esa-sceva/satcom-chunk-collection huggingface dataset:
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
###### Abstract  
All multimedia services must be accessible. Accessibility for multimedia content is typically provided by means of access services, of which subtitling is likely the most widespread approach. To date, numerous recommendations and solutions for subtitling classical 2D audiovisual services have been proposed. Similarly, recent efforts have been devoted to devising adequate subtitling solutions for VR360 video content. This paper, for the first time, extends the existing approaches to address the challenges remaining for efficiently subtitling 3D Virtual Reality (VR) content by exploring two key requirements: presentation modes and guiding methods. By leveraging insights from earlier work on VR360 content, this paper proposes novel presentation modes and guiding methods, to not only provide the freedom to explore omnidirectional scenes, but also to address the additional specificities of 3D VR compared to VR360 content: depth, 6 Degrees of Freedom (6 DoF), and viewing perspectives. The obtained results prove that always-visible subtitles and a novel proposed comic-style presentation mode are significantly more appropriate than state-of-the-art fixed-positioned subtitles, particularly in terms of immersion, ease and comfort of reading, and identification of speakers, when applied to professional pieces of content with limited displacement of speakers and limited 6 DoF (i.e., users are not expected to navigate around the virtual environment). Similarly, even in such limited movement scenarios, the results show that the use of indicators (arrows), as a guiding method, is well received. Overall, the paper provides relevant insights and paves the way for efficiently subtitling 3D VR content.  
accessibility; guiding methods; immersive media; subtitling; virtual reality +
Footnote †: journal: Applied  
1
## 2 Methodology
### Linear State Estimation (LSE)  
The SOTA LSE considers the entire power system to be a single entity. The formulation of the LSE described in [11] is considered for the network level as a redundancy layer for the D-LSE and to compare with substation-level and area-level estimation. The current  
Figure 1: Distributed linear state estimation (D-LSE) integrated with the state-of-the-art (SOTA) linear state estimation (LSE). The ’orange-line’ blocks present the contribution of this work.  
measurement bus incidence matrix (\(A\)) presents the current flow measurement's location in the network, where the TNTP is used to update. \(A\) is an \(m\) by \(b\)-sized matrix, where \(m\) represents the number of current measurements in the network, and \(b\) is the number of buses that have current measurements leaving the selected bus. The voltage measurement bus incidence matrix (\(II\)) is similar to the \(A\), where the TNTP is used to update. It presents the relationship between a voltage measurement and its respective location in the network. It is an \(n\) by \(d\) matrix, where \(n\) represents the number of voltage measurements in the network, and \(d\) is the number of buses with voltage measurements. The series admittance matrix (\(y\)) is a diagonal matrix, where the diagonal elements are the measured admittance of the respective lines. It is \(k\) by \(k\)-sized matrix, where \(k\) represents the number of branch current measurements in the network. The shunt admittance matrix (\(y_{s}\)) relates the location of each current measurement to the shunt admittance of the line it measures. It is an \(l\) by \(g\) matrix, where \(l\) is the number of current measurements in the network, and \(g\) is the number of buses where the current is being originally measured. The intermediate characteristics matrix \([B]\) can be formed with \(A\), \(II\), \(y\), and \(y_{s}\) using (1).  
\[[B]=\left[\begin{array}{c}II\\ yA+y_{s}\end{array}\right] \tag{1}\]  
The linear relationship between measurements and the states can be formulated using (2). \([r]\) is referred to as the measurement residue vector.  
\[[z]=[B][x]+[r] \tag{2}\]  
Considering that the measurements contain noise, the covariance matrix (\(W\)) appears in the solution. Then, the solution can be found using (3).  
\[[x]=\left[\left(B^{T}W^{-1}B\right)^{-1}B^{T}W^{-1}\right][z]=[H][z] \tag{3}\]
# A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications
## 2 Related Work
### Access Methods  
Hierarchical trees are among the most used access methods. In general, spatial access methods depend on object grouping or space splitting techniques. R-tree [3] and its variants, such as R*-tree [4] and R\({}^{+}\)-tree [5], depend on grouping the objects in a minimum bounding rectangle (MBR) in a hierarchical manner. Simple grid, k-d-tree [6], and its variants (e.g., k-d-B-Tree [7] and Quadtree [8]) depend on space-splitting instead. In this method, the overlapped objects are duplicated or trimmed. Otherwise, an overlapping enlargement is enforced.  
However, a moving object trajectory is an example of time-series spatial data. As a result, many versions of the previous structures had to be adapted for moving object trajectories. These structures can be grouped into augmented multidimensional indexes or multi-version structure indexes. Augmented multidimensional indexes can be built using any of the previous hierarchical indexes (in practice, mostly R-trees) with augmentation on the temporal dimension, as seen in Spatio-Temporal R-tree and Trajectory-Bundle tree (TB-tree) [9]. A Spatio-Temporal R-tree keeps segments of a trajectory close to each other, while a TB-tree ensures that the leaf node only contains segments belonging to the same trajectory, meaning that the whole trajectory can be retrieved by linking those leaf nodes together. On the other hand, multi-version indexes, such as Historical R-tree (HR-tree) [10], rely mostly on R-trees to index each timestamp frame. Then, the resulting R-trees are also indexed by using a 1-d index, such as a B-tree. Nodes that are unchanged from time frame to time frame do not need to be indexed again. Instead, they will be linked to the next R-tree.
# Design of Machine Learning Solutions to Post-Harvest Classification of Vegetal Species
## 2 Post-Harvest Classification
### Classification Algorithms
#### 2.2.3 Fine-Tuned Convolutional Neural Network  
ResNet-50+ is our okra classification version of the ResNet-50 to classify post-harvest oka. ResNet-50 is a convolutional neural network with 50 layers pretrained on the ImageNet database, which contains over a million images of multiple objects. It incorporates many categories of plants and vegetables but not okra. However, the network learned to detect many features within the plants and vegetables, which may be advantageous for this study. The model was fine-tuned to fit the case study. Fine-tuning pretrained models saves programming time and processing resources efficiently because they contain crucial information.  
We proceed to fine-tune by \"freezing\" all model layers except the output layer because it was programmed for tasks specific to the previous model. Therefore, these layers were marked as false. Next, new trainable layers were added before adding an output layer specific to our classification. Our new model adds 10 new layers, including 4 batch normalization layers. After these 10 layers, we added an output layer, which is specific to this classification problem. It is also important to note that our dataset must match the existing model datasets. Particularly, we needed to adapt the size of the images to ResNet's and did not use any other color spaces besides RGB.  
particular set of similarities. We start by defining the number \(k\), which refers to the number of classes or clusters required. Then, we define the number of iterations of this process and the proximity between points.  
This process is repeated 100 times (100 iterations). Moreover, we varied the number of clusters from 2 to 14 with only even numbers, as shown in Figure 3(a)-c, which include 3 examples. After comparing the number of clusters and their correlation with accuracy, we noticed that beyond six clusters, the accuracy did not improve significantly. It peaked in six clusters in almost all the experiments. Thus, in this study, we define the standard number of clusters as \(k=6\).
## 1 Introduction  
Today, with the increasing occurrence of natural disasters caused by climate change, the power system, as a vital infrastructure of human society, has faced a growing number of challenges and disruptions from both environmental factors and human activities. This leads huge loss to societal life and work. Between the years 2003 and 2012, the estimated annual cost of power outages caused by severe weather in the United States ranged from USD 18 to 33 billion [1]. In 2021, Texas faced a significant power outage event caused by an unprecedented winter storm, impacting around 4.5 million residential and commercial properties. The resulting financial consequences were estimated to amount to a minimum of USD 195 billion [2]. Therefore, resilient restoration strategies are of great significance for mitigating the adverse effects of natural disasters on the power system.  
Due to its ability to autonomously switch between grid-connected and islanded operation modes, microgrids (MGs) offer a promising solution to enhance the resilience of the power system. In recent years, a multitude of research studies have been conducted to enhance the resilience of microgrids, aiming to effectively address the challenges posed by severe weather phenomena. Networked MGs, which encompass the coordination of multiple MG networks within a fixed or dynamic boundary, have been demonstrated to be an optimal approach for enhancing resiliency [3]. NMGs have recently garnered attention in the literature due to their ability to facilitate the exchange and provision of electricity between MGs, thereby maintaining a balance between the power generation and load [4]. The operational feasibility of NMGs in distribution systems has been demonstrated in [5; 6] through the use of a hierarchical control structure. It represents the further development of the application of MGs through the synchronization of multiple MGs, and could be an increasingly crucial way to improve distribution system resilience. Ref. [7] confirms the unique capability of NMGs as distributed systems in enhancing power system resilience during extreme events, particularly in smart cities. Since the centralized energy management system (EMS) of NMGs obtains all the relevant information of the system and determines the optimum operating point for all network-controlled resources to achieve the global objective [8], we assume that the centralized EMS architecture is implemented for NMGs in this paper.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Experimental Design  
#### 4.1.1 Comparison Algorithm  
In this paper, the effect of signal reconstruction is compared with the following observation matrices:  
* Gaussian random observation matrix (Gaussian),
* Bernoulli random observation matrix (Bernoulli),
* Singular value decomposition and the average value of optimization random gaussian observation matrix [37] (SVD-M),
* Standard bat algorithm (BA) optimization random Gaussian observation matrix [36],
* Adaptive bat algorithm optimization the observation matrix (ABA).
## 4 Test Result
### Vlt  
The VLT of the TCPs was observed under normal incident visible lighting illumination (Table 5). To obtain a reliable average value, each TCP was tested four times. The results showed that (1) the VLT increased with the density of the OFs in the TCP, and (2) the VLT values of the TCPs with 1.5% and 3.0% OFs were slightly smaller than their OF densities (1.1%, 2.3%), while they were larger than the OF densities in the TCP with 6.0% density (6.9%)--this was due to the light rays falling into the light acceptance cone of the OFs that were actually transmitted--and (3) the material types (LWM or NWM) had no effect to the TCPs' light transmittance performance, which was determined only by the amount of the OFs.  
Under the variable oblique light incident angles, the VLT values of two TCPs (LWM and NWM) with the same VR (OF density = 6.0%) were tested (Figure 16). The experimental results showed that (1) for the two specimens, the VLT value was reduced with increasing light incidence angles, and (2) under a 20\({}^{\circ}\) light illuminance angle, the actual transmittance was 6.8%, which was identical to the effect of the normal light (6.9% in Table 5). This shows that, besides the direct normal light rays, the lights rays falling into the light acceptance cone (Figure 11) of the OFs were transmitted. However, once the incidence angle increased to 30\({}^{\circ}\), the transmittance dropped to 5.4%, but the difference to the actual OF density was 16.7%. The LVT decreased faster when the incidence angle was larger than 35\({}^{\circ}\); thus, the light acceptance angle was defined as 35\({}^{\circ}\), which was close to the computational result elaborated in Section 3.3. The two TCPs with different materials (LWM and NWM) showed similar behaviors in the test.
## 3 System Model
### Mobility  
In a wireless network, users exhibit mobility that causes complexity for data caching. Therefore, it is imperative to examine the influence of users' mobility on data access through the cached resources. When users roam around, their connection to serving SBS is unstable, and requires an effective data caching mechanism to provide users' their desired contents efficiently. Therefore, mobility patterns of users' are observed by dividing the communication time between a user and serving SBS into \(T\) time slots, which is expressed by \(\mathcal{T}=\{1,2,\ldots t,\ldots,T\}\). A user is considered static in the first time slot and moved to another position in the second time slot. In this manner, the communication time period between user \(u\) and serving SBS \(s\) is represented as follows:  
\[t_{s,u}=\left\{(t-t_{o}):\left\|\rho_{s}^{t}-\rho_{u}^{t}\right\|<R_{su},t>t_{o} \right\}, \tag{2}\]  
where \(t_{o}\) represents contact duration between serving SBS \(s\) and user \(u\), and \(R_{su}\) represents the user \(u\) under the coverage of SBS \(s\). \(\rho_{s}^{t}\) and \(\rho_{u}^{t}\) denote the location of SBS \(s\) and user \(u\), respectively. \(t_{s,u}\) follows an exponential distribution with parameter \(\lambda_{s,u}\) to determine a contact pattern between SBS \(s\) and user \(u\).
## 3 Field Quantization
### Conditions for Observation of Momentum-Field Quadratic Interactions  
In summary, two general criteria should be satisfied in a carefully designed experiment to allow investigation of momentum-field quadratic interactions:* The optomechanical interaction \(\mathbb{H}_{3}\) must vanish to allow easier study of quadratic interaction \(\mathbb{H}_{4}\). This is quite possible by design as extensively has been discussed in the above and literature [55; 56; 57; 58; 59; 60; 61; 62].
* The mechanical frequency \(\Omega\) must be of the same order of magnitude or exceeding the electromagnetic frequency \(\omega\). This is also possible and at least one experiment using superconducting optomechanics [79] has accessed this regime. Other possibilities are molecular optomechanics [78] as well as a rotating cylinder [80].  
Evidently, such momentum-field quadratic interactions might be more difficult to observe under normal experimental conditions compared to the regular optomechanical setups. However, progressive developments in the precision and accuracy of optomechanics experiments, such as what happened for the case of Laser Interferometric Gravitational Observatory (LIGO) [81], could make it eventually possible to realize and probe such unexplored domains.
# Natural Language Description of Videos for Smart Surveillance
###### Abstract  
After the September 11 attacks, security and surveillance measures have changed across the globe. Now, surveillance cameras are installed almost everywhere to monitor video footage. Though quite handy, these cameras produce videos in a massive size and volume. The major challenge faced by security agencies is the effort of analyzing the surveillance video data collected and generated daily. Problems related to these videos are twofold: (1) understanding the contents of video streams, and (2) conversion of the video contents to condensed formats, such as textual interpretations and summaries, to save storage space. In this paper, we have proposed a video description framework on a surveillance dataset. This framework is based on the multitask learning of high-level features (HLFs) using a convolutional neural network (CNN) and natural language generation (NLG) through bidirectional recurrent networks. For each specific task, a parallel pipeline is derived from the base visual geometry group (VGG)-16 model. Tasks include scene recognition, action recognition, object recognition and human face specific feature recognition. Experimental results on the TRECVID, UET Video Surveillance (UETVS) and AGRINTRUSION datasets depict that the model outperforms state-of-the-art methods by a METEOR (Metric for Evaluation of Translation with Explicit ORdering) score of 33.9%, 34.3%, and 31.2%, respectively. Our results show that our framework has distinct advantages over traditional rule-based models for the recognition and generation of natural language descriptions.  
CNN; multitask feature learning; bidirectional long short-term memory (LSTM); TRECvid 2007/2008; video captioning; smart surveillance; agriculture; intrusions +
Footnote †: journal: Applied Sciences
## 1 Introduction
Third, this study compared the simulation performances of the physical mechanism model and ML models for the first time in a plain river network, in order to compare the performance of the MIKE11 model and the ML models. Based on rainfall data, evaporation data, water level data, water conservancy hub scheduling strategies, and river network and cross-section data, the MIKE11, SVM, RF, and GBDT models were used to establish a water level simulation model in the JiaoGang Basin. This study establishes the hydrological-hydrodynamic model of the area through the river network hydrodynamic module (HD) and the RR module (NAM) of the MIKE11 model and applies the Fitvsvm function, TreeBagger function, and Fitensemble function in MATLAB (2018, 2019) to establish the SVM, RF, and GBDT models for the seven stations of the JiaoGang Basin, such as the Haian, Banjing, and Jiaogangzha stations, respectively. Additionally, it compares the performance of the MIKE11 model and the ML water level simulation models, which can provide a basis for regional river network planning, flood regulation, flood resource utilization, and sponge city construction.
# Bluetooth Low Energy Mesh Networks: A Survey
## 6 Discussion and Open Issues
### Discussion
#### 6.1.1 Flooding vs. Routing  
There exist two multihop paradigm fundamental categories in BLE mesh networks, flooding and routing. An advantage of flooding is its simplicity, as neither it requires the establishment of connections between neighboring devices nor a routing protocol. This avoids delays due to route creation, as well as the complexity and memory consumption due to routing tables and their maintenance. However, since data are flooded throughout the network, this approach may be inefficient in terms of the total number of messages sent by network nodes for the purpose of end-to-end communication between two devices. Such inefficiency increases with network size, thus longer sized networks will benefit from routing. Nevertheless, mitigation techniques (such as Trickle [9] or node-density-aware rebroadcasting [10]) allow the message overhead of flooding-based solutions to be limited.  
Another aspect to be considered is the data message rate that needs to be supported. If data transmissions are infrequent, flooding overhead might compensate the cost of creating and/or maintaining a routing structure.
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer
#### 4.2.8 Crypto Mining and Rewards  
Dew servers acting as hotspot miners can earn crypto tokens as rewards for their reliable network coverage service for IoT devices. Such crypto tokens can have any standard form or be developed in-house. The block time can be in \(\mu\) seconds and the target epoch size is \(b\) blocks. Usually, an epoch consists of all the blocks that are processed by the current CG from the end of the last epoch. Let us assume that the blockchain is designed to mint c crypto tokens per month. Then the following formula can be used to analyze the number of epochs per month, \(l\), and crypto tokens per epoch, \(m\):  
\[l=(43,200\ \text{min per month})/((\mu\times b)/60\ \text{per epoch})\]  
\[m=(c\ \text{crypto-token per month})/(l\ \text{epochs per month})\]  
The crypto tokens per epoch, \(m\), can be shared between the PoC interrogator, the PoC-convicted, and the witness nodes in a standardized manner. The reward can also be split between the CG and the network (data-only hotspot) rewards that are associated with the data transmission. Hotspot owners can use multiple crypto wallets to receive or issue crypto tokens. Such wallets may be equipped with the following features: account balance verification, network identity verification, reconciliation, address book support, and support for payments to multiple accounts/recipients.
# Unlocking the Potential of Keyword Extraction: The Need for Access to High-Quality Datasets
## 2 Literature Review
### Challenges in Keyword Extraction Methods (KeyBERT, YAKE, RAKE)  
#### 2.4.1 Challenges of the KeyBERT Model in Keyword Extraction  
Availability and Quality of Data: As with any machine learning model, KeyBERT requires a substantial amount of data to learn effectively [21]. However, finding and curating a high-quality dataset that is representative of the target domain can be challenging. Poor-quality data, such as text that contains spelling or grammatical errors, can negatively impact the model's performance.  
Domain-specificity: KeyBERT's performance can vary depending on the domain it is applied to. If a model is fine-tuned for one domain, it may not perform well on a different domain, as the keywords and context may differ [22; 23]. Therefore, it is essential to have domain-specific datasets to ensure that the model performs well for the intended use case.  
Data Pre-processing: Pre-processing data to make it suitable for the model can be time-consuming and require domain knowledge. Pre-processing includes cleaning the data, removing stop words, stemming, and lemmatizing, among other tasks. If the data is not pre-processed correctly, the model may miss or misclassify keywords.  
Data Imbalance: The distribution of data can impact the performance of unsupervised models. If the dataset is imbalanced, meaning that some keywords occur much more frequently than others, the model may focus too much on the overrepresented keywords and overlook other important ones [24].