esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of the Results  
Some questions arise from the obtained results, which are mainly related to the presence of rutile and its influence on the photocatalytic properties, as well as the influence of the heat treatment on the degradation of the Tartrazine dye. These problems have been discussed in several papers, but up until now, most of them have presented contradictory opinions. Additionally, successful degradation of the chosen dye has been achieved mainly by CuO, (Ce, Ag) and co-doped ZnO catalysts [44]. There are a few investigations  
Figure 8: Photocatalytic degradation rate of Tartrazine for three consecutive cycles using powders annealed at 500 \({}^{\circ}\)C (**a**) and 700 \({}^{\circ}\)C (**b**).  
concerning the photocatalytic degradation of Ag- and chitosan-modified TiO\({}_{2}\) against Tartrazine [45; 46]. Bearing in mind our results, it might be concluded that the heating rate plays a significant role in the photocatalytic performance of the as-prepared powders. It is well known that the presence of a high crystalline structure improves carrier migration, which plays an important role in the photocatalytic performance [47]. According to the above data, the photocatalytic tests demonstrated that the samples annealed at 500 \({}^{\circ}\)C display better photocatalytic activity than those annealed at 700 \({}^{\circ}\)C. Generally, a decrease in photocatalytic activity might originate from the degree of crystallinity, surface morphology, particle size, band gap energy and intrinsic defects [48]. Usually, all these factors provide a comprehensive understanding of these properties. However, it has been stated that it is still a challenge to correlate the surface defects with the photocatalytic activity [48]. It could be generalized that the photocatalytic activity of TiO\({}_{2}\) is dominated by the balance among all these factors. On the other hand, as the annealing temperature increases, the particles progressively grow in size. This is due to the increased kinetic energy of the particles resulting in thermal expansion, where the overall volume of the material increases. Subsequently, the crystallinity increases, which significantly reduces the surface area. Thus, the anatase is transformed into rutile, which additionally retards the photocatalytic degradation. Bearing in mind the hazardous effect of Tartrazine, especially its impact on food allergy, mutagenic, carcinogenic, and phototoxicity effects, more experimentation should be directed toward the selection of the dopant as well as the shape-control of the TiO\({}_{2}\) catalyst in addressing that problem. The main task for scientists is to collaborate to successfully eliminate dye effluents by developing materials and technology for a safer world.
# TRAP: A Three-Way Handshake Server for TCP Connection Establishment
## 5 Evaluation
### Evaluation Results  
We wrote a simple Python script to test a TRAP-enabled server and compare the results with the results without TRAP. Hence, we can compute the percentages of time that our module costs.  
The script will attempt to download a file hosted on a TRAP-enabled server, and the connection will be directed to the real server instead. On the same environment, we will record the time needed to download the file directly from the server to compare the differences. The files used for the test ranges from five megabytes to 100 megabytes of sizes. These files contain random contents generated from /dev/urandom. Table 1 shows the results of the overhead evaluation.  
In addition to overhead evaluation, we also conduct a performance evaluation simulating an ongoing DDoS attack.  
We use the hping program, an open-source network tool able to send custom TCP/IP packets. Hping is a command-line TCP/IP packet assembler/analyzer [23]. The tool is able to randomize each packet's source IP address and set its SYN flag and, thus, can be used to simulate SYN flood attacks. As an action, \"hping3-S -P -p 80 192.168.1.99 -i u10 -rand-source\", the parameter we used for hping to simulate a DDoS attack. \"-rand-source\" is used to spoof the packet's source IP, and \"-s\" is used to indicate the packet has a SYN flag. We sent a file of 5 MB with and without TRAP. It spent 253.03 ms with TRAP and spent 2935.06 ms without TRAP which gives an evident evaluation results while a DDoS simulated attack is undergoing.  
On the other hand, we processed an effective test among SYN cookies, SYN proxy, and our TRAP system. To calculate the average time period, we conducted a three-round TCP connecting and disconnecting experiment. Twenty connections were conducted in every round; hence, there were a total of 60 connection attempts for each of the three defense mechanisms. In this experiment, each target TCP server installed with one of the three defense mechanisms sent a response as soon as it received a connection request. We calculated the time period starting at the time a client sent a TCP connection request and ending at the time that the client received the connection response from the server. The averages of one TCP connection time are: 7683.6 \(\upmu\)s with TRAP, 7820.3 \(\upmu\)s with SYN Cookies, and 7978.3 \(\upmu\)s with SYN Proxy, from an experiment of 60 TCP connections for each security scheme. We found that TRAP is more effective than the others.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**File Size (M)** & **With TRAP (ms)** & **Without TRAP (ms)** & **Overhead (ms)** \\ \hline
5 & 225.70 & 77.92 & 147.78 \\
10 & 380.93 & 214.78 & 166.15 \\
50 & 748.70 & 567.42 & 181.28 \\
100 & 1360.13 & 1137.39 & 222.74 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of overhead.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Terms** & **20 mm** & **50 mm** & **100 mm** \\ \hline \(\text{CE}_{\text{Cd}}\) & 1295 ch & 1298 ch & 1291 ch \\ \(\text{CE}_{\text{EJ}}\) & 1430 ch & 1444 ch & 1434 ch \\ \(\text{QE}_{\text{Cd}}\) & & 0.15\% & \\ \(\text{QE}_{\text{EJ}}\) & & 0.2\% & \\ \(\text{LY}_{\text{EJ}}\) & & 10,000 photon/MeV & \\ Relative \(\text{LY}_{\text{Cd}}\) & 12,075 photon/MeV & 12,103 photon/MeV & 12,037 photon/MeV \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Compared relative light yield by distance to Cs-137 gamma ray source. at the Compton edge energy of Cs-137. In addition, a ratio above '1' means that the CdTe nanomaterial improved the light yield in the polymer corresponding to each channel. In this study, according to the Figure 10, it is shown that there is an improvement of more than 10% by CdTe nanomaterials. The count rate was greatly improved in the 477 keV Compton energy region by CdTe nanomaterials. In addition, the results analyzed below '1' indicate that the effect of nanomaterials may be the effect of light loss and signal overlap due to aggregation of nanomaterials. \[\text{Count ratio}=\frac{\text{count rate per channel }[\text{PS}(\text{PPO},\text{ POPOP})-\text{CdTe}]}{\text{Count rate per channel }[\text{PS}(\text{PPO},\text{ POPOP})]} \tag{5}\]  
CdTe nanoparticles have advantages such as the change of energy gap according to the size of nanomaterials, light emission due to the discontinuity in energy state density, increase in light efficiency, and light emission (even at room temperature) due to the increase in the binding energy of excitons. Until now, there have been a great deal of studies for use in CdTe nanoparticles, like optical materials, as shown in Table 4, and important studies are being conducted to improve the energy transfer rate by doping inorganic particles into CdTe nanoparticles. Figure 10: Count ratio from gamma spectra from a functional plastic scintillator. Figure 9: Normalized results from measured and simulated energy spectrum of commercial and CdTe-loaded plastic scintillator to Cs-137 gamma ray source. (**a**) Point source measurement using commercial plastic scintillator. (**b**) Point source measurement using a functional plastic scintillator including CdTe.
## 3 Results and Discussion
### The Impact of Plasma Treatment on the Activity of SOD and CAT  
SOD, a metal-based antioxidant enzyme, plays a vital role in regulating the equilibrium of reactive oxygen species (ROS) within the organism [29]. It is crucial for maintaining the equilibrium between oxidation and antioxidant processes. As shown in Figure 6a, both control and plasma-treated groups showed a steady increase in SOD activity with increasing storage time. After 16 days of storage, SOD activity was significantly enhanced in the treated group compared to the control group. After 16 d of storage, the SOD activity in the plasma-treated group increased by 52.63%, which significantly exceeded the level observed in the control group, which could be attributed to the promoting effect of plasma treatment. This result aligns with previous studies, which found that higher SOD activity decreased the germination rate of rice seeds, emphasizing the critical role of SOD activity in delaying seed germination [30].  
CAT activity showed a similar pattern. During the storage period, CAT activity showed a continuous increase in both plasma-treated and control groups. After 16 days  
Figure 6: The impact of plasma jet treatment on the activities of SOD (**a**), CAT (**b**), POD (**c**), and PPO (**d**) in potato tubers during a 16-day storage period.  
of storage, CAT activity was significantly enhanced in the treated group compared to the control group. CAT is a crucial enzyme in plants that catalyzes the breakdown of hydrogen peroxide, aiding in eliminating excess free radicals in the body [31]. The treatment group showed a rapid increase in CAT activity during the first 8 days, followed by a decline later in the storage period. This may be due to the temporary accumulation of hydrogen peroxide [32]. Plasma treatment can enhance the activity of antioxidant enzymes.
## 2 Methodology
### The WCDPSO Algorithm
#### 2.1.2 Linear Adjustment Learning Factor  
The linear adjusting learning factors method is proposed in order to search the large solution space and avoid falling into the local optimal value. The specific formulas are described as follows in Equations (3) and (4). The larger \(c_{1}\) is, the more powerful the local searching capability of particles is; the larger \(c_{2}\) is, the more powerful the global searching capability of particles is.  
\[c_{1}=c_{1\text{max}}+(c_{1\text{min}}-c_{1\text{max}})\bigg{(}\frac{i}{\text{ max}gen}\bigg{)} \tag{3}\]  
\[c_{2}=c_{2\text{max}}+(c_{2\text{min}}-c_{2\text{max}})\bigg{(}\frac{i}{\text{ max}gen}\bigg{)} \tag{4}\]  
where \(i\) is the current iteration number; max\(gen\) is the maximum iteration number; \(c_{1\text{max}}\) and \(c_{2\text{max}}\) are the initial values of \(c_{1}\) and \(c_{2}\), respectively; and \(c_{1\text{max}}\) and \(c_{2\text{max}}\) are the final values of \(c_{1}\) and \(c_{2}\), respectively. The performance of the PSO algorithm is improved by linearly adjusting the learning factor, then it is renamed as the CPSO algorithm.
## 3 Procedure, Data and Methodology
### Construction of Lexical Situation Type System
#### 3.1.2 Manual Annotation of Co-Occurrence Information
The aspectual marker \"LE\" can be divided into two separate words: \"LE1\", which functions as a modal particle at the end of sentences indicating the emergence of a new situation, and \"LE2\", which is an auxiliary word placed after the verb to indicate the completion of an action. Although [PERSON] [12] did not explicitly distinguish between the two, he noted that the \"LE\" in the above table should form a direct constituent with the verb. This means that the aspectual marker \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Aspectual Marker**} & **Necessity of Judging** & **Necessity of Judging the** & **The Meaning Expressed by** \\  & **Whether Verb and Aspectual** & **Meaning Expressed by** & **Co-Occurrence** \\  & **Marker Can Co-Occur** & **Co-Occurrence** & **Co-Occurrence** \\ \hline LE & Yes & Yes & Action Starts \\  & & & Action Finishes \\ Time-span Object & Yes & Yes & Duration of Action \\  & & & Duration after the End of the \\  & & & Action \\ ZHE & Yes & No & \\ ZAI/ZHENGZAI & Yes & No & \\ GUO & Yes & No & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: [PERSON] standard for annotating verb situation types [12]. \"LE\" includes two cases: \"LE\" functioning as \"LE2\" and \"LE\" combining both \"LE1\" and \"LE2\". It does not include \"LE\" functioning solely as \"LE1\". (2) In fact, many scholars argue that the time adverbs \"ZAI\" and \"ZHENGZAI\" differ in their semantics and co-occurrence capabilities with verbs. However, since both can indicate the progression of an action or the continuation of a state, we adhere to [PERSON]'s standard [12] and treat them as a single feature for the time being. (3) The aspectual marker \"GUO\" here is an auxiliary word. Some scholars argue that it should be divided into \"GUO1\" and \"GUO2\". \"GUO1\" follows a predicate to indicate the completion of an action, while \"GUO2\" follows a predicate to indicate a past experience [41; 42]. We do not make this distinction here, but it is important to clarify that the aspectual marker \"GUO\" does not include other homographs such as the directional verb \"GUO\". Next, we employ a manual annotation approach to label the co-occurrence capabilities of verbs with five aspectual markers. The annotators are required to make judgments based on intuition and introspection, avoiding interference from corpus data. Additionally, to conduct an internal consistency check on the data, we employed a systematic sampling method to select 20 verbs from the lexicon as repeated items. These verbs were then re-annotated by the annotators after a certain period following the initial annotation. The systematic sampling approach was able to avoid the selected verbs from suggesting co-occurring features with each other, which can prevent the failure of internal consistency checks of data.
# A Text-Generated Method to Joint Extraction of Entities and Relations
## 4 Experimental Setup
### Baseline and Evaluation Metrics  
We select four models as our baselines, CoType is a joint extraction model based on feature system. SPTree uses neural network model with abundant linguistic resources. Noveltagging and MultiDecoder both use neural network to jointly extract entities and relations without additional features. These models all achieved the best results at that time.  
* CoType [24]: a domain-independent framework by jointly embedding entity mentions, relation mentions, text features, and type labels into representations, which formulates extraction as a global embedding problem.
* SPTree [7]: an end-to-end relation extraction model that represents both word sequence and dependency tree structures using bidirectional sequential and tree-structured LSTM-RNNs.
* Noveltagging [8]: an approach that treats joint extraction as a sequential labeling problem using a tagging schema where each tag encodes entity mentions and relation types at the same time to achieve joint extraction of entities and relations.
* MultiDecoder [9]: a sequence-to-sequence learning framework with a copy mechanism for joint extraction, where multiple decoders are applied to generate triples to handle overlapping relations, completing the extraction of a relational triple every three steps. This method is the first time to solve the overlapping problem of multi-relational extraction.  
We compare our method with the above four baselines on NYT10 and NYT11 dataset respectively. In addition, we evaluate the performance of each model with micro Precision, Recall, and F1 score. Only when the relation and entity pair are all correct, we think this relational triplet is correctly predicted, where an entity is considered correct if the head and tail offsets are both correct. We used the source code provided by above baselines to reproduce their performance on NYT10 and NYT11 dataset, respectively.
# Research on Thermal and Heat Insulation Properties of Aerogel Heat-Insulating Reflective Coatings
## 2 Experimental Studies
### Test Method  
Referring to GB/T 13475-2008 and GB/T25261-2018 [19; 20], respectively, the test device for the two working conditions shown in Figure 3 was self-made, with working Condition 1 restoring the hot box method and working Condition 2 simulating the natural environmental heating test when the hot end environment is open. The device's walls  
Figure 1: Thermal insulation schematic: (**a**) insulation principle of barrier materials; (**b**) insulation principle of reflective materials; (**c**) insulation principle of aerogel [18].  
Figure 2: Schematic diagram of the fabrication of each specimen.  
were assembled from 65 mm thick XPS insulation panels, and the joints were sealed using construction insulation adhesive and a combination of insulating aluminum foil tape. In Condition 1, the test piece was placed in the middle of the chamber, and the section was divided into two parts. On one side, a 275 W infrared heating lamp was recognized as the heat source for the hot end and the other was the cold end. In Condition 2, one side of the chamber wall was replaced by the test piece to be tested. An infrared lamp was positioned 30 cm above the test piece to heat it. The air conditioning maintained a test environment temperature of (\(20\pm 1\)) \({}^{\circ}\)C. As shown in Figure 3, four T-type thermocouples were arranged on both sides of the test piece to collect the surface temperature of the test piece, while thermocouples were placed on each side of the test piece to manage the air temperature at the hot and cold ends, and for Condition 2 a thermocouple was arranged 2 m away from the device to collect the ambient room temperature. On the surface of the specimen, four thermocouples were located at right angles to a square with a side length of 270 mm, the center of which was the center of the specimen. Every 5 min, the DH3818Y collected the temperature with an accuracy of \(\pm 0.2\) \({}^{\circ}\)C. The temperature inside the testing chamber was only affected by the heat source because the insulation capacity of the tested sample was much lower than that of the walls. The temperature change between the two adjacent thermocouples during the heating and cooling phases should not exceed 0.2 \({}^{\circ}\)C to be considered a steady state. Based on the pretest results, the heating time for the test was set to 400 min.  
Figure 3: Diagram of the test setup: (**a**) diagram of Condition 1 test; (**b**) diagram of Condition 2 test; (**c**) physical view of Condition 1 test; (**d**) physical view of Condition 2 test.
# Inter-System Differencing between GPS and BDS for Medium-Baseline RTK Positioning
## 4 Experiments of Medium-Baseline RTK Positioning
### Positioning Results of the 75.9-km Baseline  
For the 75.9-km baseline, the RZTD parameter was estimated from epoch to epoch as a random-walk process. Figure 10 shows the positioning results with seven satellites. The top three panels represent the results with the classical differencing model, while the bottom three represent the results with the inter-system differencing model. We can see that, due to the longer baseline length and the introduction of the RZTD parameter, the positioning performance seems worse than that of the 22.4-km baseline, especially in the up direction. The RMSs of positioning errors in North/East/Up directions are 1.93 cm/1.33 cm/1.25 cm for the classical differencing model. While for the inter-system differencing model, they are 1.25 cm/0.96 cm/9.90 cm, which represents an improvement of 35.2%/27.6%/11.9% in the three directions. Particularly, during the period about 23:00-24:00, there are large fluctuations in the positioning errors with the classical differencing model.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**RMSs of Positioning Errors (cm)**} & \multicolumn{6}{c}{**Improvement (\%)**} \\ \cline{2-10}
**Number of Satellites** & \multicolumn{3}{c}{**Classical Differencing**} & \multicolumn{3}{c}{**Inter-System Differencing**} & & \\ \cline{2-10}  & **N** & **E** & **U** & **N** & **E** & **U** & **N** & **E** & **U** \\ \hline
7 & 1.07 & 1.18 & 6.96 & 0.81 & 0.95 & 4.59 & 23.9 & 19.6 & 34.1 \\
8 & 0.91 & 0.8
## 7 Discussion
### System and Technical Challenges
#### 7.1.2 Real-Time Processing Requirements  
Real-time processing is essential for DTs to support applications such as traffic management and incident detection, which require immediate responses to dynamic conditions. The ability to process large volumes of incoming data, identify patterns, and make rapid decisions is crucial for ensuring that the DT accurately reflects the state of the physical system throughout its lifecycle.  
During the operation phase, real-time processing enables dynamic adjustments to traffic signals, lane usage, and other operational parameters, thereby improving efficiency and safety. In the maintenance phase, real-time data processing supports predictive maintenance by identifying potential issues before they escalate. However, achieving low latency in data transfer and processing presents a significant challenge. For example, a DT designed for traffic congestion management must process data from thousands of sensors during peak hours, dynamically adjusting signal timings and lane usage in real time. This requires high-throughput data processing with minimal delays, placing significant demands on computational resources and network infrastructure.  
Edge computing has emerged as a potential solution to this challenge. By processing data locally at the source, edge computing reduces the need for continuous data transmission to central servers, thereby lowering latency and improving system responsiveness. However, this approach introduces its own set of challenges, including the need for robust hardware for local processing, ensuring data security in decentralized systems, and maintaining consistency between local and central data repositories [124].
# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
## 2 Materials and Methods
### Testing Methods  
The test aims to monitor the facade behavior and validate the integration of sensors, both regarding the InComEss architecture and the defined kit of conventional sensors. Therefore, the outcomes of previous phases were validated in a controlled lab environment by collecting data from integrated sensors for structural health monitoring (SHM) analysis. The defined set of SHM sensors integrated into a facade prototype was tested under controlled weather conditions following the curtain wall standard EN 13830. During the test, a defined tests sequence was conducted to stress the facade's behavior. The series included air infiltration and exfiltration (+600/\(-\)600 Pa), wind pressure and depressure (+1750/2000 Pa for serviceability and +2625/\(-\)3000 Pa for safety), and rain in static and dynamic regimes (+600 Pa static test and dynamic water penetration test with a fan pulsing every 3 s from 750 Pa to 250 Pa). The tests were conducted with a fan at 600 mm and with a water source simulating rain in correspondence of facade joints with a flow rate of 2 L/min-sqm and at 400 mm from the facade.  
Additionally, mechanical tests such as building movement (horizontal and vertical) and impacts at different times and forces were performed. The full test lasted three days and was performed following a determinate sequence of parameters, as reported in Appendix A. During each test, the facade was constantly monitored and checked in real-time while the sensor's data were collected, considering the test duration and parameters, to facilitate the analysis of the results. The full test sequence is reported in Appendix A (Table A1).