explain how ADDP works on large-scale video streams with an object detection application as an example. In a smart city, an AI camera deployed with an object detection application captures a live video stream and selects some frames for further detection. For data collection devices in the same region, the FSP module develops a segmentation policy for each frame based on the device's arithmetic power and assigns the segmented patches to the corresponding devices in the region. FLP is responsible for formulating the partitioning strategy of the feature extraction backbone network in target detection, and the master node must choose a model partition point to collaborate with the edge server. The edge server updates the model partition point by gaming with the connected master node. After inference is completed, the object detection results (i.e., object bounding boxes and class labels) are recorded into the video. It is worth noting that to better apply to dynamic video streams, the AI camera and edge server rely on the contextual relationship of the video stream. For example, when new objects or scene changes appear in the video, key frames (containing important targets or events) may appear in the video stream. The presence of key frames increases the density of camera data acquisition, which affects the partitioning settings of data and target detection models.
## 2 Influenced Zone of Deep Excavation on Adjacent Tunnel  
### Numerical Simulation of the Effect of Deep Exavation on Adjacent Tunnel  
#### 2.1.1 FEM Numerical Model  
Studying tunnel displacement behaviors is the basis for dividing the influenced zone of deep excavation on adjacent tunnels. Here, a series of numerical simulations were conducted using finite element software MIDAS GTS NX. Figure 1 shows the 2D FEM numerical model used to study the effect of deep excavation on adjacent tunnel displacement. The supporting structure of the deep excavation adopts a diaphragm wall (abbreviation DW) and horizontal struts. Considering that the excavation depth of the underground 2-story subway station and the underground 3 to 4-story building basement is about 18 m, the excavation depth (\(H_{\text{e}}\)) is set at 18 m, the half-width of excavation is set at 30 m, the retaining wall thickness is set at 0.8 m, and the insertion depth below the base slab (\(H_{\text{i}}\)) is set equal to the excavation depth (\(H_{\text{e}}\)). The first level of horizontal struts is 1 m below the ground surface, and the vertical interval between the other levels of struts is 4.5 m. The existing tunnel outside the pit is a typical subway shield tunnel with a diameter of 6.0 m and a wall thickness of 0.3 m, the horizontal distance from the tunnel center to the excavation boundary is \(L_{\text{t}}\), and the buried depth of the tunnel center is \(H_{\text{t}}\), which are analyzed as variables. The length of the model outside the deep excavation is 120 m, and the depth below the base slab is 54 m, which can ensure that the boundary effect is eliminated.  
In the model, the soil is simulated using a plane strain element, the retaining wall, horizontal struts and existing tunnel are simulated using beam elements, and the contacts of the wall-soil and tunnel-soil are simulated using the interface element. For displacement boundary conditions, the left and right boundaries are limited to the horizontal displacement, the bottom boundary is limited to the horizontal and vertical displacements, and the top boundary is kept free. For load boundary conditions, only the self-weight stress is considered, and no other loads are considered. For water level boundary conditions, the initial groundwater table is 1.5 m below the ground surface, and the pressure head on the groundwater line is 0.
## 2 Materials and Methods
### Deployments

Table 2: Cryologger ITB recorded variables transmitted via Iridium Short Burst Data messages. Figure 4: Examples of suitable iceberg targets instrumented with Cryologger ITBs during the 2018 and 2019 field seasons. Measurements of iceberg dimensions derived from satellite imagery. **(a)** 300434063415110 (270 m \(\times\) 204 m); **(b)** 300434063494100 (490 m \(\times\) 300 m); **(c)** 300434063392070 (600 m \(\times\) 330 m); **(d)** 300434063394110 (760 m \(\times\) 260 m); **(e)** 300434063495310 (700 m \(\times\) 240 m); **(f)** 300234065254740 ice island fragment. Figure 3: Locations and number of Cryologger ITB deployments made in the Canadian Arctic during 2018 and 2019 field campaigns. ITBs were deployed from the CCGS Amundsen in 2018 in Talbot Inlet (2) and the coastal regions of Devon Island (1) and Baffin Island (3). In 2019, ITBs were deployed in Milne Fjord (2) during a scientific field campaign. ITBs were also deployed that year from the CCGS Amundsen on Petermann Glacier (4), near Humbolt Glacier (2) and in Talbot Inlet (2).
## 5 Module I: Anonymity
### Setup Phase
#### 5.2.1 Creating Pseudonyms
Both SNi and SNj will calculate a new random value (ai\(\leftrightarrow\)i) according to Equation (5):  
\[\text{a}_{\text{i}\leftrightarrow\text{j}}=\text{H}_{\text{i}}(\text{IDi} \oplus\text{IDj}) \tag{5}\]  
Both SNi and SNj will calculate also a new pair-wise key ki\(\leftrightarrow\)i according to Equation (6):  
\[\text{k}_{\text{i}\leftrightarrow\text{j}}=\text{H}_{\text{i}}(\text{k}_{ \text{i}\leftrightarrow\text{bs}}\oplus\text{k}_{\text{j}\leftrightarrow \text{bs}}) \tag{6}\]  
SNi also calculates broadcast pseudonym ID for SNj (BPIDj) according to expression Equation (2) since SNi has already received the values of IDj and bj through Mdiscovery. It also calculates the one-hop pseudonym ID (OHPID i\(\leftrightarrow\)j) shared between SNi and SNj as expressed in Equation (7):  
\[\text{OHPIDi}\leftrightarrow\text{j}=\text{H}_{\text{i}}(\text{a}_{\text{i} }\oplus\text{a}_{\text{j}}) \tag{7}\]  
Finally, acknowledgement pseudonym ID for SNi (APIDi) will be calculated according to Equation (8):  
\[\text{APIDi}=\text{H}_{\text{i}}(\text{IDi}) \tag{8}\]  
SNi will create a table (Ti) which contains the shared values with the neighbors as listed in Table 3. In conclusion, we have replaced the ID with _quintuple pseudonyms_ to reference the SN during the communication. \begin{table}
\begin{tabular}{c c} \hline \hline
**Information in Ti Per Each Neighbor** & **Tuple for SNj** \\ \hline Shared random number & ai\(\leftrightarrow\)j \\ Shared broadcast random number & bj \\ Shared fake broadcast random number & cj \\ Shared broadcast key & BPIDj \\ Shared fake broadcast key & FPIDj \\ Shared one-hop key & ki\(\leftrightarrow\)j \\ Current one-hop pseudonym ID & OHPID i\(\leftrightarrow\)j \\ Link direction & linki\(\leftrightarrow\)j \\ Residual energy level & \(\Delta_{\text{j}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Shared values among sensor neighbors. If SNi has Ni neighbors, then Ti will have Ni tuples.
## 1 Introduction
In [30], a DI-SMC method with a constant switching frequency was presented using an analog circuit that does not require an output capacitor current branch. However, the control circuit contains double-PI compensators, which slow down the closed-loop system response, complicate the control circuit structure, and increase the cost of practical implementation. Motivated by the previous research, this paper aims to introduce a robust DI-SMC method with constant switching frequency for a PWM dc-ac buck converter using a simple analog circuit. The main contributions in this research are summarized as follows: (1) A simplified DI-SMC method is proposed for a dc-ac power converter with constant switching frequency. (2) The proposed control scheme contains a single-PI compensator and does not require an output capacitor branch as conventional DI-SMC schemes do. (3) The control law derivation and the controller gains selection, along with the existence and stability conditions, are provided. (4) A detailed design procedure is introduced to realize the analog control circuit with minimal added components. (5) To validate the proposed design methodology and evaluate the tracking performance under large disturbances, the simplified DI-SMC circuit is simulated in Simscape Electrical in MATLAB and compared with a conventional DI-SMC scheme. The rest of the paper is organized as follows. The simplified DI-SMC design approach is given in Section 2. The realization of the analog control circuit is introduced in Section 3. The simulation results are presented in Section 4, and the conclusions are summarized in Section 5.