esa-sceva/satcom-chunk-collection huggingface dataset:
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 1 Introduction
### Limitations of Traditional Detection Methods
#### 1.2.1 Primary Prevention Strategies  
* Input validation and sanitization;
* Content Security Policy (CSP) implementation;
* Output encoding;
* HttpOnly and Secure cookie flags;
* X-XSS-Protection header;
* Framework security features [9].  
Although fundamental to web security, traditional detection methods for cross-site scripting (XSS) attacks face significant limitations in effectively identifying and preventing modern XSS vectors.
## 4 Discussion  
SIF contains information about the photosynthetic physiology of vegetation, and it has the capability to estimate photosynthesis and transpiration. However, previous studies have found that the relationship between SIF and Tr has significant variability, which is influenced by environmental factors and vegetation physiology. Current research utilizing satellite data encounters significant limitations due to constraints in temporal and spatial scales, primarily focusing on the comparative analysis of vegetation functional types. To  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Temporal** \\ **Scale** \\ \end{tabular} } & \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Chinese Cork Oak**} & \multicolumn{3}{c}{**Poplar**} & \multicolumn{3}{c}{**Arborvite**} \\ \cline{3-11}  & & **R\({}^{2}\)** & **RMSE** & **k\({}_{0}\)** & **R\({}^{2}\)** & **RMSE** & **k\({}_{0}\)** & **R\({}^{2}\)** & **RMSE** & **k\({}_{0}\)** \\ \hline \multirow{8}{*}{half-hour} & 1 & 0.26 & 0.1508 & 0.82 & 0.27 & 0.1661 & 0.84 & 0.25  
clarify the impact of environmental factors on the relationship between SIF and Tr in different plantations across various temporal scales, and to improve the reliability of SIF in estimating Tr, this study used tower-based fluorescence automatic observation systems to collect SIF data from the canopies of typical plantations such as Chinese cork oak, poplar, and arborvitae in northern China. We compared the dynamics of SIF and Tr in different plantations, examined the relationship between them, and analyzed the impact of key environmental factors on the SIF-Tr relationship.
# Assessment and Review of Heavy Metals Pollution in Sediments of the Mediterranean Sea
###### Abstract  
The impact of marine sediment pollution is crucial for the health of the seas, particularly in densely populated coastal areas worldwide. This study assesses the concentration and distribution of heavy metals in the marine sediments of the main regions of the Mediterranean Sea. The results underscore high concentrations of mercury (Hg), nickel (Ni), and copper (Cu), whereas chromium (Cr), zinc (Zn), cadmium (Cd), barium (Ba), and vanadium (V) exhibit moderate values. To assess the heavy metal results, sediment quality guidelines and pollution indices (Igeo and Geochemical Signal Type-GST) were employed, revealing a consistent trend of decreasing concentrations from the coastal zone to the open sea. Principal Component Analysis (PCA) emphasizes the significant roles of Cu, Zn, Ba, and Cr in sediment chemistry. The study suggests that the distribution patterns of heavy metals are linked to wastewater discharges in coastal areas, requiring effective management strategies to ensure the health of the Mediterranean Sea.  
heavy metals; marine sediments; Mediterranean Sea; pollution; wastewater; discharge
# Memory-Enhanced Knowledge Reasoning with Reinforcement Learning
## 5 Experimental Results and Analysis
### Ablation Experiment  
To verify the correctness and effectiveness of the added model, we conducted three sets of experiments using a controlled variable method: (1) removing ConvR embedding and using ConvE as the embedding model, (2) using LSTM as the memory module, and (3) removing the self-attention layer. We compared their MRRs with the entire model on the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**NELL-995**} & \multicolumn{4}{c}{**FB15K-237**} & \multicolumn{4}{c}{**KINSHIP**} \\ \cline{2-11}  & **@1** & **@3** & **@10** & **MRR** & **@1** & **@3** & **@10** & **MRR** & **@1** & **@3** & **@10** & **MRR** \\ \hline DeepPath & 52.6 & 61.5 & 77.3 & 63.8 & 16.9 & 24.8 & 35.7 & 22.7 & - & - & - & - \\ MINERVA & 66.3 & 77.3 & 83.1 & 72.5 & 21.7 & 32.9 & 45.6 & 29.3 & 60.5 & - & 92.4 & 72 \\ Multihop KG & 65.6 & 79.2 & 84.4 & 72.7 & 34.6 & 44.3 & 58.1 & 42.1 & 78.9 & 92.7 & 98.0 & 86.5 \\ TuckRL & 65.8 & 78.5 & 83.5 & 73.1 & 36.9 & 46.3 & 59.1 & 42.6 & - & - & - & - \\ RLH & 69.2 & - & 87.3 & 72.3 & - & - & - & - & - & - & - \\ Ours & 69.9 & 81.6 & 86.9 & 75.1 & 36.5 & 45.8 & 59.5 & 42.7 & 79.2 & 92.6 & 98.2 & 86.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental comparison results.  
development set, as shown in Table 3. In most datasets, deleting each component resulted in a significant performance decrease. Generally, removing action dropout had a greater impact, indicating that thorough exploration of the path space is crucial in the dataset.
## 3 Results and Discussion  
### Fiber Selection  
Three GC/MS methods using SPME fibers (HS-, DI- and HS-TWA-) are compared for simultaneous sampling and extraction of six TMH compounds from natural aqueous solutions. All sampling and extraction parameters for each of the methods were optimized using headspace vials. Further, results are compared to HS-TWA sampling from on-site natural waters. Commercial SPME fiber products available cover a wide range of polarities and volatilities for achieving optimal extraction efficiencies for a variety of compounds. Based upon the literature [19; 20; 21; 22; 23], the SPME fibers: 7 and 100 \(\upmu\)m PDMS; 75 and 85 \(\upmu\)m CAR/PDMS, 65 \(\upmu\)m PDMS/DVB; 65 \(\upmu\)m CW/DVB; and 50 \(\upmu\)m DVB/CAR/PDMS were investigated for the extraction of THMs. PDMS is a good choice as it has the advantage of being able to extract both non-polar and polar compounds by adjusting the extraction conditions [22]. PDMS/DVB polymers are suitable in analyzing more volatile compounds [24]. Figure 4 shows typical performance obtained for the fibers tested under HS-SPME sampling and extraction method towards six THM compounds. Compared with the other fibers, both the 50 \(\upmu\)m DVB/CAR/PDMS and the 65 \(\upmu\)m PDMS/DVB fiber provided better extraction efficiency for all six target THM standards. This work was carried out using available 65 \(\upmu\)m PDMS/DVB fibers for all analysis methods. Other work has suggested that the tri-phase fiber has shown promise toward THM analyses when considering compounds with greater potential range of polarities, such which might occur when analyzing disinfection by products (DBPs) found by halo-disinfection treatments [16].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Compound** & **LOQ (ug: L\({}^{-1}\))** & **DL (ug: L\({}^{-1}\))** & **Calibration Equation** & \(R^{2}\) \\ \hline \multicolumn{5}{c}{**HS-TWA Extraction b**} \\ \hline Chloroform & 15 & 4.4 & \(y=6.63\) + 4.82 & 0.988 \\ Bromodform & 15 & 4.6 & \(y=4.35\) + 4.64 & 0.989 \\ Bromodichloromethane & 16 & 4.7 & \(y=5.08\) + 4.4 & 0.987 \\ Dibromochloromethane & 16 & 4.8 & \(y=7.6x\) + 2.7 & 0.995 \\
1,2-dibromoethane & 17 & 5.1 & \(y=62.3x\) + 27848 & 0.996 \\
1,2-dichloroethane & 16 & 4.8 & \(y=14.15x\) + 4.78 & 0.985 \\ \hline \hline \end{tabular} DL = detection limit. Note: data averaged from triplicate analysis. DI -, HS- and TWA- analysis from aqueous sample THM standards using optimized conditions with salt added a and with no salt added b. Quantified Linear Range (3-800 \(\upmu\)g: L\({}^{-1}\)) used for all extraction methods.  
\end{table}
Table 2: _Cont._  
Figure 4: A plot of Gas Chromatography (GC) peak area _versus_ different tested SPME fibers towards the 6 targeted THMs. Optimized conditions: 10% wt./vol of Na\({}_{2}\)SO\({}_{4}\) in 20 mL water sample, extraction time: 8 min, extraction temperature: 25 \({}^{\circ}\)C, desorption time: 2 min, desorption temperature: 190 \({}^{\circ}\)C.
## 3 Results
### Analysis of Clusters
#### 3.1.3 Cluster Duration  
Next, we define a cluster duration as the time elapsed between the earliest start time and the latest end time of all outages in the cluster and find that the average cluster duration is 61.4 h. The average cluster duration by cluster size is shown in Figure 4.  
Figure 4: Average cluster duration by cluster size (2013–2019).  
Figure 3: Comparative breakdown of outages by initiating cause (2013–2019).  
Figure 4 confirms that there is no observable correlation between cluster size and duration (note that there are few data points for clusters of largest sizes). Further analysis reveals that sustained outages in clusters tend to be longer than the overall sustained outages (the average outage duration is 51 h versus 40 h). The longest outages in clusters are initiated by Environmental, Failed AC/DC Terminal Equipment, and Failed AC circuit equipment (the average durations are 435, 169, and 153 h, respectively). Overall, a cluster duration depends more on causes of the outages rather than on the cluster size.
## 4 Proposed Metrics
### Results and Discussion of the Proposed Metrics
#### 4.2.2 Network Layer Metrics
MPH checks the routes every determined time period, and this produces reliable routes throughout the operation of the network. On the other hand, we observe that ZTR shows some instability, because when a node loses connection momentarily, all of its child nodes are left without an ascending route. This means that these child nodes are left with no available route to the coordinator until a parent node reconnects to the network. For this reason, ZTR sometimes presents fewer available routes to the coordinator. From the above discussion, it is worth pointing out the important fact that MPH is a proactive protocol, where routes are rediscovered on a periodic time basis, while AODV and DSR are reactive protocols, where routes are discovered when they are required. MPH has the advantage of handling neighbor tables instead of routing tables and has a quick and simple hierarchical routing algorithm. We can observe that at 100 s, MPH shows 43.1% more discovered routes than AODV, and 27.7% more discovered routes  
Figure 13: Percentage of discovered routes time. than DSR and ZTR, respectively. This means that MPH has the capacity to maintain more routes than the other protocols.
# Molecular Techniques and Target Selection for the Identification of _Candida_ spp. in Oral Samples
## 3 Molecular Identification of _Candida_ spp.
### Restriction Fragment Length Polymorphism (RFLP)  
RFLP uses unique patterns in DNA fragments after enzyme digestion (using restriction enzymes), to genetically differentiate organisms. The distance between these cleavage sites differs between each organism--the resulting restriction fragments--which can be separated by gel electrophoresis arranged by size [82].  
RFLP can be used in combination with PCR. [PERSON] and co-workers amplified, by PCR, a region of the _ITS_ rRNA gene from 84 _Candida_ isolates, including 29 from oral samples. The PCR was designed to amplify intergenic spacer regions of the rDNA with established primers (Table S3) [55]. Isolates of _C. albicans_, _C. tropicalis_, _C. stellatoidea_, _C. parapsilosis_, and _C. krusei_ were identified following the restriction digestion of the PCR products.  
The PCR-RFLP protocol used by [PERSON] and colleagues (2003) was successfully applied for the identification of five _Candida_ species [83]. The choice of the specific and correct restriction enzyme is a pivotal point. Digestion with the restriction enzyme _Hae_II was effective to differentiate _C. albicans_ from non-_Candida albicans_, while _Bfa_I digestion was useful to distinguish _C. parapsilosis_ from _C. krusei_. The _Nla_III restriction enzyme was effective in differentiating the _C. parapsilosis_ complex [84]. The predicted fragment sizes for different enzymes with the respective species are depicted in Table S4. Other studies were able to discriminate _Candida_ species from clinical samples through RFLP, using the D1/D2 region of the 28S rDNA [84], the secondary alcohol dehydrogenase-encoding gene (_SADH_) [85], and the _ITS_ region. RFLP analysis is considered a useful, rapid, and trustworthy method [9; 86; 87].  
However, the additional steps of enzyme digestion add further complexity and time in comparison with assays that rely exclusively on PCR-based methods. The time necessary for PCR-RFLP assay can be similar to routine phenotypic conventional methods [87] but it is more sensitive. The storage (refrigeration) and use of restriction enzymes are considered expensive [88], adding to the resulting complex patterns which may be difficult to interpret.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## Materials and Methods
### Land-Cover Products  
The land-cover map, which comprised of 11 land-cover types, including forests, crops, bare lands and wetlands with a spatial resolution of 30 m and temporal resolution of one month in 2014 was adopted in this paper [46]. This map was generated from Chinese HJ-1 satellite images based on the time-series patterns of the land-cover types [47; 48]. Studies have shown that the overall classification accuracy of the map could be as high as 92.19% [49]. To make the datasets comparable, the 30 m land-cover data were aggregated to 1 km.
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 5 Conclusions  
In this study, an improved traffic sign detection and recognition algorithm is proposed for intelligent vehicles. Firstly, the HSV color space is used for spatial threshold segmentation, and traffic signs are effectively detected based on the shape features. Secondly, this model is considerably improved on the basis of the classical LeNet-5 CNN model by using Gabor kernel as the initial convolutional kernel, adding the BN processing after the pooling layer, selecting Adam method as the optimizer algorithm. Finally, the traffic sign classification and recognition experiments are conducted based on the GTSRB. The favorable prediction and accurate recognition of traffic signs are achieved through the continuous training and testing of the network model. The experimental results show that the accurate recognition rate of traffic signs reaches 99.75%, and the average processing time per frame is 5.4 ms. The proposed algorithm has more admirable accuracy, better real-time performance, stronger generalization ability and higher training efficiency than other algorithms. The accurate recognition rate and average processing time are significantly improved.  
From the viewpoint of traffic sign recognition accuracy and algorithm time-consuming, the proposed traffic sign detection and recognition algorithm has remarkable advantages. Considerably enhancing the driving safety of intelligent vehicles in the actual driving environments and effectively meeting the real-time target requirements of smart cars are conducive. Furthermore, a strong technical guarantee is provided for the steady development of intelligent vehicle driving assistance. In the future, the inclusiveness and anti-error recognition of the traffic sign recognition algorithm can be further optimized and improved to exploit the overall performance of the algorithm.  
[PERSON] designed the method, performed experiment and analyzed the results. [PERSON] provided overall guidance for the study. [PERSON] reviewed and revised the paper. [PERSON] offered crucial suggestions about the experiment. [PERSON] put forward the idea and debugged the model in Python.  
**Funding:** This research is supported by the International Cooperation Project of the Ministry of Science and Technology of China (Grant No. 2010 DFB83650) and Natural Science Foundation of Jilin Province (Grant No. 201501037 JC).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results and Discussion
### Determination of Antioxidants in Seeds
#### 3.2.1 Total Phenolic Compounds (TPC)  
The TPC ranged from 63.02 \(\pm\) 1.14 to 81.80 \(\pm\) 1.01 mg GAE/100 g of seeds, in green and yellow cultivars, respectively, compared to 76.04 \(\pm\) 1.59 mg GAE/100 g in the orange cultivar (Table 3). The highest value was recorded for the extract of the yellow cultivar, followed by the extracts of the orange and green cultivars. These results revealed a significant degree of variability in both yellow and orange cultivars as compared to the green cultivar. The statistical study revealed a variety effect at the threshold \(p\leq 0.05\). Our results are in consonance with those reported by other authors in seeds of the same plant species, as summarized by a recent review article [35]. However, other studies report higher TPC values, for example in the seeds of _O. ficus-indica_ growing in Egypt, where TPC = 504.825 mg GAE/100 g [36]. This difference can be attributed to the method of extraction and analysis, the geographical origin of the sample, degree of maturity, or storage conditions. Indeed, the seeds used in this study derived from fruits that were harvested in August 2020 and it is likely that, during their storage, the degradation of some compounds occurred.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample (Cultivar)**} & **Total Phenolic** & **Total Flavonoid** & **Tannin Content** \\  & **Compounds (TPC)** & **Content (TFC)** & **(mg CE/100 g)** \\  & **(mg GAE/100 g)** & **(mg QE/100 g)** & **5.60 \(\pm\) 0.07 a \\ \hline Orange & 76.04 \(\pm\) 1.59 b ** & 2.97 \(\pm\) 0.11 a  & 5.60 \(\pm\) 0.07 a  \\ \hline Yellow & 81.80 \(\pm\) 1.01 a  & 1.20 \(\pm\) 0.01 b  & 4.10 \(\pm\) 0.03 b  \\ \hline Green & 63.02 \(\pm\) 1.14 b  & 1.15 \(\pm\) 0.06 b  & 4.74 \(\pm\) 0.26 b  \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contents of total phenolic compounds, flavonoids, and tannins in the seeds of the three cultivars of prickly pear, namely orange, yellow, and green. Values in the same column followed by the same letter are not significantly different at \(p\leq 0.05\).
## Materials and Methods
### Determination of Cellular Viability  
We used the commercially available two-color assay based on plasma membrane integrity and esterase activity (Live/Dead Kit assay, Molecular Probes, Eugene, OR, USA) for qualitative analysis of the stem cells that were cultured on the bone graft material on Days 1 and 7. The media was removed and then suspended in 1 mL of Dulbecco's phosphate-buffered saline working solution (containing 2 \(\mu\)L of 2 mM ethidium homodimer-1 and 0.5 \(\mu\)L of 4 mM calcein acetoxy methyl ester) for 1 h at room temperature. We also performed a quantitative cellular viability test using an assay kit based on the water-soluble tetrazolium salt (Cell Counting Kit-8, Dojindo, Tokyo, Japan) on Days 1, 3, 5, and 7.
## 4 Performance Evaluation
### Effect on Faults
#### 4.1.2 Results Analysis
In the system without RAD, the controller traffic between switches and the SDN controller uses multiple paths, whereas the TCP connection packets between H1 and H5 use only a single path. Figure 4: Average packet loss rate (%) vs. the number of links down. Figure 5: Transfer Delay (ms) vs. time. Finally, we evaluate our system in terms of efficiency and throughput. As shown in Figure 8, our RAD system overcomes link down events, where the result with one link down even outperforms somewhat that without a link down. We determine that a link down event triggers to generate the new paths and performs load balancing on them. However, the system without RAD suffers a serious decline in performance in link down events.
## 1 Introduction
SSP can split multi-mode UGWs by separating the received signal into a sub-signal group in the frequency domain and recombining them in the time domain without the knowledge of dispersion and propagation distance [10; 21]. SSP was first applied to ground search radar operation [25] and later to conventional ultrasonic detection [26] to reduce the scattering effect of coarse crystals and improve the signal-to-noise ratio. It is rarely applied in the field of UGW testing, there are only a few studies on SSP processing of T(0,1) UGW signals, whereas the studies about the SSP processing of L(0,2) UGW signals cannot be found to the best of the authors' knowledge. [PERSON] et al. [10; 21; 27] applied T(0,1) UGW testing to a coated pipeline; SSP was used to eliminate the noise and improve the SNR, and determined the SSP filter parameters by brute force search method. However, the processing effect of SSP is directly related to the SNR of unprocessed UGW signals, namely high noise levels are harmful to the processing effect, and some defects may not be identified when SSP processes multi-defected UGW signals. In order to compensate the shortcomings of SSP, this paper proposed an improved SSP (ISSP) based on the raised cosine filter bank with varying bandwidth and spacing, and applied variational mode decomposition (VMD) and wavelet transform (WT) to UGW signals before ISSP. The structure of this paper is as follows: the characteristics of UGWs were analyzed (Section 2); signal processing methods used were introduced (Section 3); SSP was improved by filter bank design, and the pros and cons of ISSP were analyzed (Section 4); signal processing method based on VMD, WT, and ISSP (VWISSP) was proposed to process the UGW signals with noise and multi-defects (Section 5); L(0,2) UGW testing was performed on aluminum and low carbon steel pipes containing different types and numbers of defects, and the processing effect of VWISSP was evaluated (Section 6); and Section 7 is the conclusion.