esa-sceva/satcom-chunk-collection huggingface dataset:
# Occlusion Vehicle Target Recognition Method Based on Component Model
## 2 Occlusion Vehicle Target Segmentation and Recognition Scheme Design
### Design of Vehicle Component Feature Mask Module Based on Swin Transformer
#### 2.2.4 Component Mask Recognition Unit  
In the face of the problem of foreground occlusion, such as mutual occlusion between vehicle targets and occlusion caused by non-target objects, it will affect the recognition of target vehicles. Therefore, to improve the recognition accuracy of the target vehicle under partial occlusion conditions, according to the extracted vehicle component features, a Mask Decoder is introduced to remove the damaged areas that cannot be classified as vehicle components. The component mask recognition (CMR) unit is designed to determine whether these component features belong to the vehicle component features, and finally, the vehicle is identified. The overall structure of the unit is shown in Figure 7.  
In Figure 7, the CMR unit is composed of a multi-scale feature extraction block and a recognition block. Firstly, it uses the mid-deep features \(F^{\prime}_{1}\), \(F^{\prime}_{2}\) to capture local and global information from the vehicle component feature input module obtained above. Then, it is sent to the Mask Decoder for accurate learning of feature masks. The expression equations of \(F^{\prime}_{1}\) and \(F^{\prime}_{2}\) are as follows:  
\[F^{\prime}_{1}=conv(upsample(conv(F^{\prime}_{1}))+upsample(conv(F^{\prime}_{2 }))) \tag{10}\]  
\[F^{\prime}_{2}=conv(upsample(conv(F^{\prime}_{2}))+upsample(conv(F^{\prime}_{ 3}))) \tag{11}\]  
Figure 6: Efficient attention unit.  
Figure 7: Component mask recognition unit.  
The Mask Decoder structure is like an attention mechanism module. It allows the net to learn an appropriate weight matrix through a convolution operation to remove the features of occlusion damage in the feature map. The structure of the Mask Decoder is shown in Figure 8.  
The Mask Decoder structure decodes the occlusion information in the obtained feature map into a feature mask. It is expected to shield the occlusion damage feature elements of \(F_{4}^{\prime}\) by an element-by-element product to generate feature \(X_{1}^{\prime}\) for subsequent recognition.
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 3 Preliminaries
### RSA Signature Scheme  
RSA digital signature, a mature and popular signature scheme, has been used in many studies. The key point of RSA digital signature is to use the characteristics of asymmetric encryption; that is, the private key is used to encrypt a message (i.e., forming signature) and the public key is used to decrypt the encrypted message (i.e., verifying signature). There are three algorithms in a RSA digital signature scheme, i.e., _KeyGen_, _Sig_, and _Ver_. The description of algorithms is as follows.  
Figure 1: Grid structure and associated grids.  
1. _KeyGen_ is a key pair generation algorithm. Firstly, select two large prime numbers \(p\) and \(q\). Then, compute \(N\ =\ p\times q\) and choose \(e\) so as to \(1<e<\lambda(N)\) and \(gcd(e,\lambda(N)\ =\ 1)\), where \(\lambda(N)\ =\ (p-1)(q-1)\) and \(gcd\) is the greatest common divisor. Finally, compute \(d\ =\ e^{-1}(mod(\lambda(N)))\) and then \((N,e)\) is defined as the public key and \((N,d)\) is defined as the private key.
2. _Sig_ is a signing algorithm. Suppose \(M\) is the message to be signed, then the ciphertext \(C\ =\ M^{e}(modN)\) is the signature that will be sent and used for verification.
3. _Ver_ is a verification algorithm. Given a signature \(C\), if the decrypted \(M^{\prime}\ =\ C^{d}(modN)\) is the same as the original message \(M\), then the \(C\) can be accepted as a valid signature.
## 4 Diverse Model Stacking Ensemble
### Zero-Shot Classification  
Zeros-shot classification allows a model to classify texts into a predefined set of classes, even if those classes were not seen during the model's training process. It enables a model to generalize its understanding of text and make predictions for classes it has never encountered before [74]. The goal of zero-shot text classification is to use a pretrained model without further fine-tuning on any task-specific dataset. One strategy to accomplish this objective is to employ carefully crafted prompts alongside a pretrained model that has expertise with a task more closely related to the one being addressed [79].  
Zeros-shot emotion recognition can be addressed by adapting a model that has been fine-tuned on a task close to text classification, such as text entailment [79]. In text entailment, the model needs to determine whether two text passages are likely to follow or contradict each other [80]. Indeed, the models are typically trained to detect entailments and contradictions with natural language inference (NLI) datasets such as the Multi-Genre NLI (MNLI) dataset [81] or the Cross-Lingual NLI (XNLI) dataset [80]. Each example in these datasets is composed of three elements: a premise, a hypothesis, and a label. The label can be one of three classes: _entailment_, _neutral_, and _contradiction_. The entailment label is used when the hypothesis text is true under the premise. The contradiction label is assigned when the hypothesis is false or inappropriate under the premise. The neutral label is used if neither of these cases is applicable. Tables 4 and 5 present examples of text entailment with three classes in English and Arabic, taken from MNLI and XNLI, respectively.  
The key idea in order to build an emotion recognizer using a model trained on the MNLI or XNLI datasets without requiring any labeled data is to treat the text to be classified as the premise and then formulate the hypothesis template as:  
_This text is [label]_.  
Instead of _label_ in the default hypothesis template mentioned above, one can insert the emotion class name. The entailment score is used to determine how likely that premise is to be about that class. This process can be run for any number of classes sequentially. For Arabic emotion recognition, we translated the aforementioned hypothesis template into Arabic.
## 4 Methodology
### General Architecture
#### 4.1.2 Creating Multi-Path Circuits  
To meet strict time constraints, and performance overheads in some cases, the user pre-establishes an initial three-hop circuit by selecting three different relays from the set of entry ORs, exit ORs, and middle ORs, respectively, through a bandwidth-weighting algorithm [2]. Then, the user's OP creates additional **m-1** two-hop subcircuits, which, with the initial three-hop circuit, share the same middle OR. Each of them constitutes one of the multi-path transmission schemes. Once the **m-1** subcircuits are successfully established, the anonymous multi-path can transmit data. It allows traffic to reach different entry ORs along the **m** subcircuits from the client and converge at a shared middle OR. The entry ORs serve as the splitting point and the middle OR serves as the reorganization point, and the recovered data leads to the destination via the exit OR.
# A Comparison of Energy Management System for a DC Microgrid
## 2 Mathematical Modeling for DC Microgrid Components
### Batteries  
The battery banks are one of the main energy storage systems and it can be used to store energy from renewable generation sources connected to the microgrid. Unlike renewable sources of generation, these elements can be considered as dispatchable energy sources because stored energy can be used in a controlled manner. The battery pack can function in the charge or discharge mode to give or absorb a net amount of active energy, at their connection point. This allows us to manage the level of energy stored in them. The level of stored energy is reflected through the state of charge (SOC). Considering that the bank is made up of j, the j-th battery can then be represented by means of two active power generation sources, as illustrated in Figure 1[5]. One of them represents the charging power of the j battery at a stage i of time t, and the other the discharge power of the j battery at a stage i of time t, the sum of both powers represents the net power of the j battery in a stage i of time t that belongs to a set of stages, contributed or consumed by the battery in its connection node.  
\[p_{Snj}^{t_{i}}=p_{Scj}^{t_{i}}+p_{Sdj}^{t_{i}};0\leq p_{Sdj}^{t_{i}};\,p_{Scj }^{t_{i}}\leq 0;\forall t_{i}\in T \tag{1}\]  
The decision variables the storage system are \(V_{j}^{t_{i}}\) and \(\left[p_{Sdj}^{t_{i}},p_{Scj}^{t_{i}}\right]\in x_{S}\left(\forall t_{i}\in T\right)\) being \(x_{S}\) the batteries variables, while \(\left[V_{j}^{t_{i}}\right]\in x_{R}\left(\forall t_{i}\in T\right)\) The state of charge (SOC) of the \(j\) battery in the stage \(t_{i}\)\(SOC_{S\ j}^{t_{i}}\) can be calculated by means of Equation (2).  
\[soc_{sj}^{t_{i}}=soc_{Sj}^{t_{0}}-\frac{\varepsilon_{cj}\Delta t}{E_{snom\ j}} \sum_{t=1}^{t_{i}}p_{Scj}^{t}-\frac{\Delta t}{E_{snom\ j}\varepsilon_{dj}} \sum_{t=1}^{t_{i}}p_{Sdj}^{t} \tag{2}\]  
Figure 1: Battery model.
## 7 Conclusions  
We have demonstrated a simple, easy-to-use, and fast algorithm for coverage path planning for non-destructive testing of panels used in aerospace applications using camera viewpoints. This uses a geometric method relying on generating nodes on the surface using geodesic path-finding, and it gives good results for representative sample panels used in the aerospace industry. The semi-automated approach relies on the user to set the start criteria, and two alternative approaches for setting node propagation stoppage criteria provide versatility and efficiency for covering the entire surface. This algorithm is much simpler than any optimization-based method and more suitable to be implemented in real-world applications.  
Future work for this would include implementing the geodesic path-finding algorithm using a half-edge data structure for the mesh in order to make it more robust and computationally efficient. We could also calculate and demonstrate the actual coverage area on the panel for greater benefit to the user.  
Figure 34: Linking paths from multiple start points.  
Figure 33: Boustrophedon Path for linking viewpoints.  
A further future scope of work would be to rigorously analyze the spatio-temporal complexity of the algorithm. An alternative method using an optimization and heuristic-based approach could also be implemented to solve the same problem of panel scanning. Metric comparatives could then be generated to qualitatively assess the performance of geometric methods and optimization methods.  
Conceptualization, S.C.; Software, S.C.; Investigation, K.K.I.; Writing--original draft, S.C.; Visualization, S.C.; Supervision, K.K.I.; Project administration, K.K.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
We acknowledge the help from Vashishtha Research Private Limited, Thiruvananthapuram for their aid in developing the 3D viewers used in this project. We also acknowledge the help received from the Indian Space Research Organization (ISRO) for supplying the panels used for testing the algorithms.  
Author [PERSON] is founder of Company 'Vashishtha Research Private Limited, Thiruvananthapuram'. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results  
### Data Source  
The photovoltaic dataset used in this study was provided by the Desert Knowledge Australia Solar Centre (DKASC). This region hosts numerous photovoltaic power stations, each with its unique set of data records. The selected dataset collects measured data on power and various meteorological factors from January 2020 to December 2020, including global radiation, rainfall, humidity, ambient temperature, and wind direction. These data are crucial for deeply understanding the relationship between photovoltaic power output and meteorological conditions. Specifically, the global radiation data reflect solar radiation, which is one of the primary factors influencing photovoltaic power generation. During the experiment, the dataset was divided into training, validation, and test sets at a ratio of 8:1:1. As photovoltaic components significantly reduce power output during early morning and late afternoon, with most of the time having zero or near-zero power, the original data had a resolution of 5 min. Therefore, the prediction time was chosen to be from 7:00 a.m. to 6:00 p.m. each day, with a total of 133 sampling points as experimental samples. The annual distribution of output power data for selected PV station is shown in Figure 7.  
In this study, the K-means++ algorithm was employed to partition the historical photovoltaic dataset. To enhance the effectiveness of data partitioning, each day was treated as a sample, and for each sample, the standard deviation \(\sigma\) of the global radiation, relative humidity, and temperature, which are three meteorological features, were calculated. Additionally, the skewness coefficient \(k_{ur}\) and the mean value \(\overline{x}\) were computed. These computed values were then used to form a feature vector for clustering purposes. The historical photovoltaic data were categorized into three classes: sunny, cloudy, and rainy/snowy weather conditions. The results of the data partitioning using the K-means++ algorithm are presented in Figure 8. This chart clearly displays the data distribution under different weather conditions, providing intuitive visual support for our analysis. Among these, there are 136 days of sunny weather, 133 days of cloudy weather, and 93 days of rainy/snowy weather.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 3 Performance Evaluation
### Traffic Engineering Performance  
Objective: We conducted another set of experiments to evaluate the traffic engineering performance of the proposed SAC algorithm by looking at the energy frugality resulting from the distributed signaling process and the routing efficiency of the centralized clustering process. The energy frugality of our distributed signaling model using the LIBP-based signaling was compared to those of the RPL- and CTP-based signaling in terms of the average energy consumption and the radio duty cycle. The routing efficiency resulting from the centralized clustering process was measured by looking at the end-to-end packet delays and the packet delivery ratio resulting from running the different algorithms.  
Network: The energy consumption was measured using different network sizes ranging from 10-100 nodes, while the radio duty cycle was considered for a 100-node network.  
Results: The results depicted by Figure 15 reveal the energy frugality of the LIBP-based signaling model compared to CTP and RPL in terms of energy consumption and radio duty cycle. The routing  
Figure 14: Scalability of the SAC algorithm. (**a**) Number of nodes alive; (**b**) Optimal number of clusters.  
performance revealed by Figure 16 also showed that SAC outperforms DECSA and MOCRN in terms of end-to-end packet delay and the packet delivery ratio.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 6 Use Cases
### SWE Bridge Performance  
As discussed in Section 2.1, many observation platforms present severe constraints in terms of power supply and computational resources. Although the SWE Bridge software is not performing any computationally expensive operations, it makes extensive use of dynamic memory due to the arbitrariness of SensorML documents. In this section the assessment of the SWE Bridge performance is presented when interfacing the sensors deployed in NeXOS, EMSODEV and INTMARSIS projects. In order to obtain comparable results, all tests were performed in a Raspberry Pi single board computer  
Figure 23: INTMARSIS System acquisition chain. Virtual instruments are depicted as purple components.  
(specifications shown in Table 2), acting as a host controller for the SWE Bridge. The performance was assessed using the Massif heap memory profiler [51].  
The execution of the SWE Bridge can be classified in two differentiated stages: setup (which includes the components _OGC PUCK Extractor_, _EXI Decoder_ and _SDF Interpreter_) and operation (_Mission Scheduler_). During the setup process the SDF is decoded and interpreted, which produces a peak in the use of dynamic memory. Once the setup is finished, a significant amount of memory is freed and the use of dynamic memory is maintained low and constant during the operation stage. This behaviour can be observed Figure 24, where the first 30 seconds of a time-based memory profile is depicted.  
The amount of memory used at the stationary stage is mainly dependant on the nature of the sensor response and the mission complexity. Large responses require larger buffers and each process within the SWE Bridge also increases the usage of dynamic memory. The peak usage of dynamic memory as well as the average values in stationary phase are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline Processor Architecture & ARM 7 \\ Processor Speed & 900 MHz \\ Number of Cores & 4 \\ RAM memory & 1 GB \\ Operating System & Raspbian Jessie Lite (version July 2017) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Raspberry Pi 2 specifications.  
Figure 24: SWE Bridge time-based memory profile when interfacing sensors from NeXOS, EMSODEV and INTMARSIS projects. The peak usage of heap memory is registered when decoding the SDF file. Afterwards, during the operation stage, the memory consumption is kept constant.  
The overall computational load, measured in kilo instructions per second (KIPS), is mainly dependant on the sensor's communications protocol, data stream period and response length. Sensors using TCP/UDP protocols increment significantly the computational load when compared to serial sensors. Moreover, sensors with short periods of data stream with large responses present the higher usage of CPU (i.e., SBE54 and Aanderaa 4831).
# Ultrasound Navigation for Transcatheter Aortic Stent Deployment Using Global and Local Information
## 1 Introduction
This is because in endovascular navigation, single US images cannot provide a 3D context of the surgical site and high-resolution morphological information of the aorta. Intravascular ultrasound (IVUS) is an attractive complement for common US or preoperative CT images, because IVUS obtains 2D images of the cross-sections of the blood vessels, providing information on vessel morphology for diagnosis and surgery [16]. IVUS imaging is more accurate than conventional angiography because of the relatively higher resolution [17]. Therefore, the approach proposed in this paper is to combine IVUS images with common US images in order to implement a radiation-free 3D guidance system for transcatheter aortic stent deployment. The work described here expands on our previous study [18]. The major limitations of our previous study are that: (1) the accuracies of catheter tracking and IVUS segmentation need to be improved; (2) the verification experiments of US navigation method with global and local information need to be increased. To solve these limitations, the new system not only integrates more accurate methods of tracking and segmentation into previous system but also presents more evaluations experiments and better results.
## 1 Introduction
[10] introduced a strategy that combines banning and warning signals to mitigate accident-induced congestion. However, a key limitation of this approach is that warning signals alone do not ensure high compliance among drivers. [PERSON] et al. [17] addressed this issue by applying only the ban signal in conjunction with the CTM to analyze traffic congestion. Moreover, a variety of methodologies [21; 22; 23; 24; 25; 26; 27; 28] have been proposed to reduce traffic flow. A review of previous studies suggests that the CTM is well-suited for simulating the formation and dissipation of traffic jams in city transportation systems. However, based on the contributions of CTM-related research [16; 17; 27; 28], two unreasonable phenomena have been identified. In this study, we aim to refine the CTM by adjusting key parameters, including the number of downstream cells and their vehicle capacity, to address these issues. Our study primarily follows the conventional CTM approach by dividing each link into nine cells. However, we refine the model by further dividing only cell 9 into three smaller cells, each representing a distinct movement direction (e.g., left-turn, though, and right-turn). This adjustment allows for a more accurate representation of traffic dynamics at intersections while maintaining consistency with the standard nine-cell structure elsewhere in the network. The choice ensures a balance between computational efficiency and the ability to capture critical congestion patterns at intersections. This study examines the utilization of the CTM in traffic control strategies aimed at dispersing congestion caused by accidents and assesses their effectiveness compared to existing methods. The rest of this paper is structured as follows: Section 2 succinctly defines the CTM. Section 3 identifies challenges associated with the conventional CTM approach. Section 4 details the simulation of traffic congestion propagation and dissipation within an 8 \(\times\) 8 two-way grid traffic network. Finally, Section 5 presents the conclusions of this study.