esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Method Validation
#### 3.2.6 Stability Experiments  
Short-Term Stability in Authentic Matrix  
A concentration increase was observed when the samples were stored at 4 \({}^{\circ}\)C, even for 24 h (CV 14.6%). A more pronounced increase was indicated on the seventh day, CV 21.4%. A possible explanation of that phenomenon is the release of free arachidonic acid from phospholipids incorporated in plasma lipoproteins by phospholipase A2, which remains active at mild storage conditions.  
Long-Term Stability in Authentic Matrix  
AA in serum samples remained stable at \(-\)18 \({}^{\circ}\)C for 24 h (CV 0.9%), for one week (CV 1.7%) and two months (CV 13.0%).  
Stability of AA in Processed Samples  
The sample extracts stored in the autosampler at room temperature (20 \(\pm\) 5 \({}^{\circ}\)C) were stable within 14 days without significant changes in their concentration (CV 10.0%).  
Literature data regarding the stability of AA in human blood serum are extremely scarce. Similar results regarding short-term stability of free fatty acids, including AA in mice plasma, revealed sample stability at room temperature for 6 h. The same authors showed that unprocessed mouse plasma samples were stable for 30 days when stored at \(-\)80 \({}^{\circ}\)C. Results presented by [PERSON] et al. on processed brain homogenates indicate that processed samples are stable at 4 \({}^{\circ}\)C for no more than 14 h [33]. Considering our data, it would be recommended to store serum samples prior to analysis at least at \(-\)18 \({}^{\circ}\)C for no longer than seven days.
## 5 System Identification and Notch Filter Design
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Peak** & **Resonance (Hz)** & **Damping Ratio** & **Notch Filter** \\ \hline
1 & 13.8478 & 0.7000 & \(G_{c5}=\frac{s^{2}+2.4s+7570}{s^{2}+121.8s+7570}\) \\
2 & 28.6550 & 0.7000 & \(G_{c4}=\frac{s^{2}+8s+32420}{s^{2}+252.1s+32420}\) \\
3 & 109.0439 & 0.7000 & \(G_{c3}=\frac{s^{2}+28s+469400}{s^{2}+959.2s+469400}\) \\
4 & 280.1127 & 0.7000 & \(G_{c2}=\frac{s^{2}+26s+3098000}{s^{2}+2464s+3098000}\) \\
5 & 538.0233 & 0.7000 & \(G_{c1}=\frac{s^{2}+120s+11430000}{s^{2}+4733s+11430000}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Notch filter design. Figure 16: Notch filter control result: (**a**) frequency response functions; and (**b**) changed root locus. In a real situation, the exact model of the system cannot be known due to changes in the operational environment, and the plant may vary over time. To incorporate the fact that the plant will change and to highlight the efficacy of the controller, it is assumed that an additional mass (about a 20% increase) is attached to the original system, as mentioned in Section 4. Since an added mass implies decreased resonance frequencies, a modified transfer function would exist with reduced resonance frequencies for each peak. In the next section, the SM-LMS algorithm's target-tracking performance is observed for vibration attenuation using the baseline and modified beam structures.
###### Abstract  
(1) Background: osteoarthritis (OA) of the knee is a degenerative disease accompanied by pain, reduced mobility and subsequent decrease in quality of life. Many studies on OA of the knee have reported that using an intercellular acting-derivate like platelet-rich plasma (PRP) results in a limited effect or none at all. Authors hypothesized that adding tissue-Stromal Vascular Fraction (tSVF) to PRP (Platelet Rich Stroma (PRS)) would reduce pain and improve functionality in osteoarthritis of the knee. (2) Methods: a consecutive case series of fifteen patients (aged 43-75 years) suffering from OA of the knee (Kellgren-Lawrence stage two to three) were treated with a single injection of autologous PRS. tSVF was mechanically isolated by means of the fractionation of adipose tissue (FAT) procedure. Clinical evaluation was done using a visual analogue score (VAS) score, an adapted Western Ontario and McMaster Universities Osteoarthritis index (WOMAC) and Lysholm score at fixed time points: pre-injection as well as three, six and twelve months post injection. (3) Results: VAS and WOMAC scores improved significantly after twelve months (\(p<0.01\) and \(p<0.05\)). Lysholm instability scores were also improved at twelve months (\(p>0.05\)) in comparison to pre-injection measurements. No complications were seen in any of the patients. One patient was excluded due to a total knee arthroplasty. (4) Conclusions: a single injection with PRS for OA of the knee seems to lead to an improvement of function and simultaneous reduction of pain and joint stiffness for a period of twelve months. Further controlled trials are required to determine the optimal treatment regimen and evaluate long-term results.  
SVF; PRP; knee osteoarthritis; regenerative
# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 4 Proposed Approach
### Latency Neighbor Discovery
#### 4.2.1 Initialization Process  
When a name resolution node is first started, there is not much information about other name resolution nodes, so it is necessary to initialize its latency neighbors. An important feature that distinguishes LNMRS from other NRS architecture is that the latency constraint of the nested tree structure of LNMRS guarantees a deterministic latency upper bound. We notice that after the process of dividing the resolution domain, name resolution nodes in the same resolution domain already imply a certain degree of proximity property in terms of latency. We take full advantage of this property by relying on the nested tree structure of the system for initialization: each name resolution node obtains its list of latency neighbors from its parent node in the LNMRS nested tree structure, and then communicates to the parent node and all nodes in this list to obtain their children to join the set of latency neighbor candidates, which has the same name resolution service level as the current name resolution node. The current name resolution node measures the latency of each node in the latency neighbor candidate set, adds the nodes satisfying Equation (10) to the latency neighbor list, and records the corresponding communication latency values.  
This latency neighbor discovery algorithm is a distributed operation. If the parent node of a name resolution node has not yet completed its discovery of latency neighbors, the current name resolution node needs to retry the communication after waiting. In addition, if a name resolution node performing the initialization process is located at the highest level of the LNMRS nested tree, the node is a root node, and the node needs to query the other root nodes in the system through a root index server. In LNMRS, the root index server only provides a query service to provide information about the root nodes, including--but not limited to--the node identifier and network address. However, to avoid overloading, the root index server is not responsible for complex topology computation and an indication of latency neighbor structure. The above algorithm is described in detail in Algorithm 1.  
```
Input:\(MN_{i}^{l}\), \(T=\{t_{1},t_{2}\ldots\ t_{L}\}\)m Output:neighbors
1:initialization: neighbors \(\leftarrow\)null
2:if\(l==L\)then
3:roots \(\leftarrow\) query roots from the root index server
4:for\(r\) in rootsdo
5:if\(d\left(MN_{i}^{l}\right)\), \(c\)\(<\ t_{L}\)then
6:add\(r\) to neighbors
7:else
8:candidates \(\leftarrow\)\(MN_{i}^{l}\), siblings
9:for\(m\) in \(MN_{i}^{l}\),parent.neighborsdo
10:add\(m\).children with a number up to \(m\) to candidates
11:for\(c\) in candidatesdo
12:if\(d\left(MN_{i}^{l},\ c\right)\)\(<\ t_{L}\)then
13:add\(c\) to neighbors
14:returnneighbors
```  
**Algorithm 1**Initialization Process.
## 3 Underwater Routing Protocols
### Nefp  
The novel efficient forwarding protocol (NEFP) performs three tasks: defines the routing zone to avoid unnecessary forwarding, calculates holding time to avoid packet collision and uses Markov chains to estimate the forwarding probability of packets in the varying topology [17]. However, the performance of the proposed protocol degrades in sparse conditions when the probability of forwarding a packet fairly reduces as forwarders are less likely to find in the defined forwarding zones.
## 2 Description of Flywheel Energy Storage Systems
### Structure and Components of FESS: Priciples and Components of FESS
#### 2.2.2 Electric Machine  
The flywheel rotor is coupled to an electrical machine (integrated MG) to help with the charging process and control the energy conversion of the FESS. The FESS is charged by extracting energy from an electrical source and accelerating the high inertia rotor of the flywheel. It is discharged by drawing the accumulated energy back from the flywheel rotor using the same MG, operating as a generator. Commonly used electrical machines operating in FESS are permanent magnet machine (PM), induction machine (IM), and variable reluctant machine (VRM) [24; 35]. The PM machine with attributes of high power density, high efficiency, and low rotor losses is the most widely used electrical machine in flywheels. The main types of permanent magnet machines for FESS applications are permanent magnet synchronous machine (PMSM), brushless DC machine (BLDCM), and Halbach array machine (HAM). PM is abundantly used in high speed applications because of speed limitations of IM and torque ripple, vibration, and noise of VRM. The problem with PM machine is electromagnetic drag torque losses due to eddy current and iron losses in the stator, high price, and low tensile strength of the magnet, which must be contained by a containment structure [35]. This higher idling loss disadvantage is less of an issue if the number of daily charge-discharge cycles is high, and can be compensated by the higher efficiency of the energy transfer. To take advantage of both PM and VRM, hybrid PM reluctance machines have been recently developed. A comparison between PMs, IMs, and VRMs is presented in [38], while some other unconventional electrical machines used in FESS are discussed in [39]. Some of the recent developments of electric machines for FESS are studied in [40].
# Joint Communication-Motion Planning in Networked Robotic Systems
## 2 Conventional Relay-Assisted Robot System
### Performance Assessment: Orient  
As the JCMP aims at minimizing the total energy consumption, including both the communication energy consumption and the motion energy consumption, which would be assessed in the orient procedure.
# Real-Time Security Health and Privacy Monitoring for Saudi Highways Using Cutting-Edge Technologies
## 3 Proposed Methodology
### Trusted Authorities  
The fourth layer of the RTSHPMP is TAs, TAs are the governmental bodies responsible for monitoring highway traffic and providing legal and medical help to the passengers traveling on these roads [38; 39; 40; 41]. If any unusual event (accident, robbery, etc.,) occurs on the road, TAs receive an alarm. After getting intimation of an unusual event, TA analyses the data stored on the cloud, including vehicle and passenger information and videos of the incident. TAs also take the data of other vehicles traveling on the same highway to collect the pieces of evidence for the event that occurred. Based on the analysis of collected data, TAs decide to inform the related local and national authorities (e.g., police, first aid, hospital, fire brigade, etc.).
# Production of a Human Metabolite of Atorvastatinin by Bacterial CYP102A1 Peroxygenase
## 2 Materials and Methods
### Hydroxylation of Atorvastatin by CYP102A1  
The CYP102A1 mutants' catalytic activity in the hydroxylation of atorvastatin was supported by NADPH regeneration system or hydrogen peroxide. The reaction mixtures include 200 uM of atorvastatin and 0.20 uM CYP102A1 in 100 mM of a potassium phosphate buffer (pH 7.4), and were pre-incubated for 5 min at 37 \({}^{\circ}\)C. Hydroxylation of atorvastatin with an NADPH regeneration system (0.5 mM NADP\({}^{+}\), 10 mM glucose-6-phosphate, and 1.0 IU yeast glucose-6-dehydrogenase/mL) was used to initiate reaction. The reaction mixtures were incubated at 37 \({}^{\circ}\)C for 30 min and stopped by 600 uL of ice-cold ethyl acetate.  
The hydroxylation of atorvastatin was supported by hydrogen peroxide; an aliquot of 10 mM H\({}_{2}\)O\({}_{2}\) initiated the reactions. The reaction mixtures were incubated at 37 \({}^{\circ}\)C for 10 min and stopped by 600 uL of ice-cold ethyl acetate. Quercetin (50 uM) was added as an internal standard to this solution. Then, the mixtures were vortexed for 3 min. After centrifugation (3000\(\times\) g, 10 min), aliquots (350 uL) of the ethyl acetate layer were transferred to a clean glass tube and the ethyl acetate was removed under a gentle stream of N\({}_{2}\) gas. Each sample was dissolved in 170 uL of 30% of HPLC mobile phase (see below) and 30 uL was used for the quantitation of the samples.  
The atorvastatin's metabolites were analyzed by HPLC, using a Gemini C18 column (4.6 \(\times\) 150 mm, 5 um; Phenomenex, Torrance, CA, USA) with the mobile phase A (79.9% water, 20% acetonitrile, and 0.1% formic acid) and the mobile phase B (9.9% water, 90% acetonitrile and 0.1% formic acid). The elution rate was 1.0 mL/min by a gradient pump (LC-20 AD; Shimadzu, Kyoto, Japan) with the following gradient: 0-8 min, 30% mobile phase B; 8-13 min, gradually increased to 60% mobile phase B; 13-20 min decreased to 30% mobile phase B; and 20-30 min fixed at 30% mobile phase B. The UV at 260 nm detected the eluent. The CYP102A1 mutants used in this study are shown in Supplementary Table S1.  
Calibration standards of atorvastatin and quercetin were constructed from a blank sample (a reaction mixture without substrate and internal standard), nine samples of atorvastatin (2-500 uM), and thirteen samples of quercetin covering 0.2-500 uM (Figure S2). The peak area ratio of atorvastatin to internal standard (quercetin) was linear with respect to the analyte concentration over the range of 0.2-500 uM. When 2.4 volume of ethyl acetate was used to extract atorvastatin and internal standard in buffer solution, the extraction efficiencies of atorvastatin and internal standard were 79% and 74%, respectively, at the concentrations used in the assay. Quantitation of the metabolites was done by comparing the peak areas of each metabolite to mean peak areas of the internal standard (50 uM). The detection limit of 4-OH atorvastatin was 0.20 uM.
## 2 Materials and Methods
### Soil Moisture Forecasting (SMF) Model Construction
#### 2.2.1 Approaches for Modeling Soil Moisture  
The SMF model is a time-series data forecasting application. Several clustering methods can be used for modeling time-series data. A statistical framework is the most common approach for cluster analysis, and this can use either a parametric model-based approach or a nonparametric density-based approach [24]. The model-based approach uses a finite mixture model to fit the data and identify the properties to determine the clusters [25]. The density-based approach finds the relationship between the possible clusters in the data and the characteristics of the underlying probability density function [26]. In most density-based clustering approaches, two data points are grouped into the same cluster if they can be connected by a chain of data points [27].  
The challenge for density-based clustering is understanding how to estimate the right density function from a finite sampled dataset. To do this, k-nearest neighbor density estimation is a commonly used approach. [PERSON] et al. used the k-nearest neighbor density estimation to measure the prominence of the clustering structure in the data to seek meaningful information [28]. However, the difficulty of identifying the true high-density area increases when the dimension of the dataset grows. Graph theory can help to decide the connection of the components in density-based clustering [29]. Here, the data points are vertices, and an edge is created between two vertices if their distance is below a predefined threshold value. The modeling approach in this paper used this mechanism. We focus on the scenario that uses the soil as the planting media. It can be applied for plants such as the root and stem vegetables, which are not suitable for the containerized soilless planting.  
The above methods attempt to find the relationship from the whole sampled dataset. However, high dimension data has a great impact on forecasting accuracy. Therefore, our experiment also extracted many short segments from the whole continuously sampled moisture dataset to model the variation of soil moisture and establish a suitable segment size from many experimental results. The next section introduces the details of this model.
## 4 Discussion  
This paper explored using Hyperledger Fabric blockchain technology to develop a digital product passport (DPP) to improve how products are managed from the start to finish of their lifecycle. We used a new type of product ID, called decentralized identifier (DID), to track products securely and accurately. The setup involved two blockchains: one for handling DIDs and the other for product information.  
Practical implementation was focused on building the smart contract and data model associated with the DPP. The proposed data model includes details about the product, its maker, suppliers, and the materials used. When the system was tested on the Hyperledger Fabric test network, it proved to be effective and able to handle lots of parallel requests.  
The performance analysis in this paper primarily focused on the response times of CRUD operations in a controlled test environment. While this provides valuable insights into the system's capabilities under specific conditions, it does not fully account for the scalability issues that may arise in a real-world scenario with a significantly larger number of nodes. Future performance evaluations should consider the following aspects: throughput (the rate at which transactions are processed by the network), consensus overhead (the additional time and resources required to reach consensus as the network grows), storage requirements (the increased storage needs for maintaining a copy of the entire blockchain ledger on each node), and network latency (increased communication delays between a larger number of nodes).  
The present implementation solely encompasses the backend, as outlined in this paper's solution. Subsequent efforts will extend to the development of client applications, including a web application for DPP management and a mobile application enabling users to access DPPs through QR code scanning.
## 3 Methodology
### Classification Experiments
#### 3.5.1 Zero-Shot Classification  
Initially, zero-shot classification was performed on the CARE dataset [27] using pre-trained BERT and BART models to establish baseline performance. This step allows us to compare the performance of models without any fine-tuning, providing a reference point for evaluating the impact of our synthetic datasets.
# Semantics-Preserving RDB2 RDF Data Transformation Using Hierarchical Direct Mapping
## 4 Hierarchical Mapping Rules
### Soundness and Completeness of the Rules  
Lemmas describe the features of semantic resources during the mapping process. Lemma 1 states that every semantic resource can be inferred from the OWL class. Lemma 2 states that every semantic resource referencing other resources can be typed by the OWL object property. Lemma 3 states that every subgraph, which has the same semantic resource as a root node, can be merged into a single graph. On the other hand, the problems described in Section 3.3 are specific cases of violation of semantics preservation. Problem 1 illustrates the loss of information when the relational tables are transformed without considering Lemma 1. Problem 2 illustrates another case of loss of information when attributes, binary relations, or other referencing objects are transformed without considering Lemma 2. Problem 3 illustrates incorrect RDF data generation when integrity constraints are transformed without considering Lemma 3. Therefore, mapping rules are defined based on the lemmas to perform semantics-preserving transformation of RDBs to RDF data and to avoid the loss of semantics or incorrect RDF data generation. Lemma 4 demonstrates the soundness and completeness of the provided RDB2 RDF data transformation methods (see the Appendix F for proof).  
* Lemma 4: Consider that X is a set of relational data, F is an RDB2 RDF mapping function, and G is an RDF2 RDB inverse-mapping function of F. If the mapping rules are defined based on Lemmas 1, 2, and 3, then, 1. Soundness: the mapping rules are sound if the rules generate only semantics in RDB data (X \(\supseteq\) G(F(X))).
2. Completeness: the mapping rules are complete if the rules generate all semantics in RDB data (X \(\subseteq\) G(F(X))).
# Vision-Aided Localization and Mapping in Forested Environments Using Stereo Images
## 1 Introduction
### Simultaneous Localization and Mapping  
SLAM is a critical problem in robotics, which involves constructing a map of an unknown environment while concurrently determining the robot's location within it. If the robot's position and orientation are known at any given time, mapping its surroundings using sensor data becomes a straightforward task. Conversely, if the map is already known, the problem is reduced to localization, where the robot's observations are utilized to determine its position and orientation within the map. When neither the pose nor the map is known, both the map and path must be estimated simultaneously. SLAM can be broadly classified into online and full SLAM problems. Online SLAM incrementally estimates the current pose along with the map, while full SLAM estimates the entire path and map using data from all poses and observations [46]. There has been research in forest harvesting related to the localization problem [47; 48]; however, a stem map is required in order to localize the machine, which we consider to be a major limitation of such an approach since stem maps, in general, are not readily available.  
Visual odometry (VO) and vision-based SLAM are closely related, as VO provides an estimate of the robot's path. The key difference is that SLAM mandates maintaining a map--even if it is not ultimately needed--so that the robot can recognize previously explored areas. When the robot revisits an area on the map, it provides additional constraints to optimize the trajectory and map, ensuring global consistency. This process is known as loop closure. Generally, VO is focused on local consistency, while SLAM aims for global consistency. It is worth noting that if VO is free from drift, the SLAM problem is reduced to merely mapping observations. However, error-free VO has never been achieved in practice, making loop closures essential for maintaining global consistency. To recognize loop closures, robots need to operate in environments containing distinct features that can be reidentified upon returning to a previously mapped location.  
Detecting loop closures in forested environments can be challenging, as the spatial configurations of features, such as trees, may not be unique enough to provide robust information for redetection. Integrating GNSS positioning with SLAM can assist in identifying loop closures by maintaining a globally consistent position path. In this paper, we demonstrate how intermittent and degraded GPS reception can be fused with VO to provide a globally consistent position estimate. Additionally, we present a graph-based SLAM algorithm for refining the estimated path and map simultaneously. For an introductory tutorial on graph-based SLAM, readers can refer to [49].