esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture
### System Composition  
The nodes in the fixed 3D underwater wireless sensor network have certain computing and storage capabilities. Based on the underwater acoustic mode, they receive and send service data, and learn the status of the underwater transmission environment by monitoring the communication of the neighbor nodes. The monitoring data are collected from the sensor nodes, and forwarded by the communication nodes to the sink nodes. Therefore, each node constituting the edge network can be defined according to the agent, as shown in Figure 2.  
The agent in the edge network includes the communication module, edge prediction module, and intelligent data transmission module, and has an application requirements base, environment status base, transmission policy base, and service data base. The edge prediction module is only required for underwater sensor nodes and sink nodes. The application requirements base stores the requirements for data collection type, frequency, and accuracy of monitoring applications. The environment status base stores the state information of neighbor nodes, transmission channels, and the current node. The transmission policy base stores super-parameters, thresholds, and the V values [48]. The service data base stores the monitoring data, newly received prediction parameters, correction data, etc., and temporarily stores the sent data. The communication module is responsible for data transmission and receiving the packets. The edge prediction module can not only automatically match the optimal prediction parameters, but also generate monitoring prediction data based on the prediction model. The intelligent data transmission module is responsible for selecting the appropriate transmission route and data transmission accuracy according to the status of the environment. When the status update of the neighbor node is detected, the communication module is responsible for updating the content of the environment status base. When new data are received, the communication module receives the data;  
Figure 2: Agent model of underwater wireless sensor network.  
obtains the prediction parameters, correction values, and data source node information; and writes the new data into the service data base. When sending data, the intelligent data transmission module generates the best data transmission strategy and sends the data according to the selected route and data accuracy. When the communication module finds that the neighbor node has sent data, the intelligent data transmission module deletes the temporary data in the service database.
## 7 Risk Analysis
### Emergency Response Service
#### 7.3.5 Information Disclosure  
1. **Ports**  
_Security risk_: In the event of compromised communication between an autonomous vehicle and emergency services, the outcome can be ineffective, potentially culminating in accidents, harm, or even loss of life.  
_Security measure_: Enable the vehicle to communicate via multiple technologies, such as cellular networks, dedicated short-range communication (DSRC), and satellite links. This minimizes the likelihood of total communication breakdown.
## 3 Results  
### Concentration Ratio Estimation  
Initially, a calibration process was performed using a polyester reference phantom before measuring the fluorescent samples in the mice. A calibration curve was created and used for estimating the concentration ratios of the fluorescent tubes located subcutaneously in the hind leg and deeper in the abdomen. Table 1 summarizes the data of the eight fluorescent tubes used in the animal experiments, detailing the number of the fluorescent tube, its location, its true and estimated concentration and the estimation errors. Figure 1 presents the estimated concentration ratio versus the true concentration ratio. Each data point is the average estimated value of four to six measurements (performed on different excitation locations on the mouse skin surface) with its 90% confidence interval. The total number of measurements performed on the eight tubes was 41. The data points of the tubes implanted subcutaneously are represented in Figure 1 using the star symbols and the abdomen tubes are represented using the circle symbols. The dotted lines represent the true ratio values with 15% error value and the dotted lines represent the true ratio values with 35% error value. The mean absolute error of the estimated values presented in Figure 1, is 19.86%, with a standard deviation of 14.04%. When calculating the mean error (as opposed to the mean absolute error), which averages the over estimations and the under estimations, the mean error is 0.69% with a standard deviation of 25.45%. This result indicates that there wasn't a consistent tendency neither to overestimation nor to underestimation. Therefore, we assume that the reference phantom had more or less the same attenuation as the mouse tissue, although it may have had a flatter absorption spectrum than the mouse tissue. Actually, if the results did show a clear trend for overestimation the accuracy could have been improved using a correction factor. Unfortunately, this cannot be done in our case, but additional experiments, with different reference phantoms, should be performed for complete validation.  
Figure 1: The estimated concentration ratio as a function of the true concentration ratio. The solid line represents the true ratio values. The star symbols represent the average estimated ratios of the subcutaneous tubes with their 90% confidence interval. The circle symbols represent the average estimated ratios of the abdomen tubes with their 90% confidence interval. The dotted lines represent the true ratio values with 15% errors and the dash-dotted lines represent the true ratio values with 35% errors.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### IoT Classifications  
While there are several domains from where to extract measurements for building knowledge of a device's location, several limitations arise from the actual IoT system that is built upon. The goal of this subsection is to introduce the IoT technologies, by classifying them into six main categories (see Figure 4):  
* _Licensed versus unlicensed_: which refers to the operation in a protected band, such as cellular bands versus operation in unlicensed bands, such as industrial, scientific and medical (ISM) bands;
* _Operating frequency bands_: which refers to the carrier frequency of each IoT technology; here, we divide the frequency spectrum into three parts: sub-GHz, GHz, and mmWave bands. Some IoT technologies spread over multiple ranges;
* _Protocols versus enablers_: which refers to whether a technology is seen as a specific IoT communication protocol or a possible wireless positioning enabler;
* _Range-based classification_: which refers to short-, medium-, or long-range operation;  
Figure 3: Comparative analysis of TOA-based position estimates at various bandwidths.  
* _Rate-based classification_: which refers to Low-Rate (LR) or High-Rate (HR) data rates. Typically, most IoT connectivity solutions are meant for LR high delay applications, while solutions such as WiFi and 5G cover HR and low latency applications;
* _Power-based classification_: which refers to Low-Power (LP) versus High-Power (HP) operation. Typically, LP approaches go hand in hand with LR approaches, while HP approaches go hand in hand with HP approaches. In LP operation, the devices can function for several years on batteries.  
We make the following assumptions: in the range category, we consider long-range for those protocols capable of delivering more than 10 km links; for the rate category, we assume HR as those able to achieve an uplink above 10 Mbit/s and, for power consumption category, we assume as LP all those IoT systems who can support at least non-routing devices on more than two years of battery.
## 3 Materials and Methods
### Application of SHAP for Model Interpretation  
Upon establishing a robust machine learning model as the foundation for our analysis, we focused on integrating XAI techniques to enhance the interpretability of the model. Specifically, we utilized the Shapley Additive Explanations (SHAP) algorithm as a tool for assessing the model's global explainability. SHAP provides a comprehensive framework for interpreting the predictions made by machine learning models [11]. This method assigns a SHAP value to each feature, quantifying its relative impact on the model's decision-making process. These values facilitate both local and global explanations, offering insights into the influence of individual features on specific predictions and the model as a whole [53]. The mathematical formulation of SHAP values ensures that the contribution of each feature is accurately represented, thereby enabling a deeper understanding of the model's internal mechanics.  
\[\phi_{i}(f,x)=\sum_{f^{\prime}\subseteq x^{\prime}}\frac{|z^{\prime}|!(M-|z^{ \prime}|-1)!}{M!}[f_{x}(z^{\prime})-f_{x}(z^{\prime}\setminus i)] \tag{1}\]  
where \(x\) represents a certain sample requiring explanation, \(f\) denotes the model under consideration, \(i\) identifies the feature being assessed, and \(M\) signifies the total count of features. Additionally, \(x^{\prime}\) encompasses every conceivable variation or disturbance of \(x\).  
In this study, we leveraged the SHAP library [11] in Python to compute SHAP values for the features in our dataset. Central to the SHAP framework is the assignment of a SHAP value to each feature, quantifying its incremental impact on the model's prediction for a given instance. Originally adapted from cooperative game theory, where SHAP values ensure a fair allocation of benefits among players, these values are reinterpreted within the SHAP framework to distribute the output of the model among features based on their relative contribution [53]. This approach is particularly advantageous for complex models, where discerning the impact of individual features can be challenging.  
Our analysis focused on evaluating the relative importance of features within the dataset across the individual CMA quadrants. Given the unique emotional profiles of each quadrant, specific eye-tracking metrics likely influence distinct emotional states. By applying the SHAP algorithm to our model, we aimed to uncover which eye-tracking features are most influential in evoking specific emotions, thereby providing insights into how human emotions can be effectively identified from eye-tracking data. Our computation of SHAP values for each feature allowed us to identify key features that significantly affect the model's decision-making process in each quadrant. Notably, some of the features assessed either negatively impacted the model's decisions or had no discernible effect. This observation suggests the potential for developing a similarly effective model with a reduced set of features.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ALeZi & Active Lempel-Ziv \\ AP & Access Point \\ BE & Best Effort \\ BER & Bit Error Rate \\ BRS & Bandwidth Reallocation Scheme \\ BS & Base Station \\ C4R & Citymob4 Roadmaps \\ CAC & Call Admission Control \\ CAT & Call Arrival Time \\ CDP & Call Dropping Probability \\ CHT & Call Holding Time \\ CST & Cell Stay Time \\ DH & Historical Path Database \\ DRSVP & Dynamic resource ReServation Protocol \\ FSMC & Finite State Markov Chain \\ GIS & Geographic Information System \\ HDP & Hand-off Directions Probabilities \\ HMM & Hidden Markov Models \\ ISPN & Integrated Services Packet Network \\ KS & Kolmogorov Smirnov \\ MDP & Mobility Dependent Predictive \\ MIG & Mobility Independent Guaranteed \\ MIP & Mobility Independent Predictive \\ MRSVP & Mobile resource ReSerVation Protocol \\ NSIS & Next Steps in Signaling \\ NSLP & NSIS Signaling Layer Protocol \\ NTLP & NSIS Transport Layer Protocol \\ ODbL & Open Database License \\ OSMF & Open StreetMap Foundation \\ PD & Path Database \\ PoI & Point of Interest \\ PPM & Prediction Partial Matching \\ QoS & Quality of Service \\ RMM & Random Way Point Mobility Model \\ RSVP & ReSerVation Protocol \\ RTV & Real Time Video \\ SS & Switching Subnet \\ TRM & Trace Record Matrix \\ UMP & User Mobile Profile \\ \end{tabular}
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 5 Numerical Simulation
### Simulation Setup  
#### 5.1.1 Constellation Parameter  
We used open source software SAVI [15] to design three low-orbit satellite constellations with different numbers of satellites, and then obtained the snapshots of the three constellations at a certain time through ephemeris calculation, extracted the corresponding topological information according to the snapshots, which was used to calculate the shortest path between any satellites. Considering the batch and iterative update of satellite payload, the performance of MEC servers on each satellite is different. The average dominant frequency of MEC servers CPU is set to 1 GHz, and the standard deviation is 0.2. The energy coefficient of CPU \(\varepsilon\) is set to \(10^{-28}\). As the loads of each MEC server are also different at different time slots, the computation resources of MEC are assumed to be normally distributed with the mean of 2 GHz and standard deviation of 0.2. The parameters of the three low-orbit constellations are shown in Table 3.
## 3 Results and Discussion
### Experiment Setup  
#### 3.3.1 Bee Species  
Western honeybees (_Apis mellifera_) are the most common species of bees explored in the included studies. Not all of the studies mentioned the exact subspecies of _A. mellifera_ used in their investigation. Notwithstanding, Table 4 shows the bee species and subspecies studies in the respective articles. In the case of detecting arrival and departure of bees and measuring pollination efficacy, the genus Bombus was mostly present [84; 85; 88]. As shown in [99], buzzing vibrations exhibit species-specific characteristics at least in genus Bombus. Therefore, it is vital to report the species name under experiment in order to better understand the relationship between buzz production and species.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Bee Species** & **Articles** \\ \hline _Apis mellifera_ & [20; 28; 49; 56; 60; 61; 62; 67; 68; 69; 71; 75; 78; 79; 89] \\ _A. ceruna_ & [76] \\ _A. mellifera ligustica_ & [39; 41; 50; 52; 53; 54; 55; 56; 66; 94] \\ _A. ceruna japonica_ & [73; 74] \\ _A. mellifera carnica_ & [31; 41; 55; 57; 72] \\ _Augochloropsis brachycephala_ & [83] \\ _Amegilla murraypensis_ & [98] \\ _Bombus_ spp. & [82; 90; 91] \\ _B. impurities_ & [91] \\ _B. ardens_ & [80] \\ _B. latreille_ & [90] \\ _B. bulletatus_ & [88] \\ _B. terrestris_ & [81; 87; 99] \\ _B. puscuorum_ & [85; 86; 89] \\ _B. sylvicola_ & [84; 85; 88] \\ _B. morio and B. atratus_ & [83] \\ _B. humtilis_ and _B. hypnorum_ & [81; 86] \\ _B. argillacus, B. jonellus, B. ruderarius,_ & [81] \\ _B. sylurarum_ & _B. hortorum, B. lupidarius, B. pratorum, B. lucorum_ & [81; 99] \\ _B. nevadensis, B. frigidus, B. flavifrons, B. mixtus_ & [84] \\ _Centris tarsata_ and _Centris trigonoides_ & [83] \\ _Eulleman nigrita_ & [83] \\ _Exomalopsis annlis_ and _Exomalopsis minor_ & [83] \\ _Halictus_ & [90; 95] \\ _Megachile_ & [90; 95] \\ _Melipona bicolor_ and _Melipona quadrificaita_ & [83] \\ _Osmia brevis_ (oligolectic) & [93] \\ _Pseudaugochlora graminea_ & [83] \\ _Slovenian apis mellifera carnica_ & [50] \\ _T. nipponensis_ and _V. s. xanthoptera_ & [80] \\ _Xylocopa nigriccnicta_ and _Xylocopa suspecta_ & [83] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Bee Species.
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 3 Proposal Method and Discussion
### Optimization Respect to Subcarrier and Power Allocations
#### 3.1.1 Subcarrier Allocations
In this work, a fixed set of parameters \(\boldsymbol{\alpha}_{1,\text{RIS}},\boldsymbol{\alpha}_{2,\text{RIS}},\ldots, \boldsymbol{\alpha}_{K,\text{RIS}}\) is employed for more simplification. When the EE improvement achieved by the energy-efficient design compared to the spectral-efficient design is substantial, the actual transmit power used for the energy-efficient design should be significantly lower than the maximum transmit power, ensuring efficient power utilization. In the following, we delve into the SQP method employed to optimize power allocations.
## 3 Results and Discussion
### Monitoring of Epoxy Polymerization  
The extent of polymerization was determined at different stages of the curing process by near infrared spectroscopy. A representative series of spectra is shown in Figure 2a. The measurements performed on DGEBA samples containing various amounts of DAIS-B and cured with two passes of 50 kGy can be compared to a spectrum of the uncured resin of the same thickness (Figure 2b). Using Equation (1), the conversion levels of epoxy groups were as high as 85, 88, 89 and 95%, for 0.125, 0.25, 0.50 and 1.00 wt-% of iodonium salt, respectively. These results can be compared to the values measured from samples prepared with sulfonium TAS-PF\({}_{6}\) and TAS-SbF\({}_{6}\), with the same concentration range and same radiation treatment of an overall dose of 100 kGy (Figure 3). The two triaryl sulfonium salts appear much less efficient than DAIS-B. This is due to the decrease in reduction of potential properties of sulfonium (E\({}^{\circ}\) (Ar\({}_{3}\)S\({}^{+}\)/Ar\({}_{2}\)S + Ar\({}^{\bullet}\)) \(\simeq\) -1.2 V vs. SCE) compared to the iodonium cation (E\({}^{\circ}\) (Ar\({}_{2}\)I\({}^{+}\)/ArI + Ar\({}^{\bullet}\)) \(\simeq\) -0.8 V vs. SCE) [24; 25].  
Figure 2: NIR monitoring of the polymerization of epoxy groups: (**a**) variation of epoxy absorbance at 4535 cm\({}^{-1}\) upon 385 nm irradiation of DGEBA containing TAS-SbF\({}_{6}\) (1 wt-%) for 0 to 230 min with an irradiance of 150 mW cm\({}^{-2}\) and final post-curing for 30 min at 130 \({}^{\circ}\)C; (**b**) DGEBA with various amounts of DAIS-B treated in 1 mm thick Al molds under EB with two passes of 50 kGy.  
The strong influence of the nature of counter-anion, hexafluoroantimonate or hexafluoro-rophosphate is clearly evidenced from the comparison between the two salts with the same sulfonium cation [26]. This confirms the utmost importance of the reduced nucleophilic character which limits ion pair interactions and favors propagation. For all systems, a common trend is the favorable influence of the absorbed dose and the initiator content, which lead to an expected increase of active center concentration, with a direct and positive effect on conversion. Thermal effects resulting from polymerization exothermicity, about 80 kJ \(\cdot\) mol\({}^{-1}\) for epoxy monomers, are also considerable in the rapid polymerization of large quantities of bulk monomers [27; 28].
###### Abstract
SPE is more efficient and flexible than liquid-liquid extraction in terms of flexibility, simplicity, and low consumption of organic solvents. Still, the solid-liquid separation time is long after the target is adsorbed by the adsorbent. Therefore, an obvious trend in analytical chemistry is to shorten the separation time. To solve this problem, a simple and efficient magnetic dispersive solid phase extraction (Mag-dSPE) technique is developed. Mag-dSPE has the advantages of rapid solid-liquid separation, a simple method, and low cost, and it is suitable for complex matrices. The use of Mag-dSPE greatly reduces the consumption of organic solvents, which is an environmentally friendly technology [31; 32; 33; 34]. So, Mag-dSPE is also widely used in the pretreatment of food additives in recent years [35; 36; 37; 38; 39]. In 2020, a novel magnetic porous nanofiber was used as an adsorbent for Mag-dSPE and HPLC to detect Sudan dye in food, with a detection limit of 0.88-1.27 ug/L [40]. In this study, in order to improve the preferential adsorption ability of magnetic nanomaterials to weak polar or non-polar compounds, the materials were modified by C18. C18 modification is a commonly used functional method in reversed-phase chromatography. The working principle of reversed-phase chromatographic packing is to separate the components of a chemical mixture at an appropriate flow rate by making use of the difference in affinity between chemical substances with different chemical phases and fillers. Its separation is based on the difference between the hydrophilicity and hydrophobicity of compounds. Therefore, after the modification of C18, the specific adsorption ability of the material to non-polar compounds was increased. At present, C18 materials are mainly silicon-based materials. Compared with organic resin, silicon-based materials have no advantage in preparation or cost. In order to prepare a kind of material which can replace silicon-based C18 material, a novel magnetic nanomaterial with Fe\({}_{3}\)O\({}_{4}\) as the core, PS-DVB as the shell layer, and the surface modified with C18 (C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\)) was synthesized. C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) is used as a powerful sorbent in Mag-dSPE for the separation and preconcentration of 11 food additives. In addition, an analytical procedure combining the Mag-dSPE technique with HPLC has been proposed (Figure 1). The excellent sensitivity of the developed method for 11 food additives was investigated in laboratory batch tests, and it can be applied to the routine analyses for the determination of food additives in environmental water samples.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 3 Adaptive Sub-Network Organization
### Assessing Communication Channel Quality  
To assess the quality of the communication channels between the devices of our sensor network we use the Link Quality Indicators (LQI) provided by the communication layer. For each message exchanged, the quality of the received message is used to evaluate status of the link. Devices that are either too far away or with limited transmission capabilities (e.g., due to interference of malfunctioning hardware) tend to provide degraded metrics. The goal is to filter out those devices that have poor communication capabilities, and find alternative paths to deliver messages more efficiently and reliably. Using such indicators has proven to be a reliable solution when trying to define the exact neighborhood of a device [7]. Devices consider any broadcaster as a nearby device once a number of consecutive beacon messages are received with an LQI value above a certain threshold. A second LQI threshold is introduced to set a minimum channel quality acceptable. All beacons received whose LQI is below this threshold are simply dropped. This LQI threshold is made up of two different values as presented in Algorithm 2. The first part of the threshold is used to accept another device within the sub-network, while the second threshold is used for devices that are already members of the sub-network.  
Therefore, for each device participating in the sub-network, two LQI values are stored. The LQI value of the last received message, and the respective value received by the beacon of the device which is the LQI value of the last outgoing message as it was received from the device on the other end. These values can also cause a device to enter an inconsistent state. If a device receives a beacon where the reported LQI value is greatly different than the previous one, it considers itself inconsistent.  
```
1:if (node is in neighbor_list) {
2:if (lqi >= threshold_min) {
3: neighbor_list.update(node)
4: } else {
5: drop beacon
6: }
7: } else if (lqi >= threshold_max) {
8: neighbor_list.add(node)
9: }
```  
**Algorithm 2** Processing Beacons