esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion  
### Spectral Features  
The average R, A, and KM spectra of prepared samples with different adulteration levels (0-5 mL carrageenan solution) are displayed in Figure 2. Obviously, the spectral curves suggest a similar pattern, but slightly differed in values of intensity. In the visible spectral region (400-700 nm), curves showed a relatively low reflectance (i.e. high absorbance) in the green and blue regions (about 400-550 nm) and high reflectance in the yellow (about 550-600 nm) and red regions (about 600-700 nm). The above-mentioned regions could help to clarify the reason why adulterated chicken meat samples got their orange color. As can be seen in Figure 2a, there are four downwards peaks (absorbance bands) centered at 545 nm, 570 nm, 760 nm, and 980 nm. The bands at 545 nm and 570 nm can be attributed to the presence of deoxymyoglobin and oxymyoglobin, respectively [28]. These myoglobin contents are responsible for the meat's color. The wavelengths of 980 nm and 760 nm were closely related to the second and third overtone of the O-H stretching mode of water presence in muscles [29; 30]. However, no prominent peak of carrageenan can be observed among the different levels. Therefore, chemometrics would need to be further applied for mining hidden spectral differences to predict and visualize carrageenan adulteration levels in chicken meat. Similarly, the average spectral forms of A and KM spectra transformed from R spectra, respectively, are also shown in Figure 2b,c for comparison.
## 5 Conclusions and Future Work  
In the scenario of massive MIMO enabled HetNets, we propose an optimal iterative EE algorithm and joint optimization algorithm. From the simulation results and relevant discussion about the performance of proposed algorithms, we conclude the network EE is a concave function on the weight coefficient, the spectrum efficiency and the number of antennas installed on the MBS. That is, we sacrifice some part of the performance of spectrum efficiency with maximizing the network EE. Furthermore, we also propose the joint user association and power control algorithm for the development of new energy reduction policies in massive MIMO enabled HetNets and optimize the number of antennas installed on the MBS to increase the network EE. In addition, we analyze the validity and effectiveness of all the proposed algorithms. In future works, the joint number of antennas, user association, and power control and channel allocation optimization will be considered in the massive MIMO enabled HetNets. That is, to further increase the network EE, we will also optimize the number of antennas and channel allocation in future work, which is the main source of increasing the network EE.  
This work is supported by the National High Technology Research and Development of China 863 Program (2014 AA01A701); Beijing Natural Science Foundation (414142049) ; the Natural Science Foundation of China (Grant No. 51507063); and the Fundamental Research Funds for the Central Universities 2018 QN003.  
**Author Contributions:** [PERSON] and [PERSON] initiated and discussed the research problem; [PERSON] conceived and developed the methods; [PERSON] and [PERSON] performed the simulation and made the figures; [PERSON] and [PERSON] analyzed the data; and [PERSON] prepared and wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Analysis of the Most Relevant Factors for Routing in Internet of Space Things Networks
## 4 Discussion
### Analysis of Factor Values Providing the Best Performance
#### 4.2.2 Combined Factor of NRT and HMP (Information Freshness e13)  
The NRT and HMP parameters were fixed in the second experiment to analyze the impact of route information freshness on the routing protocol performance. Finding a stable route in dynamic scenarios such as those arising in ad hoc IoST networks is of paramount importance. Thus, detecting broken links on the fly becomes crucial. Results presented in Figure 6 consider a high value in NRT and HMP (low routing information freshness), a low value in NRT and HMP (high routing information freshness), and their intermediate combinations.  
In general, all combinations benefit from a higher value in node density (e5 = Density+) and lower NBH percentage (e4 = NBH\(-\)). Both routing approaches improve their performance for the high freshness scenario. The reactive protocol exceeds the performance of the proactive protocol in most cases. As observed in Figure 7, this is due to the increase in the number of packet collisions with the proactive approach, which negatively impacts its routing performance.  
For low routing information freshness, the best combination parameters involved many retries and a high percentage of NBH for both routing approaches. The proactive approach performed the worst when configured with this combination, with 66% of PDR, confirming that information freshness is essential for this routing approach.  
The best combination for the reactive routing approach was a large NRT and short HMP with an 84.08% in PDR. In contrast, this approach performed the worst in a configuration with a short NRT and large HMP, with 68% of PDR. This reinforces the observation made for low densities, that for the reactive approach it is preferable to try to send packets through older nodes rather than having no nodes in the routing table at all.  
In contrast with the reactive strategy, the best combination for the proactive approach was with short NRT and short HMP. This is because the proactive approach benefits from having fresher information in the routing tables.  
Figure 6: PDR Comparison between the best scenarios from the combination of values of the combined factor e13.
# Inter-Satellite Link Prediction with Supervised Learning: An Application in Polar Orbits
## 3 Polar Orbits
Since the inclination is the element that defines polar orbits, the whole range from 66\({}^{\circ}\) to 114\({}^{\circ}\) is used, and its distribution is presented in Figure 2. The right ascension of the ascending node, the argument of perigee, and the mean anomaly present no restriction associated with LEO polar satellites, and the entire range from 0\({}^{\circ}\) to 360\({}^{\circ}\) is considered. Based on these results, Table 1 summarizes the range of OEs and B-star drag terms that characterize around 99% of all active LEO polar satellites. The synthetic satellites used to construct the input dataset were created by combining random values within each range. Figure 3: Probability distribution of selected characteristics for all 2613 real polar satellites from Celestrak. (**a**) Distribution of B-star drag term, with over 98% falling within the range of \(-0.03\) to \(0.03\). (**b**) Distribution of semi-major axis, showing that more than 99% of satellites are within the range of 6700 km to 7500 km. (**c**) Distribution of eccentricity, where over 99% of satellites have values ranging from 0 (circular orbit) to 0.02. Figure 2: Inclination of all 6175 real active low-Earth orbit satellites in Celestrak. The red region contains polar satellites. Sun-synchronous orbiting satellites (in brown) are a specific type of polar satellite. Notice that these seven values are all the information that SGP4 requires to propagate an orbit. Therefore, as long as orbit maneuvers are reflected in these parameters, our solution can handle them without needing additional training. However, in extreme cases where a maneuver causes the satellite to move outside the range of orbital elements presented in Table 1, encounter prediction may fail.
## 3 Simulation and Results  
This section begins with the procedure followed to perform the simulation. Various simulation parameters are listed. Subsequently, for illustration purposes, a simplified example that considers only two APs and two SNs is demonstrated. Finally, the simulation results for multi-sensor realistic environments, namely area-A and area-B, are presented and discussed.
## 3 The Details of the Proposed LE-KCR Algorithm
### AUV-Based Data Collection  
Due to the short transmission distance of UWOC, AUVs with cruise function can be adopted to assist data transmission [35]. These AUVs are equipped with acoustic-optical hybrid systems, which can be switched between optical signals and acoustic systems to adapt to different communication ranges [44]. They establish communication links directly with sea buoys. The buoys obtain their own position information through GPS positioning, and calculate the position of AUVs according to the time difference of AUVs data arrival [45]. When an AUV approaches the network, it can quickly collect the data transmitted by the node and forward it to the corresponding sea buoy. On the other hand, AUVs can also be used as an anchor node for underwater node positioning [17]. Due to sea water turbulence and other reasons, the actual position of the node may deviate from the initial position. AUVs can also periodically update the actual site information of nodes.
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 3 High-Speed Train Noise Research Methods
### Real High-Speed Train Noise Tests  
Real high-speed train noise tests can obtain the most realistic noise distribution during the train-driving process. Real high-speed train noise tests are generally divided into internal and external noise acquisition. Internal noise experiments on high-speed trains generally use different locations in the car for microphone arrays for acoustic signal acquisition [43], but the internal noise data alone cannot be used to assess the overall noise level in service and its effect on the surrounding environment: it is now generally used in sound-insulation research on high-speed train bodies [44].  
Train exterior noise tests generally involve measurement of the noise distribution in space through single or multiple acoustic sensors and can also obtain the far-field field noise information of moving high-speed trains. The total sound pressure of the high-speed trains during the driving process and the contribution of different frequency bands of noise to the overall high-speed train interior and exterior noise can be measured using the acoustic signals collected by microphones at different measurement points. The arrangement of the acoustic sensors affects the collected noise data. As shown in Figure 1, the one-dimensional horizontal arrangement of the acoustic sensor array identifies the distribution of sound sources in the direction of motion of the train. The one-dimensional vertical arrangement can identify the sound source distribution at the height of the train [45]. The X-shaped sensor array identifies noise data in both horizontal and vertical directions relative to the high-speed train, and these data can be used to map the sound source distribution of the high-speed train [46]. By increasing the number of acoustic sensors, the microphone array is also arranged in spiral, star, or spherical forms.  
The number of microphones in the microphone array further increases with the increase in the speed of the train. [PERSON] et al. [47] used a star-shaped microphone array with 78 microphones to test the sound field of a high-speed train in a real-world experiment. [PERSON] et al. [48] employed a microphone array consisting of 78 microphones to measure the noise levels of high-speed trains traveling over viaducts and embankment sections, and [PERSON] et al. [49] used a microphone combination consisting of a 78-channel microphone array and microphones at a horizontal height of 3.5 m to study the sound field of high-speed trains travelling at a speed of 350 km/h. [PERSON] [50] used a microphone array with 144 channels to conduct sound field tests on a high-speed train running at 390 km/h. As shown in Figure 2, these train noise experiments collected real high-speed train noise data and used the data to study the noise distribution and variations therein.  
Figure 1: Microphone array arrangement form [45; 46].  
Figure 2: Noise distribution at different train speeds [48].
## 6 Methodology
### System Overview
#### 6.1.2 Software Components  
The software architecture is based on the principle of separation between the non-secure and secure environments. The separation is performed by STuRoT which further loads the SM firmware and the nonsecure application.  
The SM operates within a secure processing environment, consisting of the SM-Core and a range of secure services, including cryptography, initial attestation, trusted internal storage, and firmware updates. The SM-Core facilitates communication between non-secure applications and secure services through PSA-API, manages the scheduling of secure service execution, and, with the help of a memory protection unit, ensures the isolation of secure services from one another.  
The non-secure application consists of an ML user application that performs ML model inference while simultaneously handling attestation queries through the serial interface. The ML inference framework used in this system is TensorFlow Lite Micro (TFLM), which is specifically designed for mobile and embedded devices.  
There are multiple tools and packages available for converting ML models to formats suitable for embedded systems [38]. Some of these tools directly generate code, representing the model as an instruction list that can be executed as part of the application. Others generate the model as an array of bytes, which is interpreted at runtime by the application or an inference engine. As shown in Figure 5, the nonsecure application consists of multiple software components that are compiled together into a final binary. Similarly, a ML model can be integrated directly into the application binary during the build process or stored separately in external flash memory.  
TFLite [39] is a lightweight, portable framework optimized for running ML models efficiently on devices with limited computational resources, such as microcontrollers, smartphones, and IoT devices. It supports a wide range of platforms, making it an ideal  
Figure 4: STM32H573I-DK Discovery development platform.  
choice for embedded applications. TFLite's flexibility ensures that it can be integrated seamlessly into various embedded devices while maintaining high efficiency in terms of both processing speed and resource usage.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 3 Feature Extraction and Decision-Making Parameters
### Time-Frequency Domain Features Extraction Method
#### 3.1.2 Power Margin  
The expression of the jamming-to-signal ratio (JSR) is shown in Equation (6):  
\[JSR=\frac{P_{rj}}{P_{rs}} \tag{6}\]  
where \(P_{rs}\) is the input signal power reaching the receiver, and \(P_{rj}\) is the jamming power.  
JSR can be used to measure the relationship between jamming power and target communication signal power, so as to study the influence of jamming on communication signal. To simplify the calculation, according to [27], we assume that jamming effects as long as \(J/S\) reaches 0 dB.  
The \(JSR\) that meets the jamming condition is the effective \(JSR\) threshold of jamming. If the jamming power can reach this threshold, the power margin does not exist; otherwise, the power margin exists.
# Dynamical Manipulation of Surface Plasmon Polaritons
## 2 Directional Excitation of Surface Plasmon Polaritons (SPP)
### Wavelength Dependent Directional SPP Excitation  
Resembling the wavelength-division multiplexing in the 3D space, the wavelength demultiplexing of SPP is a feasible ingredient for on-chip optical communication. In Figure 1d, adopting two grooves with different widths, a submicron plasmonic dichroic splitter is demonstrated [79]. The SPP transmission and reflection features of the groove can be tuned by changing the depth and width. Based on numerical simulation, the two grooves are elaborately designed so that one groove mainly transmits the SPP and the other reflects SPP at a specific wavelength. For another wavelength, the functionalities of the two grooves are reversed. In the experiment, two grooves with widths of 260 nm and 360 nm and depths of 100 nm are fabricated. The ratio between the SPP propagating to the left and right is 3:1 at 640 nm and the ratio is reversed to 1:2 for 790 nm incident light. Based on mode conversion and mode interference, directional SPP excitation can also be achieved with a single compact antenna containing one groove and another auxiliary resonant groove [80], shown in Figure 1e. With normally incident light, the symmetric first waveguide modes (red) in both the main groove and lower nanogroove are excited. The first mode in the lower nanogroove propagates downward and is reflected by the bottom, which can activate the antisymmetric second mode (green) in the upper main groove. In this way, the auxiliary nanogroove enables the energy conversion as well as the interference between the two modes in the main groove. The amplitudes of the modes can be tuned by the height of lower nanogroove and the phase difference between the two modes is determined by the height of main groove. SPP generation by the compact antenna can be attributed to three factors: the normally incident beam, the first mode and the second mode in the main groove. The SPP propagating to the left and right are in phase for the first two factors, but are antiphase for the third factor. Thus, directional SPP coupler can be obtained by adjusting the SPP generated by three factors. In the experiment, the maximum intensity ratio between the rightward and leftward SPP is over \(10^{3}\) around the central wavelength of 800 nm. The directional SPP coupler works well with rather wide spectral range of 222 nm. That is because the grooves are shallow and the corresponding phase varies slowly for the different modes. It should be noted the third mode in the main groove can be activated at the short wavelength region around 609 nm, which can turn the propagation direction of SPP from right to the left.
## 4 Communication Architecture
### Modeling of Intra-Level Communication Architecture
#### 4.1.1 Level 1 Sensors  
The main task assigned to sensors at this level is the detection and subsequent collection of man-made big data and related activities. At this stage, the sensors are not involved in packet routing. Each data collected by a sensor is routed to its CH through a communication with one or more hops depending on the position of the sensor. The behavior of each sensor at this level is modeled according to three states as shown in the Figure 5.  
In state 1, the radio module of each sensor remains dyed, however, the sensors continuously monitor and control the man and his related activities (the acquisition unit of the sensors remains permanently active allowing the node to send alert messages, thus minimizing the delay, as well as periodically sending the collected measurements). The sensors switch to state 2 when their sleep period has expired or if an event occurs. In this state, each sensor monitors and then collects data, in order to transfer the data to the CH. It sends a message (a request to send data) to its CH and waits (according to a defined time delay) for a response (state 3). If the message is not received, it retransmits the message. At this stage, a selective retransmission mechanism (allowing the selection of the quantities to be retransmitted) must be considered taking into account the fixed number of retransmissions (according to the priority of the captured quantity). When the data transfer request is approved by the CH, the sensor transfers the collected data to its CH and returns to the sleep state (state 1), its radio is therefore switched off. This state 1 represents the stage of the energy conservation phase of the sensors (sleep during periods of inactivity).  
Figure 5: Diagram of activity sequences of first level architecture sensors.
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 5 Stream Classification Algorithms and Architectures
### Algorithms
#### 5.2.3 Neighborhood Based  
Neighborhood-based methods (see Figure 7c) are frequently used in the stream classification setting. As one of the most common approaches, the \(k\)-nearest Neighbor (\(k\)NN) algorithm predicts labels of new data records by selecting the class label of the closest training instance [121]. For \(k>1\), the class label is defined as the majority of all \(k\) nearest neighbors of the instance.  
Nearest neighbor classifiers can be naturally transformed to the incremental setting by selecting a limited subset of the most \"useful\" examples (also called reservoir sampling or lazy learning [1; 121]. The sampling process can be biased or unbiased [111]. In biased methods, the time component of data streams is considered. Thus, more recent data points are weighted higher than earlier data records, especially suited for streams with concept drifts. Although the algorithm is intuitive, determining the appropriate \(k\), especially when considering evolving data streams, is not trivial. The ANNCAD algorithm is one of the earliest \(k\)NN algorithms designed explicitly for data streams [139]. Here, an adaptive choice of the number of \(k\) nearest neighbors is possible.  
Similar to traditional nearest neighbor approaches, classification can also be conducted based on clusters. Original training data is represented as, e.g., the centroid of supervised microclusters. Thus, the data, as well as computational costs, are reduced [111]. A class consists of one or more micro-clusters, and predictions are derived by measuring the distance of newly arriving instances to the centroids of clusters. Classification of instances based on micro-clusters is also reported by [120] as an on-demand classification. Another common technique, aligned with the one above, is representing data points as density regions in a grid [140; 141]. The idea of grid-based clusters is that only counters of the grid regions need to be stored instead of complete observations. Ref. [142] propose an ensemble of k-means classifiers--called Semi-Supervised Adaptive Novel Class Detection and Classification (SAND). Each classifier is trained on a different batch of data. The data of each batch is partially labeled and can be determined dynamically. New labeled instances are gained using records with a high classification confidence score to handle concept drifts. Thus, the classifier is updated. Further, a dense area of outliers is interpreted as a new class. Within their study, the authors compared their algorithms with, e.g., Adaptive Hoeffding Trees in terms of its classification performance and ECSMiner, (ECSMiner is a novel class detection algorithm, proposed by [143].) for the novel class detection component. The performance of SAND in both tasks is at least comparable to the considered algorithms, with only a small number of labels.  
The challenges of neighborhood-based approaches are the reduction of processing time, the development of meaningful cluster splitting methods, and the curse of dimensionality [88].