esa-sceva/satcom-chunk-collection huggingface dataset:
# Private and Secure Distribution of Targeted Advertisements to Mobile Phones
## 5 Detailed System Description
### Requester Meets an Agent
#### 5.3.3 Transmission of ARMs  
After the Requester has determined which interests to request adverts for, he/she retrieves the corresponding ARMs. Recall that ARMs and the matching keys \(K_{i}^{user}\) have already been composed and stored in memory. The ARMs are labelled with a unique identifier that is called the ARM-ID, and they are ultimately sent to the Agent. Finally, the Requester places his/her copy of the ARMs and \(K_{i}^{user}\) alongside the ARM-IDs, the User-ID of the Agent and a timestamp on a separate queue of pending requests and waits for the requester to return with the requested adverts.
# SDN Architecture for 6 LoWPAN Wireless Sensor Networks
## 4 Implementation of SD6 WSN on the Contiki Platform
### Hardware Architecture
#### 4.2.3 6 LBR in a Full-Stack Contiki Note with Ethernet Interface  
The third option is suitable when CPU and memory capacity cards are available, such as motes based on SoC CC2538, to which an Ethernet adapter with Serial Peripheral Interface (SPI) interface is coupled. This allows the 6 LBR to be totally implemented in the Contiki mote. Because the SPI driver is directly connected to mote hardware, there is no need for a SLIP connection, as shown in Figure 12.  
This option is becoming more common with the launch of SoCs with greater memory capacity, such as the SoC CC2538 integrated with an Ethernet module that uses the Microchip ENC28j60 controller. Despite the advantages of being an integrated solution, the disadvantage lies in the limited memory space for adding new components. Performance is also affected by the poor power of processing due to the inherent limitations of this class of low power devices such as the low clock frequency, for example 32 MHz for SoC CC2538.
## 4 Results
#### 4.1.1 Antenna Architectures for Energy Harvesting

Table 11: Construction characteristics of antennas within the fractal geometry classification. Resonant frequencies, bandwidth at each frequency, substrate used, and antenna width are included. * Others In addition to primary antenna categories established in existing literature, a subset of designs features highly specific geometries or hybrid configurations that deviate from conventional classifications. To ensure a comprehensive and inclusive analytical framework, these atypical instances have been consolidated under the \"other\" category. While less frequently encountered, such designs often represent novel approaches or context-specific adaptations; consequently, their inclusion offers valuable insight into emerging trends and alternative strategies in energy harvesting antenna development, as summarized in Table 12.  
[PERSON] et al. [33] develop a compact dipole-type rectenna for wireless power transfer in the ISM spectrum. [PERSON] et al. [34] present technologies aimed at capturing ambient RF energy, specifically designed for autonomous wireless sensor systems. [PERSON] et al. [47] investigate a compact, high-efficiency rectenna for ultra-low-power RF energy harvesting. Moreover, [PERSON] et al. [56] develop compact metamaterial antennas, including dipoles and slots, focusing on medical applications and IoT devices. [PERSON] et al. [62] analyze hybrid rectennas employing printed dipoles on double-negative dielectric media to power sensors via ambient RF energy. [PERSON] et al. [69] design a dual-output quasi-Yagi antenna to capture RF energy outside the primary range. Additionally, [PERSON] et al. [93] investigate a compact, wide-spectrum textile antenna, suitable for near-field and far-field wireless power transmission. [PERSON] et al. [98] develop a scalable dual-band metasurface array, designed for electromagnetic energy harvesting and wireless power transmission. [PERSON] et al. [90] focus on developing two types of microstrip antennas specifically designed for electromagnetic energy harvesting systems. The first is a hybrid resonator planar structure antenna, optimized to operate at 900 MHz. The second is a multiband Archimedean spiral antenna, covering a broader frequency range from 1 to 3 GHz. Both antennas are distinguished by their design on a low-cost FR4 substrate and their ability to operate efficiently at standard frequencies like GSM, Wi-Fi, LTE, and UMTS, making them highly promising for ambient energy harvesting in various locations and at different times.
# Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
## 1 Introduction
However, there are several obvious problems with some current deep learning-based IoT intrusion detection methods. First, most of the current methods use previous data sources, i.e., NSL-KDD, KDD-99, etc., for evaluation [9; 10; 11; 12; 13], which do not contain current and up-to-date attack data against the IoT. Secondly, the detection models mentioned in a significant number of methods have a complex structure [11; 12; 13; 14], which leads to the limitation of the application of the models in the IoT environment. In addition, some methods ignore the fact that IoT traffic data has both spatial and temporal features [12], which leads to inadequate feature extraction by the models proposed in the methods and thus affects the detection performance. Moreover, some models cannot be processed for heterogeneous data in the IoT environment, which leads to a slight lack of model generalization [15], i.e., the models are not sufficiently adaptable to new data and have poor detection performance on new datasets. Finally, the limited amount of labeled data available for training deep learning models in the IoT environment seriously affects the detection performance of the classifier. In our work, we proposed an improved residual network structure, which consists of three residual modules, each containing a CONV-LSTM sub-network, and connects the module design to the attention module, which greatly reduces the complexity of the model structure. The evaluation results based on ToN_IoT with the UNSW-NB15 dataset show that our model has better performance performance than existing models. The contributions of our work are as follows:  
* Considering the spatiotemporal characteristics of IoT traffic data, we proposed an improved residual network structure that avoids the performance impact of extracting only a single feature. * We introduced an attention mechanism in the model to compute weights representing the importance of different features to help the model focus on the most important features. * Higher detection accuracy. The performance of the algorithm proposed in this paper, in terms of detection accuracy, is superior to some current state-of-the-art methods. * Stronger generalization ability. With a certain amount of data, this paper improves the model's expressiveness by increasing the network width and optimizing the loss function to reach the global optimum. The rest of this paper is organized as follows. Section 2 describes the intrusion detection-related work. Section 3 describes the mathematical modeling of the proposed model. Section 4 presents the experimental results and discussion. Finally, Section 5 summarizes the contributions and results of this study.
## 2 Results and Discussion
### Volatile Organic Compounds  
Aroma has a considerable influence on consumer acceptability [35]. VOCs contribute to the flavour and aroma of food products and are key factors determining perceived quality. Therefore, the profiling of secondary metabolites, such as VOCs, by analytical techniques is widely used in food quality assessment [36]. In this study, VOC composition was investigated both in BLP and in cooked pasta using solid-phase microextraction (SPME) with gas chromatography-mass spectrometry (GC-MS). In this study, only VOCs with identity of the highest threshold and that could be associated with odour descriptors [37] were included in the analysis (Table 3). In total, 66 aromaticative compounds were identified, including 21 aldehydes, 15 ketones, 14 alcohols, 2 sulphur compounds, 6 terpenes, 2 alkanes, 4 furan derivatives and 2 other compounds. BLP was characterized by a rich profile of VOCs (52 compounds), including 16 aldehydes, 14 ketones, 8 alcohols, 2 sulphur compounds, 5 terpenes, 2 alkanes and 4-(2-propenyl)-phenols. The most abundant VOCs in BLP were (_E,E_)-3,5-ocatadien-2-one, 1-penten-3-ol, 6-methyl-5-hepten-2-one and (_Z_)-2-penten-1-ol. Compared to the C pasta, the total abundance of VOCs in BLP was 8.5 times higher. Only 29 VOCs were detected in C pasta, with aldehydes being the most numerous group (12 compounds). The most abundant VOCs in C pasta were hexanal, 1-hexanol, limonene and 2-pentyl-furan. In pasta fortified with BLP, the presence of 59 compounds was detected, including 30 VOCs derived from BLP, all originating exclusively from C (14 VOCs),  
Figure 2: QDA sensory analysis and PCA biplot of experimental pasta fortified with BLP. C: control pasta; B2.5: pasta fortified with 2.5% broccoli leaf powder; B5: pasta fortified with 5% broccoli leaf powder; o: odour; c: colour; t: taste. whereas 15 compounds were present in both C and BLP. Consequently, the odour descriptions of cabbage, pungent, sulphur, rancid, malty and spicy could be assigned to BLP and fortified pasta but not to C pasta. The most abundant VOCs in B2.5 were hexanal, 1-hexanol, 1-octen-3-ol and limonene, whereas in B5, the same compounds were the most abundant but in a different order (hexanal > limonene > 1-hexanol > 1-octen-3-ol). Statistical comparison showed that the abundance of 19 compounds, including 8 aldehydes, 5 ketones, 4 terpenes and 2 furan derivatives, increased with the increased addition of BLP, which resulted in a significant increase in the total peak area in pasta B5 (Table 3). The odour descriptions of these compounds were mainly sulphuruous, green, rancid, fruity and floral.
# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 2 Mechanical Design of Cleaning Robot  
When the robot encounters large obstacles such as balconies and partitions, using the jumping strategy to overcome obstacles is most effective. The main idea of the jumping strategy is to realize 3D movement by relying on the 5-rope parallel configuration.  
To obtain continuous and flexible three-dimensional space motion on complex glass curtain walls, the proposed rope-driven parallel cleaning robot is mainly composed of a cleaning body with four propeller fans to provide wind pressure and five driving winches. The propeller fans in the four corners can form even and stable pressure, pushing the robot body to closely fit the surface, solving the problem of efficient cleaning for concave surfaces. In accordance with previous studies [29; 33], the rope-driven robots connected to more than \(n+1\) ropes in a space with \(n\) degrees of freedom are called redundant rope-driven parallel robots. In this configuration, the ropes in different directions are always subjected to a high tension in the workspace, which substantially reduces vibrations in the ropes. So, four winches are mounted on the facade, similar to a normal rope-driven robot, but one particular winch is mounted on the ground and provides a pulling force perpendicular to the facade, enabling the robot to leave the wall surface.  
As shown in Figure 1, the four winches on the facade are designed to maintain the movement stability on the two-dimensional plane of facade to prevent the robot body from shaking due to external factors and colliding with the wall or obstacles. The winch is the power unit that drives the cleaning body to move by controlling the winding/unwinding of the rope fastened to the body. A winch placed on the ground provides a third degree of freedom for robot movement, giving the robot the ability to \"jump\" on walls to easily cross obstacles. When the robot encounters an obstacle, a fifth rope lock is shortened; at the same time, with the change in the other four ropes' lengths, it can be temporarily removed from the wall to avoid collisions with obstacles. After the robot travels over the obstacle, the fifth rope is extended and works with the other four ropes again. At this time, the robot can return to the wall surface to continue its cleaning work.  
Therefore, through the above structural design, the robot can overcome large obstacles and realize stable spatial motion. This is crucial for the robot to efficiently complete global cleaning.
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
## 2 Materials and Methods
### Nanomaterial Preparation  
#### 2.2.1 Preparation of MoS\({}_{2}\) Nanosheets  
The preparation of ultrathin MoS\({}_{2}\) nanosheets was achieved by a one-step hydrothermal method following the described procedure [31]. Briefly, 2.28 g (66.3 g/L) of thiourea and 1.24 g (34.4 g/L) of hexaammoniumheptamolybdate tetrahydrate (NH\({}_{4}\))\({}_{6}\)Mo\({}_{7}\)O\({}_{24}\).4H\({}_{2}\)O were dissolved in 36 mL of deionized water to form a homogeneous solution after stirring for 30 min. Then a tightly sealed 50 mL Teflon-lined stainless steel autoclave was filled with the obtained solution and was heated at 220 \({}^{\circ}\)C for 24 h. The product was cooled down to room temperature (RT), black precipitates were then collected after centrifugation and washed with distilled water and absolute ethanol for several times. Finally, the obtained MoS\({}_{2}\) nanosheets were dried in vacuum at 60 \({}^{\circ}\)C for 24 h and characterized with Raman and EDX.
## 3 Results and Discussion
### Lead, Cobalt and Nickel Concentrations in Bottom Sediments
Figure 7: Nickel content in the “water-bottom sediments” system (Water surface layer in \(\upmu\)g/L, water bottom layer in \(\upmu\)g/L, bottom sediments in mg/kg, lake water depth in m) of Lake Markakol. The visualization shown in Figure 7 is a 3D model also presented in the Supplementary Material Figure S3. A certain level of anthropogenic load present in the tributaries can be traced in the distribution and accumulation of lead, cobalt and nickel in the aquatic ecosystem of Lake Markakol. At elevated concentrations, these metals are highly toxic. Their toxicity increases in combination with other metals, which may lead to a negative impact on the aquatic biota [8; 9]. Among the analyzed elements, nickel has the highest migration ability. The intensity of water migration is determined by the properties of the elements, i.e., the intensity of concentration and dispersion, which, on the one hand, depends on the degree of element prevalence in the lithosphere and participation in biochemical processes, and, on the other hand, anthropogenic inputs [74]. The anthropogenic load on Lake Markakol's ecosystem is heightened by the fact that the rocks of the surrounding study area are rich in nickel-cobalt ore deposits, as well as by the atmospheric transport from industrial enterprises [40; 42].
# A Review of Research on Signal Modulation Recognition Based on Deep Learning
## 6 Research Challenges and Future Directions  
### Further Research on DL-Based AMR Algorithms in Visible Light Communication Systems  
It can be seen from the above introduction that most of the existing deep learning-based modulation identification algorithms are proposed for wireless communication, and a few modulation identification algorithms are proposed for visible light communication. Visible light communication is a high-speed communication technology that does not require a licensed frequency band and will play an important role in 6G [116]. Therefore, studying the modulation and identification technology of visible light communication is also an important research direction in the future, with good prospects.
## 3 Different Equalization Algorithms
### Least Mean Square Algorithm  
The LMS is a very simple and robust algorithm that is broadly used in applications of digital filtering [29; 30] and signal processing. The simplicity of the algorithm is that the only value to be corrected is the step size '\(\mu\). The filter tap weights are adjusted during a reiterative process, and ultimately the filter minimizes the error signal between the equalizer output and the stored ideal signal [31]. An LMS algorithm can be realized as indicated in Figure 2 [32]. The required signal j(k) is trailed by modifying the filter coefficients c(k). The known ideal signal g(k) is then supplied to the finite impulse response filter (FIR). The difference between the desired signal and y(k)--which is the exit of the filter--is the error signal e(k). The erroneous signal e(k) is then inserted into the LMS equalizer to calculate the filter coefficients c(k + 1) in order to reiteratively reduce the mean square error J.  
\[J=E\Big{\{}e^{2}(k)\Big{\}}, \tag{1}\]  
and the error signal is given by  
\[j(k)-c^{T}(k)g(k) \tag{2}\]  
\[c(k)=[c1(k),\,c2(k-1)\dots,\,cL(c-\ L\ +1)^{T} \tag{3}\]  
Figure 1: Block diagram of a converged 5G mm-wave A-RoF multicarrier OFDM digital communication system consisting of an OFDM transmission chain, mm-wave generation and an OFDM reception chain.  
w(k) is the coefficient vector of filter length L or the number of taps, and  
\[\text{g(k)}=[\text{g(k)},\text{g(k-1)}\ldots,\text{ g(c- L +1)}^{\text{T}} \tag{4}\]  
is the adaptive filter data vector. The update equation for the LMS equalizer is given by  
\[\text{c(k +1)}=\text{ c(k)}+2\text{g(k)e(k)} \tag{5}\]  
where g(k)e(k) is the negative gradient, and \(\mu\) is the step size [32; 33]. The step size \(\mu\) is a very important variable, and it should be selected to lie in the following range:  
\[0<\mu>\frac{1}{\text{LP}_{\text{x}}} \tag{6}\]  
where, \(\text{P}_{\text{x}}\) is the input signal power. The convergence time of the LMS filter is the time that the filter converges; it is based on step size \(\mu\). If \(\mu\) is small, the filter converges very slowly; if \(\mu\) is very large, the algorithm would not converge.
## 2 Theoretical Background  
### EMD Theory  
EMD is a time-frequency analysis method and can self-adaptively process complex signals into a set of IMFs. It works with three assumptions [14]: (1) the target signal must have at least two extrema (one maximum and one minimum); (2) the characteristic time scale is defined by the time lapse between the extrema; and (3) if the data are totally devoid of extrema but contain only inflection points, then they can be differentiated one or more times to reveal the extrema. In addition, the obtained IMFs of EMD are nearly orthogonal functions and simple oscillation modes with physical significance. The procedure of EMD is shown as follows [14; 15; 16; 17; 18; 19].  
(1) Use the cubic spline line to produce upper and lower envelopes with all the local extrema points of the original signal (\(x(t)\)).  
(2) Define the mean of the obtained upper and lower envelopes as \(m_{1}\), and then calculate the difference between \(x(t)\) and \(m_{1}\) by  
\[h_{1}=x(t)-m_{1}. \tag{1}\]  
Here, there are two standards that should be used to judge whether \(h_{1}\) is a strict IMF [14]. If \(h_{1}\) satisfies the above two standards, then it is regarded as the first IMF component; otherwise, \(h_{1}\) is treated as \(x(t)\), and steps (1) and (2) are repeated \(n\) times until \(h_{1n}\) is a real IMF. As a result, the first IMF can be obtained by  
\[c_{1}=h_{1n}. \tag{2}\]  
(3) Then, we can obtain  
\[r_{1}=x(t)-c_{1}, \tag{3}\]  
where \(r_{1}\) is named the residue.  
Repeat above steps \(K\) times when \(r_{K}\) is monotonous or the program meets the stop condition. Then, we can obtain the total \(K\) IMFs and satisfy  
\[\begin{cases}r_{1}-c_{2}=r_{2}&;\\ \quad\vdots&\\ r_{K-1}-c_{K}=r_{K}&\end{cases}. \tag{4}\]  
Finally, \(x(t)\) is decomposed into \[x(t)=\sum_{k=1}^{K}c_{k}(t)+r_{K}(t). \tag{5}\]
## Appendix B Recovering Precise Orbit Corrections, Clock Offsets, and DCB  
The orbit and clock corrections in the PPP-B2b message were used to correct CNAV1 and LNAV for BDS-3 and GPS, respectively. With the orbit correction vector \(\partial O\) in the radial, along-, and cross-track directions provided by the PPP-B2b orbit correction message, the satellite position correction vector \(\Delta X\) can be calculated using Equation (A1):  
\[\left\{\begin{array}{l}e_{r}=\frac{r}{|r|}\\ e_{c}\ =\ \frac{r\times r}{|r\times r|}\\ e_{a}\ =e_{c}\ \times\ e_{r}\\ \Delta X=[e_{r}\ e_{a}\ e_{c}]\cdot\partial O\end{array}\right.\] (A1)  
where \(e_{r}\), \(e_{a}\), and \(e_{c}\) are the components of the direction unit vector in the radial, along-, and cross-track directions, respectively, and \(r\) and \(\hat{r}\) represent the satellite position and velocity vectors of the broadcast ephemeris, respectively.  
The precise satellite position \(X_{orb}\) can then be calculated using Equation (A2):  
\[X_{orb}=X_{brd}-\Delta X\] (A2)  
where \(X_{brd}\) is the satellite position calculated based on the broadcast ephemeris.  
It should be noted that \(X_{orb}\) calculated using Equation (A2) is the satellite position with the antenna phase centre (APC) as the reference point.  
The precise clock offset can be calculated using the clock correction  
Therefore, when the IFLC of pseudorange observations \(P_{1}\) and \(P_{2}\) are adopted in data processing, the satellite hardware code bias can be ignored because it is absorbed in the satellite offset corrections provided by the PPP-B2b message.  
For BDS-3, the satellite clock corrections given in PPP-B2b refer to the B3I signal, which means that the recovered satellite clock offset correction, \(\overline{d}t_{B2b,B3I}^{C}\) in Equation (A5), contains the actual satellite clock offset and the satellite hardware code bias at the reference frequency \(f_{B3I}\).  
\[\overline{d}t_{B2b,B3I}^{C}=dt^{C}-B_{s,B3I}^{C}\] (A5)  
In Equation (A5), \(dt^{C}\) is the actual satellite offset, and \(B_{s,B3I}^{C}\) is the satellite hardware-code bias for signal B3I.  
Therefore, the differences in the satellite code hardware bias between any other signal and the B3I signal must be corrected for dual-frequency data processing with the corresponding DCB parameters. The DCB in the PPP-B2b message is the difference in the satellite hardware code bias between the signal and the reference signal B3I. Thus far, the DCB parameters for BDS-3 at frequencies of \(f_{B1I}\), \(f_{B1C}\), \(f_{B2a}\), \(f_{B2b-I}\), and \(f_{B2b-Q}\) have been broadcast in the PPP-B2b message.