esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Semantic Dependency Parsing  
Several SDP models have been presented in recent years. Their parsing mechanisms are either transition based or graph based. Most of them are first-order parsers. Wang et al. [13] presented a neural transition-based parser, using a variant of the list-based arc-eager transition algorithm for dependency graph parsing. Lindemann et al. [24] developed a transition-based parser for _Apply-Modify_ dependency parsing. They extended the stack-pointer model to predict transitions. Fernandez-Gonzalez and Gomez-Rodriguez [14] developed a transition-based parser, using the Pointer Network to choose a transition between _Attach-p_ and _Shift_. More recently, there has been a predominance of purely graph-based SDP models. Dozat and Manning [12] extended the LSTM-based syntactic parser of Dozat et al. [25] to train on and generate SDG. Kurita and Sogaard [26] developed a reinforcement learning-based approach that iteratively applied the syntactic dependency parser to build a DAG structure sequentially. Jia et al. [27] presented a semi-supervised model based on the Conditional Random Field Autoencoder to learn a dependency graph parser. He and Choi [28] significantly improved the performance by introducing contextual string embeddings (called Flair).  
Higher-order SDP has received less attention. Martins et al. [15] developed a second-order parser which employed a feature-rich linear model to incorporate first and second-order parts (_arcs_, _siblings_, _grandparent_, and _co-parents_). The \(AD^{3}\) algorithm was employed for approximate decoding. Cao et al. [16] presented a quasi-second-order parser and used a dynamic programming algorithm (called Maximum Subgraph) for exact decoding. Wang et al. [8] extended the parser [12] and managed to add the information about three types of second-order parts (_siblings_, _grandparent_, and _co-parents_) for score computation and then applied either mean-field variational inference or loopy belief propagation for approximate decoding.  
These second-order parsers demonstrated that second-order information could bring significant accuracy gains in SDP. However, the second-order parts used in these second-order parsers are limited to specific types, including _siblings_, _grandparent_, and _co-parents_. Utilizing more types of higher-order parts (e.g., _tri-siblings_, _great-grandparent_, _grand-tri-siblings_, etc.) in SDP still remains under-explored. The reason for this issue is that modeling higher-order parts is non-trivial [8].
## 2 Methods and Techniques
### Neural Network Design
Indeed, the decomposition of a MIMO problem into multiple MISO yields better results and a lower risk of local minima entrapment [41]. The choice of the network size was made on statistical considerations: by testing the NN repeatedly with increasing number of neurons in the hidden layer, the error progressively diminished. However, over a specific number of neurons, the differential increase in performance was negligible, so the last point with relevant increase in performance was taken as the optimal size. In Figure 3, an example of mean and variance for network performance (RMSE) can be seen. A low standard deviation for the chosen point confirms that the performance of the network was not due to chance. The NNs were trained by the Levenberg-Marquardt optimization algorithm. Parts of the results obtained in [15] were used to train the NN, whereas the other results were kept as an independent data set for validation. Hidden neurons use the sigmoid activation function whereas output layer neurons use the linear activation function. The full neural system can be seen in Figure 4.  
Figure 4: Complete neural system for the inverse dynamics. Figure 3: Root Mean Square Error (solid) and Standard Deviation (dashed) Error plot function of the number of neurons (from 1 to 10). For this second step, results obtained using the NN approach were compared to those obtained in the traditional approach considering that both can be considered muscular forces estimators, neither of them being able to provide an exact solution. Indeed neither the former nor the latter can be considered the gold standard. The comparison can thus be performed through a Bland-Altman plot, considering the average between the two estimates as the hypothesized true value.
# Neural Network-Based Identification of Cloud Types from Ground-Based Images of Cloud Layers
## 4 Results and Discussion
Additionally, we found that several cloud genera had a high recall but low precision in Clahe. This finding further supports the conclusion that Clahe is less effective in processing cloud texture features compared to Gamma. From Tables 4 and 5, it can be observed that the overall performance of the Sobel dataset is \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Recall** & **Precision** & **F1** & **Accuracy** \\ \hline AC & 0.75 & 0.83 & 0.79 & \\ AS & 0.8 & 0.64 & 0.71 & \\ CB & 0.85 & 0.81 & 0.83 & \\ CC & 1 & 0.77 & 0.87 & \\ CI & 0.6 & 1 & 0.75 & \\ CS & 0.5 & 0.67 & 0.57 & \\ CU & 0.9 & 0.67 & 0.77 & \\ NS & 0.8 & 0.76 & 0.78 & \\ SC & 0.75 & 0.94 & 0.83 & \\ ST & 0.85 & 0.89 & 0.87 & \\ \hline Stratiformis & 0.675 & 0.74 & 0.71 & \\ Undulatus & 0.675 & 0.68 & 0.68 & 0.71 \\ Cumulus & 0.85 & 0.71 & 0.77 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of Gamma datasets with multiple classification methods. similar to that of the Canny dataset, with both performing worse than Clahe. We found that after processing with Canny, the model struggled to identify Stratus (St) and was likely to incorrectly classify cloud genera as Nimbsostratus (Ns). Furthermore, Canny performed worse in classifying cloud genera based on texture, with an accuracy of only 54%. It struggled to differentiate Cumulus from the other two categories. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Recall** & **Precision** & **F1** & **Accuracy** \\ \hline AC & 0.65 & 1 & 0.79 & \\ AS & 0.8 & 0.5 & 0.62 & \\ CB & 0.85 & 0.77 & 0.81 & \\ CC & 0.7 & 1 & 0.82 & \\ CI & 0.75 & 0.56 & 0.64 & \\ CS & 0.6 & 0.92 & 0.73 & \\ CU & 0.8 & 0.1 & 0.86 & \\ NS & 0.95 & 0.95 & 0.89 & \\ SC & 0.75 & 1 & 0.88 & \\ ST & 0.85 & 0.57 & 0.68 & \\ \hline Stratiformis & 0.72 & 0.63 & 0.67 & \\ Undulatus & 0.54 & 0.61 & 0.57 & \\ Cumulus & 0.65 & 0.68 & 0.67 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of Sobel datasets with multiple classification methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **Recall** & **Precision** & **F1** & **Accuracy** \\ \hline AC & 0.9 & 0.6 & 0.72 & \\ AS & 0.7 & 0.74 & 0.72 & \\ CB & 0.8 & 0.9 & 0.85 & \\ CC & 0.95 & 0.9 & 0.93 & \\ CI & 0.55 & 0.85 & 0.67 & \\ CS & 0.95 & 1 & 0.97 & \\ CU & 0.9 & 0.67 & 0.77 & \\ NS & 0.65 & 0.8 & 0.72 & \\ SC & 0.85 & 0.65 & 0.74 & \\ ST & 0.95 & 0.70 & 0.83 & \\ \hline Stratiformis & 0.54 & 0.77 & 0.63 & \\ Undulatus & 0.77 & 0.63 & 0.69 & \\ Cumulus & 0.78 & 0.76 & 0.77 & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of Clahe datasets with multiple classification methods. Figure 9: The situation of Cirrus (Cs) labeled samples under 20 times performed by the model when using Gamma dataset.
# Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm
## 5 Validation and Results
### Skoltech Anomaly Benchmark Dataset  
With the goal of comparing CDCA with other anomaly detection algorithms, the dataset (_SKAB_) [44] was used. The development of _SKAB_ is based on sensor readings deployed in a water circulation system from an IoT testbed, located in the Skolkovo Institute of Science and Technology (Sklotech). The testbed used to build the dataset is shown in Figure 10.  
Anomalies are introduced by inserting different physical faults in the system, such as closing valves, increases in temperature, and rotor imbalances. The testbed is composed of IoT sensors that communicate using the OPC Unified Architecture (OPC UA) protocol and whose data is stored in a database (from which the dataset is built). The dataset comprises 34 different files, each representing a specific anomaly. Each data point is real valued and consists of a sensor reading, such as current, pressure, temperature, and volume flow rates of the circulating fluid. All anomalies are collective, meaning the anomalous points are contiguous.  
There are ten original features in the dataset. However, since we are treating this problem as a time series, the authors derived other features, such as moving average, derivative and absolute derivative, the difference from moving average and squared distance from moving average, in a total of 26 features.
## 1 Introduction  
Satellites equipped with Synthetic Aperture Radar (SAR) use a technique that creates images based on radio waves reflected from the ground, enabling them to perform reconnaissance missions more efficiently than Electro-Optical/Infrared (EO/IR) satellites. SAR satellites must be stored in the limited volume of launch vehicles, and the performance of both the launch and the satellite mission operations must be considered. Given these weight and performance requirements, reflector antennas with deployable structures are being developed and deployed in advanced countries, as well [1; 2; 3; 4; 5; 6; 7; 8; 9; 10].  
Deployable reflector antennas must be designed to ensure thermal and structural stability, taking into account both the launch and orbital environments. The main reflector, a key component that accounts for a large proportion of the antenna's total weight, consists of 24 individual reflectors and significantly influences the satellite's performance. The initial design of the composite main reflector was confirmed based on the design and analysis of the unit reflectors and performance analysis of the system model, which includes all the antenna components [11; 12; 13; 14; 15].  
In this paper, in order to secure a method for reducing the weight of a satellite, a case study of a composite main reflector was conducted to design and develop it, and a study was conducted to develop a composite main reflector that is lighter and that can secure lightweight performance through development.  
Composite structures exhibit varying performances depending on the manufacturing process, making the selection of process variables and performance verification essential for developing composite structures suitable for deployable reflector antennas. In this study, (1) four types of main reflectors were manufactured using Carbon Fiber Reinforced Plastic (CFRP) and honeycomb sandwich panels, each corresponding to different manufacturing methods, (2) the performance of each structure was evaluated based on weight, mass properties, dynamic stiffness, and surface shape accuracy, and (3) a composite reflector suitable for SAR antennas was developed.
## 3 Material and Methods
### Feature Extraction  
In this study, our interests lie in the spatial and visual experience along the plot, and SVIs are not accessible in private plots. Therefore, we focused on the encoding of spatial characteristics of the plots along the street to interrelate the geometric features with the visual features from the SVIs. Taking into consideration the spatial feature at one observing point in the street and the spatial sequences of walking along the street, we distribute observing points along the AOI boundary at each corner and every 60 m at the edge. Visibility is an important factor to describe a space because it shows people's perception of the space when standing at a specific point. Therefore, the isovist, which is usually used for evaluating visibility, can be applied for encoding the spatial features, the numbers of which can be the input of clustering. For each observing point, 32 rays are distributed and 32 ray distances (RD) from the point to the first obstacles are stored as the geometric feature vectors. Building footprints within a 100 m radius circle around the target plot are included for the measurements. Figure 4 shows an example of observers, rays and spatial radial chart of a plot.  
The elements in an SVI are indicators of visual appearance and perception, so the semantic segmentation technique is applied to the SVIs. We implemented the semantic segmentation for the SVIs by a pre-trained model \"Ademxapp Model A1 Trained on Cityscapes Data\" on the Mathematica platform. Nineteen elements including road, sidewalk, building,  
Figure 4: Allocation of the observers and the corresponding rays on a plot.  
Figure 3: Examples of the samples in the dataset, include satellite images, master plans, and SVIs.  
wall, fence, pole, traffic light, traffic sign, vegetation, terrain, sky, person, rider, car, truck, bus, train, motorcycle, and bicycle are labeled in each image. The images are resized to 300 \(\times\) 300 pixels. Each pixel is labeled as an index for the element after the pixel-based training. Based on the labeling, we can visualize the mask of segments for each SVI and evaluate the percentage of each element in the image. The elements' percentage values after normalization are used as imagery feature vectors. In another word, the feature vectors for SVIs are reduced to only nineteen dimensions. Figure 5 shows scatterplots for the percentage of each element in the images. Figure 6 shows the colored visualization of each element in the images.
## 5 Case Study
### Results
#### 5.3.2 Results of the Use of the Web Application  
The two groups of students performed the configuration of the assigned data network equipment in a connectivity laboratory, which is designed for these practices. Each student was timed on how long they took to perform the configuration in the traditional way (CLI) and with the automation application (Web App). The results are shown in Figures 8 and 9.  
Table 4 shows the average time obtained in the configuration of the communication equipment using the CLI and the automation application. As can be seen, in simulation 1 (Figure 4) the average time it takes a student to configure the equipment of the proposed network using the CLI is 38 min versus the 29 min it takes to do the same job using the web application. Similarly, the configuration time with CLI is 53.7 min versus 37.2 min with the application in simulation 2 (Figure 5). The use of the automation application saves 11 and 16.5 min in the configuration of equipment in simulations 1 and 2, respectively.  
Based on the results, it can be stated that teaching the configuration of networking equipment using automation can make the time required for this work more efficient.
## 1 Introduction  
The traditional target detection approach is called track-after-detect (TAD); it involves the application of a detection threshold at every scan, which leads to a loss of information, as some sensor responses my fail to pass the detection threshold. Additionally, TAD methods cannot detect the targets with a low signal-to-noise (SNR) under 10 dB [1]. An alternative approach is called track-before-detect (TBD) [2],which jointly processes the raw sensor data (infrared sensor, radar, passive sonar, _etc._) without a threshold and jointly declares the presence of a target and its track. This is acceptable if the SNR is low.  
A large number of TBD algorithms have been developed in the past several decades including direct maximum likelihood [3], the Hough transform [4], dynamic programming [5,6], and so on. These methods generally require discretization of the state space and are very computationally intensive. Recently, the popularity of the particle filter (PF) method has increased due to its flexibility to handle cases where the dynamic and observation models are non-linear and/or non-Gaussian [7]. Particle filter track-before-detect (PF-TBD) was first proposed by [PERSON] [8], in parallel with the work by [PERSON] [9]. These algorithms have since been extended in [10, 11, 12], which form a single target filter for two targets. The literature [10] presents a performance comparison of three particle filters using several different particle proposal densities designed for a single target. In [11], a new model for solving the problem of detecting a single target arrival and tracking its state in a TBD context is presented. The work in [12] shows a modeling setup and algorithm for a multiple target recursive Bayesian TBD application in a radar context. Phase information is used to improve the PF-TBD algorithm in the literature [13]. In [14], the PF is replaced by a cost-reference particle filter (CRPF). However, these proposals have some disadvantages in terms of computational complexity.  
In this paper, an improved PF-TBD algorithm is proposed, which exploits a novel particle updating strategy that regards the particle updating process as an optimization problem using the hybrid differential evolution (HDE) algorithm [15,16] before the resampling process. It can be drawn from this strategy that we can reduce effectively the particle impoverishment and optimize the performance of weak target detection. Simulation results are presented to demonstrate the validity of the algorithm.  
The remainder of this paper is organized as follows. The TBD system model is introduced in Section 2. In Section 3, first of all, we introduce the Bayesian PF-TBD approach and its weaknesses. Then, the HDE algorithm is given, and an improved PF-TBD algorithm is proposed. We evaluate the performance of the proposed algorithm in Section 4. At last, conclusions and pointers for future research are presented in Section 5.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Short-Range Static Communications
#### 7.3.3 Near Field Communication (NFC)  
A key technology of the IoV is NFC to perform short-distance communication in a secure and easy way between devices [103]. Applications of this technology extend to nearly all aspects of vehicular connectivity, with the use cases including contactless payments, where the drivers and the passengers are enabled with a safe, cashless, and seamless transaction. In terms of vehicles, NFC makes it possible for easy device pairing for smartphones, tablets, or other compatible devices with the car system. NFC's simplicity, security, and versatility have led to its popularity for driving a connected, user-friendly vehicle experience in today's modern vehicles.  
These wireless technologies act as the communication backbone in IoV applications, enabling an interconnected transport ecosystem. Finally, these technologies are integrated to support safety, efficiency, and innovative services that are helping to shape smart and connected vehicles in the context of larger ITS.  
Open Research Challenges, Concerns, and Research Directions in Internet of Vehicles in Smart Cities  
One of the most challenging problems within the IoV ecosystem is the integration between all components and the ability to communicate between objects. In order to meet this demand, many researchers have proposed IoV architectures with structural, layered designs [104; 105; 106; 107; 108; 109; 110; 111]. The following are a few important open research challenges as depicted in Figure 8.
# SCoPE: Service Composition and Personalization Environment
## 3 Background and Literature Review  
In this section, a number of key concepts, relevant for service personalization in elderly care ecosystems are briefly presented, such as business services, collaborative networks, collaborative business ecosystem, and service composition and personalization. **Business Services**: the concept of business service has been developed mainly in two areas: computer science and management. For the Information and Communication Technology (ICT) community, a software or technical service represents a computational action executed in response to a trigger event. On the other hand, from the business perspective, business services add value to a customer [16; 17; 18]. According to the same authors, it is logical to separate these two views, being worth noting that business service delivery is performed through business process execution. The activities of a business process can be done automatically (invocation of some software service) or manually (human-executed activities). Nevertheless, the concept of business service itself is not clearly defined, and different authors offer slightly different notions. Table 1 summarizes some partial definitions from the literature. From the definitions presented in Table 1, it can be inferred that care and assistance services for elderly can be considered as a kind of business service. The notion of business process corresponds to the management of services execution, involving in this context both software services and or manual services. In Figure 1, software services fundamentally represent software applications that define part of a system which can be \"consumed\" distinctly by numerous objects [24]. Thus, software services execute elements that perform business processes activities. Manual services correspond to services provided to the customers which involve some human interventions [25]. Hence software services and manual services support the business process execution, materializing the notion of business service delivered to the customers [24]. Many initiatives on services for elderly care have been mostly focused on isolated services development, considering a single service provider and often presenting an extreme techno-centric idea. However, through collaboration to overcome their weaknesses and strengthen their expertise, companies can more efficiently respond to current challenges and deliver better integrated services, resulting in the acquisition of a competitive advantage [11; 26; 27]. Additionally, in the elderly care domain, personalized services should cover the specific needs of each user, respecting the elderly individuality, and the evolution of limitations that come as the person and life environment change [17; 28; 29].