esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Transformer-Based Approaches
#### 3.3.5 Multi-Stream Transformer Models  
To capture the diverse information in videos more effectively, multi-stream transformer models have been developed. [PERSON] et al. introduced the Collaborative Three-Stream Transformers (COST) framework [41], which is illustrated in Figure 11. This framework comprises Video-Text, Detection-Text, and Action-Text branches, each tailored to model interactions between global video appearances, detected objects, and actions, respectively. By fusing these branches, COST generates comprehensive captions, and a cross-granularity attention module ensures feature alignment across the different streams. This design allows COST to capture intricate interactions between objects and their actions/relations across various modalities.  
In a different approach, [PERSON] et al. [42] presented SnapCap, a method that bypasses the reconstruction process and generates captions directly from compressed video measurements. This method is guided by a teacher-student knowledge distillation framework using CLIP. While multi-stream transformer models, such as COST, significantly enhance video captioning performance by leveraging complementary semantic cues, they do come with higher computational costs due to their multiple streams. SnapCap, on the other hand, offers an alternative that may address some of these computational challenges while still aiming for accurate caption generation.
# Human-AI Collaboration for Remote Sighted Assistance: Perspectives from the LLM Era +
## 4 Identifying Challenges in RSA: User Interview Study
### Common Navigation Scenarios  
The most common types of navigation scenarios that our participants asked RSA agents for help with are traveling and navigating unfamiliar indoor or outdoor places. Navigating unfamiliar areas where a blind user might utilize the RSA service came up often in our study, which is consistent with the literature [101; 1]. For outdoor navigation, common scenarios include checking and confirming the location after Uber or Lyft drop-offs; finding an entrance from a parking lot; taking a walk to a park, coffee shop, or mailbox; navigating in a big college campus; and crossing the street.  
The common indoor places they called RSA agents for help with were airports and large buildings (e.g., malls, hotels, grocery stores, and theaters). In an airport, they usually asked RSA agents to find a gate and baggage claim area. Inside large establishments or buildings, they asked for finding a certain point of interest (e.g., shops, customer service desk); entrances and exits, stairs, escalator, and elevator; and objects, e.g., vending machines and trash cans. Our data suggest that blind users repeatedly use RSA services to navigate the same place if its layout is complex (e.g., airports), or their destination within the place is different (e.g., different stores in a shopping mall), or the place is crowded and busy (e.g., restaurants).
## 3 Materials and Methods
### The Proposed CLpCEwDCS Decoding Framework
#### 3.2.2 The CLpCE Objective Function  
As an objective function, CE is widely used in the optimization procedure of text generation. For a text input \(x\) containing \(m\) sentences with length \(n\), assuming the corresponding distribution is \(y\) and the predicted distribution is \(\widehat{y}\), the CE loss is calculated as in Equation (6).  
\[\begin{split} L_{CE}&=\frac{1}{mn}\sum_{i=1}^{n} \sum_{j=1}^{m}\{-y_{i,j}\times\log(\widehat{y_{i,j}})\\ &-(1-y_{i,j})\times\log(1-\widehat{y_{i,j}})\}\end{split} \tag{6}\]  
As to the same input as in Equation (6), the objective function of CL of text \(x\) can be calculated as in Equation (7), and notably, the parameters are defined the same as those in Equation (4).  
\[L_{CL}=-\sum_{i=1}^{n}\log(\frac{e^{cos(h_{i}h_{i}^{+})/\tau}}{\sum_{j=1}^{N} e^{cos(h_{i}h_{i}^{+})/\tau}}) \tag{7}\]  
Inspired by CL [37] and SimCSE [38], CLpCE is designed for guiding the fine-tuning process of GPT-2. The optimization goal of CLpCE can be defined as in Equation (8), where parameter \(\beta\in[0,1]\) is used to adjust the proportion of the loss functions. It should be mentioned that when \(\beta=0\) and \(\beta=1\), the objective function CLpCE degenerates into CE and CL, respectively.  
\[L_{CLpCE}=(1-\beta)\times L_{CE}+\beta\times L_{CL} \tag{8}\]  
Figure 2 shows the model fine-tuning procedure. It consists of CE-based supervised learning and CL-based unsupervised learning parts, both of which are weighted by \(\beta\) in the CLpCE objective function.
## 1 Introduction  
Over the past 70 years, three generations of biomaterials have been used in total joint arthroplasty (TJA). The first generation was comprised of essentially bioinert compounds, the second generation included bioactive and biodegradable materials, and the third generation is now composed of bioactive substances designed to stimulate specific cellular responses at the molecular level [1]. Yet, despite this evolution, biomaterials are still limited to three classes: metals [2], ceramics [3], and polymers [4]. Each class plays a critical role in the mechanical stability, osteointegration, and wear performance of prosthetic joints. Although TJA is considered to be highly effective, with success rates of \(>\)90% at the 10-year follow-up, aseptic loosening, instability, and infection do occur [5; 6; 7]. Wear-induced osteolysis is a significant factor in loosening and instability, but the root cause is a lack of adequate osteointegration. To fight pathogens, practitioners have opted for the sole use of systemic antibiotics to eliminate periprosthetic joint infections, and until recently, little consideration had been given to using implants that inherently exhibit antimicrobial properties. However, currently engineered biomaterials are both osteoconductive and bacteriostatic [8; 9]. Several approaches are being taken to provide them with these important characteristics [10; 11; 12; 13; 14; 15; 16; 17].  
Silicon nitride (Si\({}_{3}\)N\({}_{4}\)), a new third-generation biomaterial, is a bioactive non-oxide ceramic. Its unique surface chemistry is both antibacterial [18; 19; 20; 21] and osteoconductive [22; 23; 24; 25; 26]. In its monolithic dense form, it is mechanically suitable as an orthopedic structural member and has been approved by multiple regulatory organizations as an implant for spinal arthrodesign [27; 28]. Its surface can be highly polished to serve as a prosthetic femoral head, with superior wear performance, or that equivalent to the best currently available alternatives [29; 30]. As a porous ceramic, it has been shown to have angio- and osteo-promotive characteristics [31; 32]. It can be added to a polymer to form a composite [33] or applied as a powder coating. In fact, multiple studies have demonstrated that it can impart its beneficial biological characteristics onto other biomaterials [34; 35; 36; 37; 38; 39].  
Using a laser cladding procedure, we created a sintered Si\({}_{3}\)N\({}_{4}\) coating on low-density polyethylene (LDPE), titanium (Ti6 Al4V), zirconia toughened alumina (ZTA), and yttria-stabilized zirconia (Y-TZP). These coatings were then morphologically and chemically analyzed, and subsequently evaluated for their in vitro osteoconductive and antibacterial characteristics. Mechanical properties and adhesion strength of the silicon nitride coatings were already previously discussed for Ti6 Al4V [36] and zirconia [37] substrates.
## 3 Towards Autonomous SC with Tenderfone  
Generally speaking, BCT exposing some notion of smart contract can be described as layered distributed systems, whose main layers are depicted in Figure 4: _(i)_ a ledger-based state machine replication (SMR) layer (\(\mathbf{L}_{1}\)), letting a number of distributed processes share a common state and update it in a consistent way, via consensus protocols, and _(ii)_ a replicated interpretation layer for SC (\(\mathbf{L}_{2}\)), built on top of the SMR layer, dictating smart contract semantics and exposing two main interfaces to either SC developers or end users.  
The end-user interface (\(\mathbf{I}_{1}\)) is aimed at letting users deploy SC or send messages to them, by issuing transactions. Conversely, the SC interface (\(\mathbf{I}_{2}\)) aims at providing SC developers with built-in functionalities to write SC. These may include, for instance, message passing (or money exchange) primitives, common computational and storage constructs, and some means to read relevant information from the underlying ledger (e.g., time).  
With the twofold aim of validating our envisioned roadmaps and providing a common ground for researchers to experiment with and build systems integrating the foundational concepts of agency into the blockchain, in this section we describe a novel design for \(\mathbf{L}_{2}\), \(\mathbf{I}_{1}\), and \(\mathbf{I}_{2}\), aimed at supporting autonomous, asynchronous, time-aware, and logic-based smart contracts. In particular, we discuss the case of Tenderfone (source code publicly available at [[https://gitlab.com/pika-lab/blockchain/tenderfone/tenderfone-sc](https://gitlab.com/pika-lab/blockchain/tenderfone/tenderfone-sc)]([https://gitlab.com/pika-lab/blockchain/tenderfone/tenderfone-sc](https://gitlab.com/pika-lab/blockchain/tenderfone/tenderfone-sc))), an experimental platform we built to advance the state-of-the-art blockchains with autonomous, asynchronous, time-aware, and logic-based smart contracts. First, we describe the language for programming SC and manipulating them (i.e., \(\mathbf{I}_{1}\) and \(\mathbf{I}_{2}\)), then we summarize the functioning of the Tenderfone Interpreter (i.e., \(\mathbf{L}_{2}\)), emphasizing the assumptions required for the underlying BCT to make SC autonomous, asynchronous, time-aware, and logic-based.  
As a last note, it is worth to highlight that, while currently leveraging on Tendermint [34] as the underlying ledger-based SMR technology, the Tenderfone Interpreter specification is both technology and consensus protocol agnostic as it relies on very loose assumptions about the underlying SMR layer. Thus, even if we chose Tendermint as our implementation platform, any other BCT satisfying such assumptions could be used as well.
# Automotive Seat Comfort and Vibration Performance Evaluation in Dynamic Settings
## 2 Materials and Methods
### Laboratory Evaluation
#### 2.2.2 Lab Test Protocols  
The study was designed as a repeated-measures double blinded test (neither the researchers or participants were aware of or knew the differences among the seats) with a randomized seat testing order. Three test seats were installed on a six degree-of-freedom motion platform that created different WBV exposures in a laboratory setting. Prior to the experiment, all the participants were briefed on the study objectives and protocols, and gave their written consent. Then, anthropometric and demographic data were collected to adjust the seat. The seat height was adjusted to make the thighs parallel to the floor while the seatback angle was set at 110 degrees (i.e., 20 degrees inclined) per ergonomic guidelines [30]. Participants were not allowed to adjust the pre-determined seat back angle to avoid any potential confounding due to different postures.  
\begin{table}
\begin{tabular}{c c c} \hline  & **Mean \(\pm\) SD** & **Range** \\ \hline Age (years) & 27.3 \(\pm\) 6.7 & 19–40 \\ Height (cm) & 169.6 \(\pm\) 11.2 & 154–185 \\ Weight (kg) & 67.5 \(\pm\) 9.8 & 52–80 \\ BMI (kg/m\({}^{2}\)) & 23.7 \(\pm\) 3.0 & 19.6–29.6 \\ Driving Experience (years) & 10.0 \(\pm\) 6.9 & 3.5–24 \\ \hline \end{tabular}
\end{table}
Table 2: Laboratory vibration study participants’ demographic summary (\(n=10\)).  
Figure 2: Overall hardness measured by load-deflection curves (**left**) and vibration transmissibility (**right**) of three testing seats per SAE J2896.  
Before being exposed to the vibration, the first impression ranking data were collected based on the participants' initial preference and overall comfort. Then, each participant sat for 45 min in each seat with a 10-min break between seats (Figure 3). The 45-min testing time per seat consisted of real tri-axial field-measured vibration (15 min) and sinusoidal vibration (30 min). Perceived body discomfort was collected via the Borg-100 questionnaire before the vibration exposure and at the end of each vibration condition (indicated with the black arrows in Figure 3). Seating comfort was measured with a Likert 7-point questionnaire before the vibration exposure and at the end of field vibration and sinusoidal vibration exposure (red arrows in Figure 3). After completing all the conditions, the post ranking was collected based on the participants' post preference and overall comfort.
## 4 CILS Framework: Enhancing Cross-Cultural Language Learning
### Automation and Optimization of Language Learning
#### 4.3.2 Machine Learning for Optimizing Learning Paths  
Within the CILS, ML is essential for optimizing the learning paths and, as a result, for personalizing educational experiences for each learner based on individual needs and preferences. By processing extensive data on learner performance, ML algorithms recognize patterns and trends that dictate the adoption of learning modules and activities.  
The CILS uses ML algorithms to continuously assess various instructional strategies and content delivery approaches and adapt them to improve learners' engagement and retention [34]. For instance, if a learner learns vocabulary faster through visual aids than through the traditional, textual approach, the system automatically alters its tactics to include more graphical content in the subsequent lessons.  
ML promotes personalization of the learning path by anticipating potential learning barriers and being ready with remedial content. Such a proactive approach ensures that learners are supported on time, avoiding frustration and making the learning process smoother. On the other hand, ML promotes personalization scaling, allowing educational content to be fine-tuned for learners of different cultural backgrounds and levels of prior knowledge.  
Through ML, the CILS enables not only the optimization of individual learning paths but also their scalability and adaptability, thus offering effective language learning possibilities to a diverse global population. Such technology-based applications provide faster learning processes, quicker language acquisition, and a detailed understanding of both linguistic and cultural nuances.
## 1 Introduction
This has been pushed by the development of more efficient semiconductor switches, being the MOSFETs, i.e., metal oxide semiconductor field effect transistors, based on SiC technology, presently the most used switches. The aim of this paper was to present a highly flexible pulse generator that can be used in several electroporation applications in biomedical and biotechnological context described, where the generation of high-voltage, high-frequency pulse burst of different pulse shapes, with pulse-to-pulse adjustment of pulse width, polarity, and amplitude is possible. Henceforward, a new pulsed generator, which includes four independent MOSFET based Marx modulators, operating individually and in combination, controlled from a computer user interface is described. The generator can be programmed to apply into biological loads different pulse shapes, from unipolar to bipolar pulses, in symmetric and asymmetric modes, with voltages up to 6.5 kV and currents up to 65 A, in pulse width from 100 ns to 100 \(\upmu\)s, including overcurrent protection, current, and voltage monitoring. The design of this generator gives it the flexibility to generate pulses with different pulse widths, polarity, and amplitude from pulse-to-pulse, while operating in burst mode up to 5 MHz on four independent channels for applying synchronized asymmetric bipolar pulses. This paper shows these properties and other characteristics of the generator.
## 4 Data Analysis in Maritime IoT Applications
### Case Studies of Successful Data Analysis in Maritime IoT
#### 4.1.6 Case Study 6  
Optimization in Shipping and Port Industries Through Big Data Analytics: A study by [PERSON] et al. explores the role of Industry 4.0 technologies, such as AI, BDA, Cloud Computing, and IoT in revolutionizing operations within the shipping and port industries. The research focuses on the management of vast amounts of data generated by on-board ship computer systems and IT applications related to ship and port management. The authors propose the use of software tools to extract and process relevant information and advanced algorithms to perform statistical analyses. Through a SWOC (Strengths, Weaknesses, Opportunities, and Challenges) analysis matrix, the study identifies and discusses the tools and techniques associated with BDA, highlighting the potential of effective Big Data management in improving or optimizing maritime transport activities and port operations [71].
# Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition
## 4 Experiments
### Ablation Studies
#### 4.3.3 Ablation Study of Fusion Strategy  
The MGFF module integrates output features from ICSA of varying granularities through three fusion approaches, average fusion, linear fusion, and dynamic fusion. Specifically, \(\text{MGFF}(n=1,2,3)\) denotes the fusion of three ICSA modules, with granularities of 1, 2, and 3, respectively. Average fusion refers to summing the output features of the three ICSA modules and then taking their mean. Linear fusion involves passing the output features of the three ICSA modules through a linear layer for integration. Dynamic fusion, on the other hand, processes the output features of the three ICSA modules through the MGFF module.  
As shown in Table 2, MGFF's dynamic fusion achieves optimal performance (91.0% X-Sub, 95.8% X-View), surpassing linear fusion by 0.4-0.5% and average fusion by 0.2%. This is because linear fusion and average fusion merely perform simple combinations of the output features from different ICSA modules, failing to fully utilize their potential. In contrast, the MGFF module adopts a data-driven approach to generate weight vectors, which effectively leverages the output features of each ICSA module to adaptively produce weight vectors with different granularities. This demonstrates that the proposed MGFF module can effectively fuse output features at different granularity levels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **X-Sub (\%)** & **X-View (\%)** \\ \hline MSTC-4 & 90.2 & 94.2 \\ MSTC-6 & 90.5 & 94.9 \\ MSTI-4 + fixed weight & 90.7 & 95.4 \\ MSTI-4 + dynamic weight & 90.8 & 95.4 \\ MSTI-6 + dynamic weight & **91.0** & **95.8** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study of MSTI module on the NTU RGB+D 60 dataset.
## 4 Procedures and Results of Diagnosis
### Diagnosis of Basic Scratch Faults  
Diagnosis was performed on three types of bearing conditions (H, SS, and FS) without considering the rotation speed of the induction motor; the features obtained at four rotation speeds were merged. The datasets of the 30 and 90 Hz frequency components were divided into two groups randomly in a 70:30 ratio. Seventy percent of the data for each bearing condition (336 in total) was used for training, and the remaining 30% (144 in total) was used for diagnosis. Since DL follows a stack-flow method of diagnosis, H, SS, and FS took stack values of 0, 1, and 2, respectively.  
The epoch dependences of the accuracy rate and loss in H, SS, and FS diagnosis for the proposed method are shown in Figures 9 and 10, respectively. The number of epochs was related to the number of optimization rounds required to train the data. The error performance or loss was reduced as the number of epochs increased. Therefore, the epoch needed to be selected carefully considering the available datasets. The relation between epoch and loss can be explained using Equation (2):  
\[epoch=1/loss \tag{2}\]  
However, many epochs can make the network overfit the training data and degrade its performance. Therefore, the selection of epochs is important. A few iterations were conducted for the entire input dataset based on the epoch value. The number of iterations was set based on the batch size (number of training samples in one forward-backward pass). The accuracy rate increased with the epochs and reached 83.08% in this study, which was acceptable.  
The diagnostic results of the basic scratch faults are given in Table 1 where, for example, H-SS means a comparison between healthy and single scratch bearings. The accuracy rate of H-SS and SS-FS was 100%, indicating that the proposed method separated the two clusters and differentiated their bearing conditions. The differentiation of the three clusters (H-SS-FS) had a practically acceptable accuracy rate of 83.08%.  
Figure 8: Feature distribution without considering the rotation speed (concentrated scratch).
## 3 The Proposed Hybrid Demodulation Technique
### Modulation Intensity Distribution
In addition, since the operation proposed in Equation (7) can be illustrated as a joint down-sampling and filtering procedure,  
Figure 4: The process of the sideband filtering. the down-conversion operation takes place during the computation of \(X_{1/\Delta f}(t,f)\). Based on Equation (10), the spectral correlation density between components \(x_{\Delta f}(t,f+\ \beta)\) and \(x_{\Delta f}(t,f)\) is given by  
Figure 4.0  
Similarly, the SCor density can be also computed between components \(x_{\Delta f}(t,f-\beta)\) and \(x_{\Delta f}(t,f)\), that is,  
Figure 4.1  
It should be noted that frequency shifting in Equations (11) and (12) causes the frequency \(f\) to be the center frequency for both filtered components. To find the relationship between three spectral components spaced by cyclic frequency \(\beta\), the two SCor densities introduced in Equations (11) and (12) are adopted to calculate the MID using a given \(\Delta f\), which can be expressed by  
Figure 4.2  
Practically, the product of SCor densities as the MID might not be the most utilized metric because of large differences in signal energy in lots of frequency bands. In such cases, the illustration of MID maps may be much more useful when the absolute measure of modulation intensity is normalized between 0 and 1. Therefore, the \(\text{MID}_{\Delta f}^{\text{SCoh}}\) can be readily extended to the use of the spectral coherence (SCoh) density as the MID [16]. SCoh density is given by  
Figure 4.3  
Figure 4.4  
Then, the expression of MID with SCoh is also given below:  
Figure 4.5  
Certainly, both \(\text{MID}_{\Delta f}^{\text{SCor}}\) and \(\text{MID}_{\Delta f}^{\text{SCoh}}\) can be employed in signal demodulation. The difference between them will be investigated in the following subsection.