esa-sceva/satcom-chunk-collection huggingface dataset:
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 5 Experiment and Analysis
### The Performance Comparison of WFO, FQ, and FCFS
#### 5.3.2. WFO vs. FQ and FCFS: Impact of the Bandwidth of Cloud on Delay  
In this section, the CC bandwidth was used as a variable to measure the delay of various data offloading strategies. The experiment involved 100 and 150 nodes accessing the CC at the same time, while the bandwidth of the CC ranged between 100 MB and 850 MB. The data-tolerance delay was set to 20 ms, and the experiment results are presented in Figure 8.  
Figure 8 shows the proposed WFO strategy in yellow, the FQ strategy in red, the FCFS strategy in green, and the WFO with HPC in orange. Overall, as the bandwidth of CC increased, the offloading delay of data for all three strategies decreased significantly. The delay for FQ and FCFS decreased significantly, while the delay change for WFO was relatively small due to the increase in the number of nodes accessed simultaneously by FQ and FCFS with the increase in CC bandwidth. The bandwidths of the two nodes gradually met the requirements of all node accesses, resulting in rapid performance improvement. WFO initially performed exceptionally well, with a delay of 78.9 s when the CC bandwidth  
Figure 8: WFO can maintain a stable delay under crowded (100–500) and loose (500–900) bandwidth conditions. In the event of congestion, performance increases by 80%. (**a**) Node = 100 & Data = 300; (**b**) Node =150 & Data = 300.  
Figure 7: WFO vs. other strategies: with the increase of data, the performance advantage of WFO increases gradually. The maximum delay reduction is 73% and 75%, respectively. (**a**) Bandwidth = 150 & Node = 100; (**b**) Bandwidth = 150 & Node = 150.  
was 100 MB/s. FQ and FCFS had delays of 447 s and 515.9 s, respectively. WFO outperformed FQ and FCFS by 82.3% and 84.7%, respectively. Similar results were obtained in other scenarios where an increase in bandwidth led to a slight decrease in WFO. However, the delay was still better than that of FQ and FCFS, which proves that WFO can leverage the characteristics of cloud edge architecture to achieve a stable data offloading capacity. Although WFO has a partial performance loss compared to WFO (optimal), it is still entirely acceptable. Hence, the proposed algorithm HPC holds practical significance.
# Research on Co-Interactive Model Based on Knowledge Graph for Intent Detection and Slot Filling
## 3 Cimkg
### Knowledge Graph-Based Shared Encoder Module
#### 3.1.1 Knowledge Layer  
In the knowledge layer, entities from the domain-specific knowledge graph are linked to entities in the input utterance to construct a sentence tree that is rich in semantic information. This process primarily consists of two steps: knowledge query (K-query) and knowledge injection (K-injection). For an input sentence \(s=\{w_{1},w_{2},\ldots,w_{n}\}\) and a given domain-specific knowledge graph \(K\), the output sentence tree is represented as \(T=[w_{1},w_{2},\ldots,w_{i}[(r_{i1},w_{i1}),\ldots,(r_{ik},w_{ik})],\ldots,w_{ n}](1\leq i\leq n)\) after undergoing knowledge query and knowledge injection. Here, \(n\) denotes the number of tokens in the query, \(w_{i}\) represents an entity, \(w_{ik}\) represents an ontology label, and \(r_{ik}\) represents the relationship between the intent and slot.  
The knowledge query process involves retrieving and acquiring knowledge triplets from domain-specific knowledge graph \(K\) that are related to all named entities in utterance \(s\). This process can be formally described as shown in (1):  
\[E=K\_Query(s,K) \tag{1}\]  
where \(E=[(w_{i},r_{i1},w_{i1}),\ldots,(w_{i},r_{ik},w_{ik})]\) is a collection of corresponding triples related to the entities in utterance \(s\).  
In the knowledge-injection process, the acquired knowledge triplets \(E\) are injected into the corresponding positions of the utterance \(s\) to obtain a sentence tree \(T\). This process can be described as shown in (2):  
\[T=K\_Inject(s,E) \tag{2}\]  
Using \"What causes high HbA1c levels in patients?\" as an example, the sentence tree structure formed after importing knowledge graph triplets is shown in Figure 4.  
Figure 3: Knowledge graph-based shared encoding process.  
Here, \([CLS]\) is a classification token for intent detection, the red numbers represent the readable order, and the black numbers represent the actual order. The knowledge sentence after the injection of the knowledge graph is as follows.  
What causes high Hb1c is glycated hemoglobin for glucose management levels in patients?
# Activation of Persulfate for Groundwater Remediation:
## 3 Applications and Development of Persulfate in ISCO
In practical site remediation projects, persulfate is often activated by a combination of multiple methods rather than a single method. [PERSON] (2006) used the iron-heat-peroxide combined methods to activate persulfate and successfully reduced the contaminants concentration below the risk-based target (86-96% removal) [113]. Hydrogen peroxide and Fe\({}^{2+}\) were also used in combination when persulfate was applied to remediate TCE [114]. The persulfate-based ISCO technology can be enhanced by the use of surfactants, which increase the solubility of hydrophobic contaminants and thus better enable them to come into contact with free radicals [115]. At a former truck service facility site in New Jersey, surfactant-enhanced ISCO technology was used with a low persulfate to contaminants ratio (4.2:1) to clean up the extractable petroleum hydrocarbon (EPH). After seven days, the average concentration of the EPH in the soil was decreased from 5075 mg/kg to 3098 mg/kg [109]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline
\begin{tabular}{c} Na\({}_{2}\)S\({}_{2}\)O\({}_{8}\)- \\ NaOH-17,034 L \\ \end{tabular} & & & & \\ \hline \end{tabular}
\end{table} 
Table 5: Pilot scale and site applications with persulfate.
## 3 Results and Discussion
### Spectral Data of Pre-Treated Samples  
As can be observed in Figure 2, there exists a clear effect of the scattering baseline due to the grain size of soybean and oatmeal [46]. Accordingly, it was helpful to carry out a pre-treatment of the raw data to reduce this effect. Some alternatives are recommended, such as standard normal variation (SNV) and SNV + detrending (two orders) [24; 47]. In Figure 3a, we can see that the vertical displacements of the baseline of the spectrum were almost completely corrected, and the differences between the mean spectra of each sample began to be more noticeable after the SNV pre-treatment was employed. The SNV pre-treatment managed to reduce the changes in the baseline, but we can still observe an increasing trend in the slopes of the spectrum bands; this behavior could affect the classification of the sample groups into their main components in the PCA analysis. Therefore, to reduce the changes in the slopes of the spectra in Figure 3a, we applied an SNV + detrending (two-order) pre-treatment. Furthermore, in Figure 3b, the effect of the baseline is visualized; this could be corrected using the first derivative of Savitzky-Golay. This pre-treatment sequence was performed on the raw data, as shown in Figure 3a-c. Differences in absorption spectrum intensities between the samples are best depicted in Figure 3c.  
Figure 3: _Cont._Figure 3: Pretreatment of spectra for each sample. (**a**) SNV, (**b**) SNV + detrending (2-order), and (**c**) SNV + detrending (2-order) + Savitzky–Golay first-derivative with 19 smoothing points.
## 6 Experiment and Analysis
### Implementation Details  
For the entire optimization problem, we set the following parameters: \(m=3\), \(B=10^{6}\), \(r_{max}=1\) km. To balance the profit term, loss term, and fairness term, we set \(c_{1}=c_{2}=c_{3}=\frac{1}{3}\).  
In our proposed PPO-Attention algorithm, we utilize the following parameters: \(Update\;Steps=512\), \(Batch\;Size=128\), \(Learning\;Rate=0.002\), and \(\gamma=0.8\). Moreover, the encoder MLP and decoder MLP both have a size of \(64\times 64\). The attention layer incorporates two heads, with \(d_{k}=32\). The total training timesteps for all three algorithms are set to \(10^{5}\).  
For the genetic algorithm employed as a baseline, we encode and perform crossover operations on the population using an MLP with three linear layers, each consisting of 64 units. The parameters used in the genetic algorithm are as follows: \(Number\;of\;Generations=20\), \(Population\;Size=100\), \(Crossover\;Rate=0.8\), \(Mutation\;Rate=0.01\), \(Mutation\;Factor=0.001\), and \(Maximum\;Global\;Steps=5\times 10^{4}\). The parameters for the profit-based greedy algorithm and demand-based greedy algorithm remain consistent with those of the PPO-Attention algorithm, except for the differences in policy selection.  
All experiments were conducted on a platform equipped with an Intel Core i7-12700 CPU, NVIDIA GeForce RTX 3070 Ti GPU, and 32 GB memory.  
Meanwhile, a few indicators are used to evaluate the performance of the algorithm:  
* The objective function \(gain(p)\). The value of \(gain(p)\) denotes the overall performance of the model. The higher the score is, the better.
* Benefit of the \(gain(p)\): the sum of the profit term \(profit(p)\) and fairness term \(fairness(p)\) in the objective function, which represents the positive impact of the current solution. Higher values indicate better performance.
* Time cost \(cost(p)\). \(cost(p)\) contains following parts: travel time, which is the sum of travel times within the road network, charging time, which is the sum of the charging times within the road network, and waiting time, which is the sum of the waiting times occurring at all CS in the road network. Additionally, we calculated the longest travel time and longest queuing time throughout the entire process. For all the time terms, a lower score is better.
## 5 Discussion
### Computational Cost Evaluation  
The simultaneous collaboration between the RGB-D skeletonization method and proposed SSSE method is crucial for the real-time deployment of the fusion application. Thus, limiting the computational cost is essential for the effectiveness of the fusion method. The test platform is a mainstream personal laptop connected with the first generation Kinect, equipped with one Intel Core i7 central processing unit (CPU) and 16 Gigabyte of random access memory (RAM). Two indicators, i.e., maximum process capability per second and single frame delay are concerned in order to evaluate the computational cost. This evaluation test aims to process as many frames as the computational capability allows based on the proposed method. The computation cost efficiency of the fusion application is determined by the number of frames processed per second. As shown in Figure 8, the stable maximum process capability remains around 25 frames per second after the initial stage where less than 10 frames are processed per second. The experimental result indicates that the fusion application is feasible for real-time deployment based on its stable maximum process capability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test Set** & \multicolumn{3}{c}{**Joints**} & \multicolumn{3}{c}{**Frames**} \\ \hline
**Set No.** & \(J_{R}\) & \(N_{Ej}\) & \(N_{rj}\) & \(E_{rj}\) & \(F_{R}\) & \(N_{Ef}\) & \(N_{rf}\) & \(E_{rf}\) \\ \hline  
Total & 0.8149 & 31152 & 28848 & 3460 & 0.7655 & 24415 & 22651 & 3962 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Result of unreliable joint position redress. \(J_{R}\) is the precisely recovered joint rate. \(F_{R}\) is the precisely recovered frame rate. \(N_{Ej}\) is the number of unreliable joints. \(N_{Ef}\) is the number of frames affected by unreliable joints. \(N_{rj}\) is the number of total recovered unreliable joint. \(E_{rj}\) is the number of inaccurately recovered joints. \(N_{rf}\) is the total number of recovered frames. \(E_{rf}\) is the number of frames containing inaccurately recovered joints.
## 3 Simulation-Based System Architecture
### Characteristics Marked by the Scenario and the Demand  
Requests issued by the system users will be assumed to contain the following basic information: origin and destination locations of the journey and also the latest possiblearrival time \(\hat{t}\) at the destination. Then, according to the characteristics of a given journey request within an urban area, the dispatching system may consider dividing the journey into up to 3 legs and will provide answers to the trip requests in the form of:  
* **1-Leg Trips (Unimodal)** of the form **RP**, when using exclusively the ride-pooling service, or **PT** when using exclusively the public transport.
* **2-Leg Trips (Intermodal)** of the form **RP + PT**. The journey's first leg uses the ride-pooling service, taking the passenger to a proper transfer point or hub of the public transportation network (referred to as PT-AN in the following) to further continue the route by public transport and reach its final and near destination by foot. In the inverse case, of the form **PT + RP**, the journey begins by walking to a PT-AN and then continuing the route by public transport until a final destination stop, switching there to the ride-pooling service to get its final destination.
* **3-Leg Trips (Intermodal)** of the form **RP + PT + RP**. The journey uses ride-pooling as both the first and last leg. First, a vehicle would pick up the customer at its origin to take him to a PT-AN, where a leg in public transport will start until the proper alighting point. There, another RP vehicle would take him to its final destination. A representation of this option is shown in Figure 1 below.  
Additionally, trip requests have been divided into two types depending on how long in advance they arrive to the system:  
* **Type HN**, for 'Here and Now'. Requests are placed at that instant and dispatched immediately.
* **Type B**, for 'Booking'. Requests are placed well in advance.  
A customer's trip preference has also been taken into account. This may determine the intermodality type in some cases. The following categories have been considered:  
* **a. Pure-RP**. Clients want an RP-exclusive trip. Their trips must be composed of a single leg (RP-only).
* **b. Including PT**. Clients want a trip with PT included. Their trips must be composed of either one leg (PT-only), two legs (RP + PT or PT + RP) or three legs (RP + PT + RP).
* **=. Indifferent.** Clients have no preference. Their trips can be made up of one, two or three legs of any type.
# Assessment of Nerve Repair Augmented with Adipose-Derived Mast Cells in an Animal Model
## 3 Results  
### Mast Cells  
#### 3.1.1 Assessed by Luxol Fast Blue  
Microscopic evaluation of the normal nerves--the control group showed a low density of mast cells in the nerve and the adjacent tissue. The mast cell density inside the nerve was on average 2-3/microscopic field \(\times\)200.  
At 4 weeks, we found an average of 9.25 mast cells/microscopic field \(\times\)200 on the side with simple nerve suture, with one case showing 22 mast cells/microscopic field \(\times\)200. Repairs augmented with the adipose graft showed an average of 16 mast cells/microscopic field \(\times\)200 (Table 1, Figure 2). The total number of mast cells was obtained by considering both nerve tissue and adipose tissue.  
At 10 weeks, the number of mast cells increased both at the site of simple nerve suture and at the site of adipose augmentation. Simple neurorrhapby cases had an average of 23 mast cells/microscopic field \(\times\)200. Neurorrhapby plus adipose had an average of 27.6  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Control Group** & **Right Sciatic Nerve—Only Nerve** & **Left Sciatic Nerve—Nerve Stuture Plus** \\  & **(n = 8)** & **Suture** & **Adipose Graft** \\ \hline  & & **4 Weeks** & **10 Weeks** & **4 Weeks** & **10 Weeks** \\  & & **(n = 8)** & **(n = 8)** & **(n = 8)** \\ \hline  & & & & Nerve 9.75 & Nerve 16 \\ Luxol fast blue & 2–3 & 9.25 & 23 & Adipose tissue 6.25 & Adipose tissue 11.6 \\  & & & & Total 16 & Total 27.6 \\ Mast Cell Tryptase & 0 & 4 & 4.5 & 2.5 & 0.2 \\ CD34+ & 1–2 & 4 & 7 & 5.5 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The average number of mast cells (both luxol fast blue and tryptase positive) and CD34+ cells per microscopic field at \(\times\)200 magnification.  
total mast cells/microscopic field \(\times\)200: 16 from neural tissue and 11.6 from the adipose graft (Table 1, Figure 3).  
Figure 3: An example of sciatic nerve treated by neurorrhapy at 10 weeks. The addition of adipose tissue yields a higher number of mast cells, inside the nerve and in the tissue surrounding it (luxol fast blue, magnification \(\times\)200).  
Figure 2: Aspects at 4 and 10 weeks for the right **(a,b)** and left **(c,d)** sciatic nerves; in this example, there is a higher concentration of mast cells positive to luxol fast blue in the suture augmented with adipose tissue.  
3.1.2. Assessed by Mast Cell Tryptase  
There were no mast cell tryptase positives in the normal nerves--control group per microscopic field \(\times\)200.  
At 4 weeks, we found an average of 4 mast cell tryptase positives in the simple nerve sutures and 2.5 in the repairs augmented with an adipose graft. The total number of mast cells was obtained by considering both nerve tissue and adipose tissue.  
At 10 weeks, we found an average of 4.5 mast cell tryptase positives at the site of simple nerve suture and 0.2 at the site of adipose augmentation (Table 1).
# Pseudorange Measurement Method Based on AIS Signals
## 2 Principle of Pseudorange Measurement in AIS-R
### Measurement Scheme  
The specific program for AIS signal transmission delay measurement is shown in Figure 2.  
In this scheme, the pre-filter is used to suppress the interference caused by various types of noise in the Gaussian baseband waveform. Taking into account that the ideal baseband waveform should be a Gaussian waveform, the pre-filter uses a Gaussian low-pass filter.  
In order to get these timestamps in a smooth Gaussian baseband waveform, we proposed two detection algorithms (zero-crossing detection and differential peak detection) to detect the bit-0-phases in the baseband waveform. As one of the key research contents of this research, the mathematical  
Figure 1: AIS digital message sequence and Gaussian baseband waveform. (**a**) AIS message digital sequence waveform; and (**b**) Gaussian minimum shift keying (GMSK) baseband wave form.  
Figure 2: Scheme block diagram.  
model of these schemes is analyzed theoretically in Section 3. In Section 4, the detection accuracy of these schemes under AWGN channel is simulated and analyzed.  
Since the existing AIS system is a communication system rather than a professional pseudorange measurement system, the detection accuracy of a single timestamp cannot meet the requirements of measurement accuracy. Considering the slow-motion characteristics of the ship, the optimal estimation algorithm is used to improve the estimation accuracy of the detection algorithm. The corresponding contents are discussed in detail in the Section 5.
## 2 Related Work
### Routing Protocols Based on Heuristic Algorithm
A dynamic route adjustment method is proposed in [24], the algorithm divides the nodes into several groups according to the location information and chose the node with most residual energy as the CH among each group. A greedy algorithm is used for CH to generate a chain to transmit data to the mobile sink and the CH which is nearest to the mobile sink is the chain leader. The topology of the network will not change until the chain leader changes.
## 1 Introduction
However, for satellite-based TIR observations, the stress-induced thermal radiation intensity is relatively weak compared with weather-induced changes and influenced by multiple factors. Therefore, it remains a challenge to extract the stress-induced TIR information under a strong interference background and detect crustal tectonic activity. Based on the above purpose, the study was performed to explore the possibility of extracting stress-induced weak TIR signals under a strong interference background in the experimental condition. We also considered the periodic stresses that are commonly encountered in crustal and rock engineering, including periodic changes in the tidal forces due to celestial bodies [39; 40; 41], the effects of repeated loading and unloading of the dam basement during drainage [42], and the effects of cyclic loading due to explosions on open mine slopes and reserved pillars [43]. In addition, studies have shown that seismic activities are periodic [44; 45; 46]. Therefore, the cyclic loading method is adopted to observe the TIR changes for the rock using an infrared thermal imager and to extract the relationship between the TIR change and stress. Considering the temporal features of stress change, the wavelet analysis method is performed to decompose the original TIR signals and to extract the relatively weak TIR related to stress in the strong interference background. The results could provide an experimental basis for monitoring crustal stress, mining, and rock engineering via thermal infrared remote sensing.