esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Sentence Retrieval Using Sequence Similarity
## 4 Partial Match of Terms Using Sequence Similarity
### BM25 Model with Sentence Retrieval  
The BM25 model uses document ranking, and this model can also be used for sentence retrieval. The ranking function of the BM25 method used to sentence retrieval is defined as [6]:  
\[\text{BM25}(s,\ q)=\sum_{t\in q}log\frac{N-sf(t)+0.5}{sf(t)+0.5}.\frac{(k_{1} +1)c(t,s)}{k1\Bigl{[}(1-b)+b\frac{|q|}{a\text{vs}}\Bigr{]}+c(t,s)}.\frac{(k_{3 }+1)c(t,q)}{k_{3}+c(t,q)} \tag{2}\]  
where  
* \(N\) is the number of sentences in the collection,
* \(sf(t)\) is the number of sentences in which the term \(t\) appears,
* \(c(t,s)\) is the number of appearances of the term \(t\) in a sentence \(s\),
* \(c(t,q)\) is the number of appearances of the term \(t\) in a query \(q\),
* \(|s|\) is the sentence length,
* \(avsl\) is the average sentence length, and
* \(k_{1},k_{3},\) and \(b\) are the adjustment parameters.
## 1 Introduction
Expanding on the abovementioned methods, this paper combines a practical case study for salvaging the Korean Sewol ferry [10] to propose an assessment method suitable for the twin-barge salvage of a shipw. In contrast to scholars such as [PERSON], who used a single barge for salvaging shipwcks, we position two barges on the water's surface, offering a highly redundant lifting solution. However, the assessed operating conditions in this paper differ. Additionally, [PERSON] et al. [34] suggested the possibility of more simulations under extreme sea conditions, including broken cables, which are explored in this paper. Finally, this paper deals with a heavier shipw and more lifting cables, which align more closely with the actual circumstances of salvaging the Korean Sewol. Drawing upon the salvage of the Korean Sewol, this paper conducts a dynamic analysis of shipw retrieval using two lifting barges at a water depth of 280 m, based on the computational fluid dynamics (CFD) method. The main quantities under investigation include the motion response of the two lifting barges and the shipw, along with the tension response of the lifting cables. The primary objectives of this paper include the following aspects:  
* Dividing the shipw retrieval process into three stages (Processes 1-3), starting from disengagement from the seabed until a portion of the structure emerges above the water's surface. A method is developed within this framework to assess the safety of twin-barge retrieval. The assessment involves two categories: primarily quantitative assessment, followed by qualitative assessment. First, in terms of qualitative assessment, the maximum absolute motion amplitude (computed as the difference between the maximum and minimum values) [34] of the six degrees of freedom (6 DOFs) of the three vessels is calculated, providing an intuitive depiction of the motion characteristics of the three vessels in distinct situations. Second, in terms of quantitative evaluation, the safety of the salvage system can be quantified by considering the safety factor of the hoisting cables along with the maximum absolute value (MA), root mean square (RMS), and coefficient of variation (CV) of the 6 DOFs for the three vessels. * Analyzing the impact of the wave direction on the salvage system to mitigate the difficulty of the salvage operation and enhance safety. A comprehensive evaluation of the three vessels' motion responses and cables' tension responses is conducted to determine the appropriate wave interval. * Confining the motion of the three vessels during the salvage process and optimizing cable safety by proposing five schemes to add cables between vessels. Analyzing the impact of these schemes on the salvage system and identifying the most suitable operating approach. * Analyzing the risk of the lifting-cable breakage, providing insights for managing unexpected situations during salvage operations, and deepening the understanding of the underlying physical principles governing the salvage process.
## 2 Preliminary Studies on Federated Learning and Deep Reinforcement Learning
### Federated Learning for Privacy-Preserving Assurance
#### 2.1.1 Edge Federated Learning  
Edge FL consists of edge aggregator orchestration to reduce the number of direct round communication between local participants and server, which is significant for communication-critical and computation-limited IoT clients. There are complementary studies to be outlined in this subsection for analyzing the deployment applicability of edge FL in massive IoT. Edge FL leverages the EC resources for enabling a converged beneficial factor of FL and edge computation offloading [44; 45; 46; 47]. A three-tier architecture between participant, edge, and central FL allows the round communications of participant updates and global aggregation to be client-edge and edge-cloud, respectively. To improve client-specific service, each label can aggregate within the distributed edge server for understanding the service efficiency and alleviating unimportant client-cloud communications. The layers of the defined model are separated for co-training between selected participants and edge servers. The computation-intensive and lightweight layers are operated in edge entities and local devices, respectively. Therefore, the upper-bound execution time of FL training expectation is decreased and acceptable for practical use cases. Figure 3 shows the primary procedures of edge FL, including (1) global model initialization from the parameter server, which is executed in the edge cloud, (2) global model distribution, which is executed in the client-edge tier, (3) local training and loss minimization, which are co-processed between client and edge, (4) loss-optimized local model updates based on labels, (5) multi-model edge aggregation at edge tier, (6) multi-model aggregated updates between edge and cloud, and finally, (7) multi-class global models averaging for the next iteration.  
The primary objectives of edge FL are to reduce global communication cost/frequency and reliability of FL performances. There are complementary studies that explored the promising edge-assisted paradigm. In [48], the impact of global model transmission costs was analyzed in the process of training a model with various bandwidth states and hyperparameter values (e.g., batch sizes and epoch numbers). The authors also provided guidelines about parameter configuration to obtain better learning accuracy and less resource consumption. Furthermore, in [49], the authors introduced edge-assisted FL by showing the efficiencies of integrated design/delay valuation, offloading decisions, and elasticity in different scenarios. The threshold-based offloading strategies were formulated to minimize the tolerable delay under the edge-assisted framework. However, in a heterogeneous IoT environment, the variety of device taxonomies can cause high-complexity offloading path decisions. An experience-driven paradigm or state-detailed observation of the agent can be applied to efficiently optimize the performance in numerous network conditions.
## 2 Materials and Methods
### Cleaning Tests  
Three cleaning tests were carried out on the SSw and Sw areas. They were selected among the most used agarose-based rigid gels and silicone-based methods: (i) deionized water in agarose gel (agarose: 0.75 g, H\({}_{2}\)O 48 mL [38]), (ii) Tween 20 (Honeywell Fluka(tm) Fisher Scientific Italia, Rodano Milan, Italy) 3% deionized water solution supported in agarose gel (agarose: 0.75 g, Tween 20 1.5 mL, H\({}_{2}\)O 48 mL [38]), and (iii) Velvesil(tm) Plus added with its solvent Cyclomethicone D5. Velvesil(tm) Plus (Momentive Performance Materials, Inc., Waterford, NY, USA) consists of a silicone copolymer gel and a silicone-based surfactant in a silicone-based solvent [19]. For each cleaning method four application times were considered according to the recommended cleaning practice. The following application times were selected: t\({}_{1}\) = 30, t\({}_{2}\) = 60, t\({}_{3}\) = 90, t\({}_{4}\) = 180 min for the water in agarose; t\({}_{1}\) = 45, t\({}_{2}\) = 60, t\({}_{3}\) = 90, t\({}_{4}\) = 180 min for the Tween 20 (3%); and t\({}_{1}\) = 5, t\({}_{2}\) = 15, t\({}_{3}\) = 25, t\({}_{4}\) = 45 min for the Velvesil(tm) Plus. The Velvesil(tm) Plus was applied for shorter times due to its faster action compared to the other considered procedures. When t\({}_{0}\) is reported in the text, it is referred to the surface condition before the tests, namely the sweaty (Sw), and sweaty and soiled (SSw).  
The cleaning tests were applied along a line from the left to the right with an ascending order of application time, respectively for the SSw and the Sw areas. The shape of the test areas depended on the viscosity of the cleaning product: the agarose gels were applied in a rectangular shape \(2\times 1\) cm sized in the SSw area and in a square shape \(1\times 1\) cm sized in the Sw area, whereas the Velvesil(tm) Plus was spread in a round of 1.5 cm of diameter or less. In order to facilitate the reading of the results, the test areas are highlighted for each specimen in Figure 1d-f in which a change is recognizable in the delimitated cleaned areas with respect to the surfaces represented on Figure 1a-c.  
Figure 1: Photographic documentationck-ups, before (**left**) and after (**right**) the cleaning tests, respectively: water in agarose (**a,d**), Tween 20 (**b,e**), and Velvesil(tm) Plus (**c,f**). Treated areas and test distribution for each application time are schematically highlighted.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 2 Methods
### Summary of Algorithm  
The complete restoration strategy is summarized in Algorithm 1.
## 3 Results and Discussion
### Developed Technologies for Exhausted Adsorbent and Desorption Solution Disposal
#### 3.5.1 Arsenate Stabilization and Leaching Study  
After the stabilization of the desorbed As(V) species in effluent water, the FeCl\({}_{2}\) and FeCl\({}_{3}\) blended solution or iron ion-containing washing solution showed that the arsenate concentration decreased by 95.14% and 96.86%, respectively. Inorganic pollution elements appeared to be effectively stabilized, with the leachate concentrations well below the regulatory limit values as obtained by the TCLP test (method given in SM S2.7.5). The obtained values are lower than the maximum permissible concentration of 10 ppb prescribed by the Environmental Protection Agency (EPA) [63].
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 4 Photonic Devices and Components
### Technologies for Energy Harvesting  
In the metro and access segments, the software-defined nodes providing physical-layer re-configurability to the network are expected to be placed also in street environments and not only in COs, since most of the operators have plans to consolidate the number of COs in order to reduce real estate costs [76]. This means that the provisioning of power supply will become an increasingly critical issue, given the major complexity of a field-installed supply. This reason, added to the obvious necessity to reduce the power consumption to achieve OpEx saving, underpins the research for energy harvesting/scavenging mechanisms. In fact, one of the main roadblocks that could prevent the introduction of flexible optical networking technology in the access segments is to retain a fully passive outside fiber plant [39].  
In some of the metro-access topologies which will be introduced in the Section 5.2, erbium doped fiber amplifiers (EDFAs), remotely pumped from the CO, are used in the remote nodes (RNs) to compensate both fiber attenuation and insertion losses. On one hand, this configuration avoids local power supply provisioning but, on the other hand, the total amount of pump power demanded for the CO is an important limitation of the network.  
[PERSON] et al. [77] have proposed an improved energetically-efficient RN able to select a different EDFA optimized to the necessary gain, thus reducing the total amount of pump power. Moreover, in particular conditions, the pump extra-power is used to convert to electrical power by a special  
Figure 10: Schematic representation of the packet switching technique proposed by [PERSON] et al. [75]: (**a**) one control wavelength; (**b**) different control wavelengths.  
harvesting device and stored electrically in order to be used to control the switches, thus keeping the network fully passive.  
[PERSON] et al. [39] have demonstrated a fully-passive yet reconfigurable optical cross-connect (FOX), i.e., a special ROADM for metro-access networks, based on micro-opto-electro-mechanical system (MOEMS) technology, which is fed through energy scavenging at the optical network layer through a photovoltaic PIN diode.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 3 Mainstream Models and Methods
### Analytical Modeling  
#### 3.2.1 Linear Regression Model  
Linear regression models determine the parameters of the model by fitting the relationship between multiple independent and dependent variables, thus regressing back to the original equation to predict the trend of the dependent variable. As shown in Table 3, the domestic and international literature on the application of regression models to surface taxi time prediction over the last 20 years is summarized. The impact of departure queue size on taxi time was initially considered a significant factor and incorporated into the characteristic variables of the model. Subsequently, researchers also introduced the number of arrival aircraft to study the combined effects of these two influencing factors on taxi-out time. In 2001, [PERSON] et al. (2006) pioneered the study of surface traffic flow characteristics' influence on taxi time and proposed the concept of the departure queue. They published the first paper on predicting taxi time based on a multiple
# YOLO-OHFD: A YOLO-Based Oriented Hair Follicle Detection Method for Robotic Hair Transplantation
## 2 Materials and Methods  
### Proposed Method  
In this study, we introduce a novel oriented object detection method tailored for hair follicle identification, designated as YOLO-OHFD (You Only Look Once-Oriented Hair Follicle Detection). As depicted in Figure 3, the proposed system architecture comprises three core components: a feature extraction network, a feature fusion network, and a  
Figure 2: Motivation of this study.  
detection network. Initially, dermoscopic images from the posterior occipital region are processed through the feature extraction network, which integrates ECA-Res2 Block to facilitate multi-scale feature extraction. This network captures feature layers at various scales (C1, C2, C3, C4, C5), essential for analyzing the orientation of hair follicles. Subsequently, these multi-scale feature layers are amalgamated in the feature fusion network, resulting in combined feature maps (F2, F3, F4, F5). The Feature Alignment Module (FAM) within this network addresses potential misalignments during the fusion of oriented object features, ensuring precise feature integration. The enriched feature maps (F2, F3, F4) are then relayed to the detection network, which predicts the position (x, y, w, h) and orientation angle \(\theta\) of each follicle, alongside classifying the follicles into categories {1, 2, 3, 3+} based on the number of hairs. These classifications correspond to Class1, Class2, Class3, and Class4, representing follicles containing one, two, three, and more than three hairs, respectively. For visual clarity, Figure 1 illustrates these classifications: green boxes indicate single-hair follicles, red boxes denote two-hair follicles, blue boxes represent three-hair follicles, and purple boxes are used for follicles with more than three hairs. To enhance the precision of angle prediction, this study employs angle classification instead of regression. The subsequent sections provide a detailed description of these methodologies and their implementation.
## 2 Related Work
Some approaches proposed using more latent codes to capture local shapes prior to generating more precise geometry [38]. To complete this, the point cloud is first divided into uniform grids or local patches, and then a neural network is utilized to extract latent coding for each grid/patch. Unsigned Distance Functions (UDFs) are capable of extracting surfaces directly from gradients. Some research uses UDFs as a normal estimation or semantic segmentation. Nevertheless, due to the noncontinuous nature of point clouds, these approaches require ground truth distance values or even large-scale meshes during training, and it is difficult to obtain smooth distance fields near the surface [39]. However, it is more difficult to learn implicit functions straight from raw point clouds without ground truth signed/unsigned distance or occupancy data. Current work presents agnostic learning for learning SDFs from raw data using a specifically designed network initialization [40], gradient constraints [41], or geometric regularization [42]. Neural-Pull [43] learns SDFs in a novel way by drawing neighboring space onto the surface. However, because they are designed to learn signed distances, they are unable to recreate complicated forms with open or multi-layer surfaces. Our technique, on the other hand, can learn a continuous unsigned distance function from point clouds, allowing us to recreate surfaces for forms and sceneries of any typology. We also compare previous studies with our developed framework, as shown in Table 1. Thus, the major contributions of our proposed method can be highlighted as follows:  
* We proposed an end-to-end 2D-to-3D framework including 2D-to-3D point cloud via PIFu processing and regenerating 3D-point cloud-to-3D fully regenerated instances via the CAP-UDF model, in which these two steps are integrated as one mainstream task;
* Comparisons from multiple perspectives were conducted using one benchmark dataset and one actual implementation for a garment task;
* Garment visualization has been further demonstrated in a virtual world after mapping from the regenerating task.
## 6 Open Issues and Future Research Directions
### Network Dynamicity and Link Failures  
The network is very dynamic, and the topology often changes because of the rapid speed of UAVs in FANETs. Due to the UAVs' shifting locations, linkages can be formed and broken often, resulting in intermittent connections. This destabilizes the communication network, having a negative influence on routing efficiency and performance. The node density is modest in most applications, and the network split can last for a long time. Because of failure or energy fatigue, UAVs can exit and rejoin the network at any time. Topological changes are also caused by mission updates on the fly and environmental barriers such as mountains, temperature variations, and geographic uncertainty. Networks with broken connections make routing extra difficult in UAV networks. Developing routing protocols would become a complex undertaking due to this complexity. Further research investigation can be done to address these challenges. For instance, diversifying and selecting relay nodes to establish the significance of the cooperative diversity technique with bio-inspired computing can be used in routing protocols to reduce the link breakages [111].
## 3 Review of Limbasiya et al.'s Protocol
### Message Signature and Confirmation Phase of [PERSON] et al.'s Protocol  
\(TPD_{i}\) signs the information with the session key and forwards to the connected \(RSU_{j}\). Figure 4 shows the detailed equations with process steps, as follows.  
**Step 1:**: for signing the message, \(TPD_{i}\) computes \(\sigma_{i}=SK_{RID_{i}}\oplus h(M_{i}||T_{1})\) and \(M_{i}^{{}^{\prime}}=SK_{RID_{i}}\oplus ID_{RSU_{j}}\). Subsequently, \(TPD_{i}\) sends message \(\{ID_{i+2},RID_{i},\sigma_{i},M_{i}^{{}^{\prime}},T_{1}\}\) to the concerned \(RSU_{j}\).
**Step 2:**: after receiving the message, \(RSU_{j}\) computes \(M_{i}=SK_{RID_{i}}\oplus M_{i}^{{}^{\prime}}\oplus T_{1}\oplus RID_{i}\oplus ID _{RSU_{j}}\) and \(\sigma_{i}^{{}^{\prime}}=SK_{RID_{i}}\oplus h(M_{i}||T_{1})\).
**Step 3:**: then, \(RSU_{j}\) compares the \(\sigma_{i}\) with \(\sigma_{i}^{{}^{\prime}}\). If they are equal, \(RSU_{j}\) uses \(M_{i}\) for future computations. Additionally, Generally for batch verification, \(RSU_{j}\) inspects the exacton by a following equation:  
\[(\sum_{i=1}^{n}v_{i}\cdot\sigma_{i})=\sum_{i=1}^{n}v_{i}\cdot SK_{RID_{i}} \oplus\sum_{i=1}^{n}v_{i}\cdot h(M_{i}||T_{1})\]
## 2 Materials and Methods
### Cell Culture  
Human muscle precursor cells (huMPCs) were cultured in a growth medium (GM) consisting of HAM's Nutrient Mixture F10 without L-Gluamine medium (Eurolone, Milan, Italy; #ECB7503L) supplemented with 20% Defined Fetal Bovine Serum, US Origin; (Eurolone, Milan, Italy; #CHA1111L), 1% Penicillin-Streptomycin Solution 100X (Eurolone, Milan, Italy; #ECB3001D) 0.1% Gentamicin 10 mg/ml sulphate (Eurolone, Milan, Italy; #ECM0011D) and 1% Stable Glutamine (200 mM) (Eurolone, Milan, Italy; #ECB3004D) in an H\({}_{2}\)O-saturated 5% CO\({}_{2}\) atmosphere at 37 \({}^{\circ}\)C. At a 60-70% confluence, the cells were treated by adding trypsin-EDTA 1X in PBS w/o Calcium w/o Magnesium w/o Phenol Red (Euroclone, Milan, Italy; #ECB3052D) solution, harvested, centrifuged at 800 rpm for 5 min, suspended in fresh GM, counted in a Burker chamber, and plated on plasticNunc(tm) Thermanox(tm) coverslips (10.5 \(\times\) 22 mm, ThermoFisher Scientific, Waltham, MA, USA; #174934). Plastic coverslips were preferred to glass coverslips since these latter could break during launch and shipment to the ISS. We had already used plastic coverslips to cultivate muscle cells in both proliferation and differentiation conditions, obtaining good results [12]. The cell proliferation rate was evaluated as population doubling level (PDL) using the formula logN/ln2, where N represents the ratio between the number of cells that were detached after 10 days of culture in GM and the number of cells that were initially seeded. To evaluate cell survival, huMPCs were trypsinized, suspended in PBS, and added with trypan blue 0.4% solution (Sigma-Aldrich, Milan, Italy; #T8154) (1:1 \(v/v\)). Cell viability was determined as the percentage of trypan blue-negative cells with respect to the total amount cells.  
To induce differentiation into myotubes, huMPCs were added to a differentiation medium (DM), composed of Dulbecco's Modified Eagle's Medium high-glucose (DMEM) Euroclone, Milan, Italy; #ECB7501L) supplemented with 5% heat-inactivated (56 \({}^{\circ}\)C, 30 min) horse serum (Euroclone, Milan, Italy; #ECS0091L), 50 \(\upmu\)g/mL Gentamicin 10 mg/ml sulphate, 1% Stable Glutamine (200 mM), 1% Penicillin-Streptomycin Solution 100X, 10 \(\upmu\)g/mL insulin solution from bovine pancreas (Sigma-Aldrich, Milan, Italy; #I0516-5 ml) and 100 \(\upmu\)g/mL of apo-transferrin human (Sigma-Aldrich, Milan, Italy; #T2036). We followed the differentiation process until 7 days.