esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Nanofertilizers
### Copper and Copper Oxide Nanoparticles (CuO NPs)  
Copper is seen as the third most important metal due to its daily use, and it is important for most living creatures [85]. Cu is an important micronutrient required by plants and should be administered at very low doses. Bulk CuO has a long history in agriculture, especially as fungicide, and has contributed to environmental pollution due to its insolubility, thereby easily being eroded into water bodies. This property limits its usage but the development of nanotechnology offers new hope for the application of copper as the environmental risks are reduced due to existence of copper in 0, +1 and +2 oxidation states, which exhibit different physicochemical properties [34].  
In agriculture, copper oxide nanoparticles have found use in fertilizers, plant growth regulators, pesticides, herbicides and as additives for soil remediation [86]. A study on the accumulation of CuO NPs on lettuce and cabbage at concentration of up to 250 mg/L shows reduced water content and growth of the vegetables [86]. Since they are leafy vegetables, in addition to absorbing CuO NPs from the soil, they are also vulnerable to atmospheric pollution, as the nanoparticles are deposited on their leaves, which is the part ingested by humans. The dosage on the vegetables tends to increase, since the absorption is from both the leaves and soils. The absorbed quantities also depend on the type of plant, the soil and environmental factors, as it has been reported that 0.3 mg/L Cu\({}^{2+}\) released from 1000 mg/L of copper nanoparticles increases plant growth and is not toxic to the plant [87]. However, some reactions may lead to release of copper ions inside the plant cells that could be toxic. Toxicity of copper nanomaterials results from their solubility in the medium of application and redox processes arising from their interactions with other substances [3].  
CuO NPs tend to be more toxic than copper nanoparticles due to their oxidative nature, even at low concentrations, but positively impact on the photosynthetic process of the plant. CuO NPs have also been reported to exhibit antimicrobial activities, which affect the microbial reactions in the soil as some are resistant while others are not [3]. The resistance could be a result of release of Cu\({}^{2+}\) ions, which bind with the cellular membrane of microorganisms thereby causing damage.
# Moving Vehicle Tracking with a Moving Drone Based on Track Association
## 3 Multiple Target Tracking
### Two Point Initialization  
Two-point initialization has been applied to target tracking by a drone [25; 26; 27]. The initial state of each target is calculated by the two-point differencing following a maximum speed gating. The initial state vector and covariance matrix for target \(t\) are, respectively:  
\[\hat{\mathbf{x}}_{t}(k|k)=\left[\begin{array}{c}\hat{x}_{t}(k|k)\\ \hat{v}_{tx}(k|k)\\ \hat{y}_{t}(k|k)\\ \hat{v}_{ty}(k|k)\end{array}\right]=\left[\begin{array}{c}z_{tx}(k)\\ \frac{z_{tx}(k)-z_{tx}(k-1)}{\Delta}\\ \frac{z_{ty}(k)-z_{ty}(k-1)}{\Delta}\end{array}\right],\ P_{t}(k|k)=\left[ \begin{array}{c}r_{x}^{2}\frac{r_{x}^{2}}{\Delta}\ 0\ 0\\ \frac{r_{x}^{2}}{\Delta}\ \frac{2r_{x}^{2}}{\Delta^{2}}\ 0\ 0\\ 0\ 0\ r_{y}^{2}\ \frac{r_{y}^{2}}{\Delta}\ \frac{2r_{y}^{2}}{\Delta}\\ 0\ 0\ \frac{r_{y}^{2}}{\Delta}\ \frac{2r_{y}^{2}}{\Delta^{2}}\end{array}\right] \tag{9}\]  
The state is confirmed as the initial state of the track if the following speed gating is satisfied: \(\sqrt{\left[\hat{v}_{tx}(k|k)\right]^{2}+\left[\hat{v}_{ty}(k|k)\right]^{2}} \leq V_{max}\), where \(V_{max}\) is the maximum speed of the target.
# Guidelines for Telemetry Studies on Snow Leopards
## 5 Snow Leopard Captures Using Foot Snares
### Monitoring of Traps  
The cumulative risks of injuries and mortality increase with the time that the animal stays caught in the trap--for some species/individuals the risk may be constant per unit time whereas others may become more desperate to break free the longer they have been trapped [31]. This is perhaps even more critical when using traps such as snares that do not confine the animal in a small space [22]. There are several different types of trap surveillance solutions, utilizing radio, cell phone, or satellite communication. Cell phone (GSM) technology is relatively cheap and easy to use. Trap transmitters are programmed to send text messages to cell phones and/or email addresses with status updates and notifications if the trap is triggered. Cell phone systems are dependent on a reliable GSM network. In areas without GSM coverage, one can use satellite communication-based trap transmitters. For such a system to work it is dependent on internet access in the living quarters of the capture team. In areas that lack a GSM network or internet access, the traps can be equipped with VHF-transmitters. To ensure that animals are not trapped for longer periods than necessary, these transmitters should be monitored with custom-made systems that provide constant trap monitoring, e.g., [43]. Using such a system, the average time from capture to darting of 22 snow leopards was 28 min (range 13-48 min). If the signal from a transmitter indicates that a trap is triggered, the capture team should immediately mobilize as per a rehearsed and practiced tactical plan, with all necessary equipment at hand (which necessitates that all equipment and capture team members be at the ready at all times). The same protocols apply if the signal from the trap transmitter cannot be heard, as a captured animal may have displaced the transmitter or the antenna. A transmitter that cannot be monitored should be treated as a triggered trap and responded to appropriately.  
Even with trap monitoring systems in place, traps must also be visited and inspected as often as possible, preferably once every day to ensure they remain in working order, have not been disturbed, and that no animal has been caught without triggering the alarm.
## 3 System Model
### Model Building  
We establish an early arrival discrete-time queueing model for a single service desk according to the proposed strategy. The service desk is an abstraction of the channel, and the user packets are equivalent to the customers in queueing theory. The entire timeline is separated into time slots of the same length. The slot boundary points are \(t_{1}\), \(t_{2}\), \(\cdots\), \(t_{n}\), and at most one user packet can be transmitted in the channel in each time slot. In addition, the size of the cache that holds SU packets is \(k\).  
The probability that the system state changes from normal to failure due to the occurrence of failure is \(f\) (\(f=1-f,0<f<1\)), and \(f\) represents the communication failure rate. The probability that the system returns from the failure state to the normal state through the repair process is \(r\) (\(\bar{r}=1-r,0<r<1\)), and \(r\) represents the system repair rate. All user packets' arrival intervals and transmission time also conform to the geometric distribution. The arrival rate of PU1 packets is \(\alpha_{11}\) (\(\bar{\alpha}_{11}=1-\alpha_{11},0<\alpha_{11}<1\)), the arrival rate of PU2 packets is \(\alpha_{12}\) (\(\bar{\alpha}_{12}=1-\alpha_{12},0<\alpha_{12}<1\)), and the arrival rate of SU packets is \(\alpha_{2}\) (\(\bar{\alpha}_{2}=1-\alpha_{2},0<\alpha_{2}<1\)). When the system state is normal, the transmission rates of PU1 and PU2 packets are \(\beta_{11}\) (\(\bar{\beta}_{11}=1-\beta_{11},0<\beta_{11}<1\)) and \(\beta_{12}\) (\(\bar{\beta}_{12}=1-\beta_{12},0<\beta_{12}<1\)), and the transmission rate of SU packets is \(\beta_{2}\) (\(\bar{\beta}_{2}=1-\beta_{2},0<\beta_{2}<1\)). When the system is in a failure state, the transmission rates of PU1 and PU2 packets are \(\epsilon_{11}\) (\(\bar{\epsilon}_{11}=1-\epsilon_{11},0<\epsilon_{11}<\beta_{11}\)) and \(\epsilon_{12}\) (\(\bar{\epsilon}_{12}=1-\epsilon_{12},0<\epsilon_{12}<\beta_{12}\)).  
At time \(t^{+}\), the number of all packets is \(U_{t}\), in which there are \(V_{t}\) packets of PU1 and \(W_{t}\) packets of PU2, and the system state is \(O_{t}\). Then, \(\{(U_{t},V_{t},W_{t},O_{t}):t\geq 0\}\) composes a four-dimensional Markov chain (4 DMC). The system's state space is defined as \(\mathbf{E}\).  
\[\mathbf{E}=\{(0,0,0,s):s=0,1\}\cup\{(l,0,0,0)\cup(l,0,1,0)\cup(l,1,0,0)\cup(l,0,0, 1)\cup(l,0,1,1)\cup(l,1,0,1):l=1,2,\cdots,k+1\} \tag{1}\]  
The state \((l,m,n,s)\) in Equation (1) indicates a total of \(l\) user packets in the system at present, including \(m\) PU1 packets and \(n\) PU2 packets. In addition, the system state is determined by \(s\). If \(s\) is 1, the system state is failure; if \(s\) is 0, the system state is normal.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 3 Cookie Collector Methodology
### Phase 2: Rapid Prototyping Cycles
#### 3.2.1 Cycle 1 (Single-Browser Test)  
**Prototype:** Implementation of a bot for an initial browser (Chrome) with basic navigation. Connection of the bot with the cookie database to extract information. Make a copy of the cookie database to avoid blocking while the browser is open.  
**Testing:** cookie extraction success rate of 99%, simulating opening the browser, entering a URL, searching, waiting until the page is completely loaded, extracting cookies, and closing the browser.  
**Findings:** URLs with usernames that allowed student identification.  
**Improvements:** a URL anonymization system is required in the deployment phase to remove any personally identifiable information.
# Towards a Near-Real-Time Protocol Tunneling Detector Based on Machine Learning Techniques +
## 4 Protocol Tunneling Detector
### Feature Extraction  
As discussed in Section 4.1, sequential features allow us to understand if the content of a network packet is either compressed or encrypted. We rely on a statistical package developed by the Information Technology Laboratory at the National Institute of Standards and Technology, containing a set of 15 tests that measure the randomness of a binary sequence [34]. These tests have been designed to provide a first step towards the decision whether or not a generated binary sequence can be used in cryptographic applications, namely if the sequence appears to be randomly generated. In other words, each new bit of the sequence should be unpredictable. From a statistical point of view, each test verifies if the sequence being under analysis is random. This null hypothesis can be either rejected or accepted depending on the statistic value on the data exceeding or not a specific value--called critical value--that is typically far in the tails of a distribution of reference. Test reference distributions used in the NIST tests are the standard normal and the \(\chi^{2}\) distributions. Even if the statistical package contains 15 tests, we use only 5 of them, because the length \(N\) of the binary sequence we test does not meet the corresponding input size recommendation in [34]. To each sequence, we apply the following tests: frequency within a block, longest-run-of-ones in a block, serial test, approximate entropy, and cumulative sums. In addition, in our experimental evaluations, we extract some statistical properties and compute the Shannon entropy metrics [36] that, combined with the previously mentioned tests, have shown to improve the overall accuracy of the classification. As statistical properties, the following features are extracted from the corresponding hexadecimal representation \(h\) of a bit stream of \(N\) bytes:  
* number of different alphanumeric characters in \(h\) normalized over \(h\) length;
* number of different letters in \(h\) normalized over \(h\) length;
* longest consecutive sequence of the same character in \(h\) normalized over \(h\) length.
## 4 Discussion
### Strengths, Weaknesses, and Prospects of This Study  
This study leverages the advantages of multi-source big data to efficiently estimate the housing vacancy rate in Longquanyi District, Chengdu City. Compared to traditional methods, such as household surveys, this approach offers benefits including speed, efficiency, and relative accuracy. In the absence of a mature and comprehensive official survey mechanism, it provides a scientifically feasible method for assessing the housing vacancy rate in the region, while also serving as a supplement to other survey methods. However, due to the limitations of the spatial resolution of the data, it is difficult to further improve the estimation accuracy under current data conditions. Although higher precision night-time light satellite data, such as Luojia-1 and Jilin-1, which can achieve sub-meter spatial resolution, are available, their low timeliness and high cost make them unsuitable for rapidly changing markets. We hope to obtain higher quality satellite data in the future to further enhance estimation accuracy and look forward to the emergence of more targeted data to promote the development of housing vacancy rate surveys based on multi-source big data. Moving forward, the assessment of housing vacancy rates requires continuous innovation in both data and methodology:  
1. Explore broader multi-source data fusion: On the basis of existing data, further integrate demographic data, night-time takeaway orders, water, electricity and gas usage data, cell phone signaling data, etc., to build a more comprehensive assessment system that better reflects the real occupancy situation.
2. Develop smarter identification algorithms: Develop algorithms that can automatically identify mixed residential and commercial areas; for example, combining deep learning technology to extract features such as building form and surrounding environment to improve identification accuracy. At the same time, develop a more sophisticated identification model for vacant house lights to exclude the influence of other light interference sources, such as car lights and landscape lights.
3. Build a dynamic monitoring and evaluation system: The state of housing vacancy is not static, so it is necessary to establish a dynamic monitoring mechanism, such as regularly collecting night-time lighting data, updating the relevant databases, and making real-time adjustments to the model in conjunction with changes in the market, in order to more accurately reflect the dynamic trend of the housing vacancy rate.  
In conclusion, the assessment of housing vacancy rate is a complex and dynamically changing subject, which requires the comprehensive use of multi-source data, intelligent algorithms, and dynamic monitoring tools to continuously improve the accuracy and reliability of the assessment and to provide more powerful data support for urban planning and real estate market regulation.
## 3 Results
### The Effect of EO Concentration on the ZEA Level in Maize and Wheat Grain  
The reduction of ZEA concentrations in cereals treated with EOs in general depended on EO concentration (Figure 5). The greatest degree of ZEA content reduction by all tested EOs, both in maize and wheat grains, was observed at EO concentrations from 5% to 10%. The percentage of ZEA reduction was close to or exceeded 99%, depending on the EO. All tested EOs significantly decreased ZEA content at a concentration of 1%, however, cinnamon and verbena EOs were more effective, with ZEA reduction at levels of 96% to 100%, depending on the EO and grain type. The percentage values of ZEA reduction by palmarosa EO were 84.6% and 66.2% in maize and wheat grain, respectively. At the concentration of 0.1%, only cinnamon and verbena EOs significantly reduced ZEA in cereal samples, with the highest efficacy being cinnamon EO in maize (62.2% ZEA content reduction) and verbena EO in wheat (88.3% ZEA reduction). Based on the obtained result, it should be noticed that ZEA concentrations in samples treated with palmarosa EO at a concentration of 0.1% was higher compared to the control, however, only in wheat grain was the difference significant.  
Figure 4: Effects of EOs on ERG contamination in maize and wheat samples after inoculation with the _F. graminearum_. nd-not detected, below the limit of quantification (<0.01 \(\mathrm{\SIUnitSymbolMicro g}\)/g). Averages with different letters (a–d) for each cereal are significantly different at the \(p\) < 0.05.
# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
###### Abstract
They used a tsunami database of pre-calculated waveforms and inundation maps using many fault models. [PERSON] et al. (2014) [14] have reported that there is a close correlation between the average  
Figure 1: Topography map of DONET around the Nankai Trough area in Japan. Numerals are numbers of observatories; circles and squares indicate locations of observatories and science nodes. of the absolute pressures read by DONET and the calculated heights of the first tsunami waves in coastal areas. [PERSON] et al. (2016) [15] have prepared a tsunami database using many fault models set on the plate boundary and have selected tsunami scenarios based on multiple indices of observed and calculated waveforms. Their method selects common fault models based on all of the data in the ocean floor network system. [PERSON] et al. (2016) [16] have applied a Gaussian process to the tsunami database and aimed to improve the accuracy of predictions for relatively large M earthquakes. [PERSON] et al. (2012) [17] have developed a real-time Ground-Based Earth Observing Network (GEONET) analysis system for the rapid deformation monitoring (REGARD) application that determines a fault model using Global Navigation Satellite Systems (GNSS) data. [PERSON] et al. (2014) [18] have constructed a prediction system to calculate tsunami inundation in real time using a supercomputer and the REGARD application (vide supra). This method also determines a unique fault model for the prediction. [PERSON] et al. (2015) [19] have proposed a tsunami prediction method using a data assimilation technique for the ocean floor network system. [PERSON] et al. (2021) [20] have proposed a tsunami forecasting approach using convolutional neural networks through numerical experiments.
###### Abstract  
Device-to-Device (D2D) communication technology has become a key factor in wireless sensor networks to form autonomous communication links among sensor nodes. Many research results for D2D have been presented to resolve different technical issues of D2D. Nevertheless, the previous works have not resolved the shortage of data rate and limited coverage of wireless sensor networks. Due to bandwidth shortages and limited communication coverage, 3 rd Generation Partnership Project (3 GPP) has introduced a new Device-to-Device (D2D) communication technique underlying cellular networks, which can improve spectral efficiencies by enabling the direct communication of devices in proximity without passing through enhanced-NodeB (eNB). However, to enable D2D communication in a cellular network presents a challenge with regard to radio resource management since D2D links reuse the uplink radio resources of cellular users and it can cause interference to the receiving channels of D2D user equipment (DUE). In this paper, a hybrid mechanism is proposed that uses Fractional Frequency Reuse (FFR) and Almost Blank Sub-frame (ABS) schemes to handle inter-cell interference caused by cellular user equipments (CUEs) to D2D receivers (DUE-Rxs), reusing the same resources at the cell edge area. In our case, DUE-Rxs are considered as victim nodes and CUEs as aggressor nodes, since our primary target is to minimize inter-cell interference in order to increase the signal to interference and noise ratio (SINR) of the target DUE-Rx at the cell edge area. The numerical results show that the interference level of the target D2D receiver (DUE-Rx) decreases significantly compared to the conventional FFR at the cell edge. In addition, the system throughput of the proposed scheme can be increased up to 60% compared to the conventional FFR.  
wireless sensor network (WSN); device-to-device (D2D); fractional frequency reuse (FFR); almost blank sub-frame (ABS); long-term evolution (LTE); signal to interference plus noise ratio (SINR) 2017 17, 1088; doi:10.3390/s17051088
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 6 Applications: XAI's Impact across Diverse Domains
### Healthcare
#### 6.1.2 Example 2: Augmented Understanding of Breast Cancer Diagnoses  
Another compelling application of XAI is demonstrated by [PERSON] et al. [73], who deployed an XAI model to provide explanations for breast cancer diagnoses. This model not only aids medical practitioners in making more informed decisions but also empowers patients by offering them insights into the diagnostic process. By demystifying the AI-driven diagnosis, XAI fosters collaboration between healthcare providers and their patients, improving overall healthcare experiences.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 4 Experiments
### Log-Mel Spectrogram
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Log-Mel Spectrogram Parameters** & **Size** & **Accuracy** \\ \hline \multirow{3}{*}{Hop Length 1} & 300 & 0.3235 \(\pm\) 0.0019 \\  & **400** & **0.3288 \(\pm\) 0.0016** \\  & 500 & 0.3275 \(\pm\) 0.0008 \\ \hline \multirow{3}{*}{Number of Mel Filters 2} & 32 & 0.3425 \(\pm\) 0.0091 \\  & 64 & 0.3494 \(\pm\) 0.0085 \\  & **128** & **0.3571 \(\pm\) 0.0039** \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 5: The experimental results of the hop length and the number of Mel filters of the log-Mel spectrogram feature. For example, contrast the spectrograms of the 07497_4.wav airport scene and the 00095.wav beach scene. Intermittent high-frequency, high-intensity sounds can be seen in the high-frequency portion of the airport scene spectrogram (the darkest part at the top); medium-to-high-intensity sounds can be seen in the middle portion (the dark, large area in the middle of the spectrogram); there is obviously medium-low intensity sound in the middle and rear portions of the low-frequency portion (the orange-white part at the bottom). By manually listening to the 07497_4.wav file, we can clearly hear the airport broadcast beginning at the third second in the scene (corresponding to the orange part starting at the third second in the spectrogram), as well as the noisy human conversation in the scene (corresponding to the darker, high-intensity portion of the spectrogram). An obvious low-intensity (large areas of orange-yellow) sound in the middle and low frequencies (low-intensity sounds are mainly shown in the middle and lower parts of the spectrogram) can be seen in the spectrogram of the beach scene. By manually listening to the 00095.wav file, we can identify the long-lasting, low-intensity ocean waves that are audible in this scene. Additionally, the spectrogram makes it easy to see the audio noise (possibly wind sound) and the brief burst of bird calls in the first few seconds. Different areas in the spectrogram are represented by the frequency components, energy distribution, time-domain characteristics, background noise, and other information of the various sounds in the environment. It is crucial for subsequent models to achieve good classification performance to choose an appropriate audio feature extraction technique by choosing better hyperparameters.