esa-sceva/satcom-chunk-collection huggingface dataset:
# Portable L-Band Radiometer (PoLRa): Design and Characterization
## 2 Hardware
### RF Front End  
PoLRa is a direct-detection radiometer with three analog filter stages, including one before the first amplifier. The front-end filter is critical for preventing radio-frequency inference (RFI) signals from saturating the low noise amplifier (LNA) [21]. The radiometer uses two internal calibration noise sources as references, including a matched resistive source (RS) at ambient temperature, and an active cold source (ACS). A four-port low-loss RF switch switches between the two calibration sources and the two (vertical and horizontal) polarization antennas. Temperature sensors monitor the physical temperatures of the reference noise sources, as well as the antenna and cable. After multiple filter-amplifier stages, the RF signal is detected by a linear square-law power detector.  
The block diagram of the RF front end is shown in Figure 1. The filters are ceramic resonator filters, the two LNA stages provide a total gain of \(\sim\)70 dB. The RF components are currently connected with coaxial cable lines and SubMiniature version A (SMA)-type connectors. The RF components could alternatively be connected with microstrips or coplanar waveguides, which allows the implementation of the entire RF front end on a single printed circuit board (PCB). The measured response of a single bandpass filter is shown in Figure 2.  
The front-end loss, or noise figure (NF), is driven by the components before the first LNA, and determines the radiometer system noise temperature, and thus the radiometric resolution. Due to the required low-mass and -volume of PoLRa, the use of a large low-loss resonant cavity filter is impractical. The insertion loss of the four port RF switch, the isolator, and the ceramic cavity filter are 1.3 dB, 0.2 dB, and 2.1 dB, respectively. The NF of the first LNA is 0.6 dB, and there is an additional loss due to all connectors and SMA sections of \(\sim\)0.8 dB. The NF from the switch through, and including, the first LNA is 5.0 dB. The radiometer system noise temperature \(T_{\text{sys}}\) is calculated from the NF in dB using [22]:  
\[T_{\text{sys}}\ =\ T_{\text{ref}}(10^{\text{NF}/10}-1), \tag{1}\]  
where \(T_{\text{ref}}\) is 290 K. This corresponds to \(T_{\text{sys}}\) of 627 K.  
Figure 1: Block diagram of the L-band radiometer radio-frequency (RF) front end and post-detection electronics.  
Figure 2: Filter response measured with vector network analyzer (VNA): (**a**) broadband response; (**b**) frequency \(y\)-axis zoomed near the protected band.
## 4 Existing Jamming Attack Detection Methods and Their Analysis  
Several scholars have suggested different mechanisms to detect jamming attacks. All of these suggested mechanisms are to be implemented at the individual node level to crisply conclude whether the node is jammed or not. Their technique is either based on threshold values of some of the metrics, as discussed before, or to use digital signal processing techniques to differentiate between a legitimate signal and an illegitimate (jamming) signal and thus conclude about the presence or absence of the jammer. Few of the methods use comparison of the node conditions with those of its neighbors to fine-tune their findings. We discuss the existing solutions, as proposed by different scholars in the following sub-sections.
## 5 Implementation and Deployment
### Communication Protocol  
The TS must manage many environmental nodes, which requires communication between the nodes and the TS.  
The widely adopted Transmission Control Protocol/Internet Protocol (TCP/IP) protocol is suitable for complex network architectures and applications that require high reliability and real-time responsiveness. Consequently, this protocol is not applicable.  
As an Internet of Things (IoT) protocol, the Message Queuing Telemetry Transport (MQTT) protocol based on the publish-subscribe mechanism is well-suited for low-bandwidth networks and resource-constrained devices. It supports three Quality of Service (QoS) levels and offers significant advantages, such as flexibility, scalability, and high concurrency. The testing system referenced in the related work [12], and a communication protocol hybrid testbed [26] also utilizes MQTT in its protocol.  
It is worth noting that it is necessary to deploy a message broker for MQTT, which decouples the server and client in time and space. Thus, participants only need to focus on the publish-subscribe process to achieve a connection, making the platform's implementation scheme more flexible.  
Based on the analysis above, we adopted the MQTT protocol to ensure the communication and interaction between the nodes and the TS.  
Figure 6: Software’s modules and interfaces.
## 4 Experimental Evaluations and Results  
We experiment with a realistic use case, where DRL agents are used in a remote electrical antenna tilt environment, as shown in Figure 3. It is a multi-agent environment where each antenna is controlled individually. In this experiment, we trained a shared policy using DQN with reward decomposition (drDQN) where each agent obtains its observation, but uses the same policy as the others.  
Figure 3: The simulation map with seven base stations arranged in a hexagonal shape where each of them has three antennas. The antennas are illustrated as inverted L-shape lines in black where their directions are illustrated in various colors. The green dots illustrated the position of the UEs that are spread across the field.  
Figure 2: Illustration of BEERL dataflow in generating explanations in two different levels. The BEERL explanations are shown by green color while reward decomposition elements as introduced in [7] are shown by yellow; feature important components are shown by purple; common RL components are shown by grey.
## 2 Methodological and Operational Framework for CDTs
### Enablers for CDTs in Agile Supply Chains  
As introduced in the previous section a production entity (workstation, production line, Factory) or a supply chain is considered as a network of inter-connected DTs, each one having different capabilities. To realize this concept, each manufacturing context (factory, supply chain) can be modelled accordingly with different levels of cognition, communication and monitoring needs. Such configurability can be achieved by allowing the end user to configure and monitor the physical assets as CDTs and associate them with different capabilities/enablers presented in Figure 1.  
Most of the above enablers have already been introduced in the literature [12,18,19] and from different perspectives. Some of them are by default functions of a DT (profile, lifecycle, visualizations); others have already been introduced as well (communication, trustworthiness and computational). We propose the above approach which is in line with the focus on agile supply networks. We are also complementing the previous work, by emphasizing on the communication (among CDTs) and CDT governance aspects, an issue that has not been elaborated much and with regards the applicability in the supply chain.
## 7 Conclusions  
In this study, we extended the FP imaging technology from microscopic imaging to long-distance imaging with dramatically improved resolution. In addition, by introducing an imaging lens before the sample, we placed the Fourier transform plane at the imaging lens (and hence at the pupil function of the system). Therefore, scanning the camera + imaging lens had a clearly defined effect of shifting the PSF. This is in contrast to previous work, which affected the PSF less dramatically for each scan position [29; 30; 31]. First, we used the Fraunhofer diffraction theory to derive and verify the mathematical model of the proposed macroscopic FP method. The results of both simulations and experimental data showed that the proposed long-distance FP method can successfully recover HR images and improve the resolution. Second, to further validate the effectiveness of the proposed method, we tested it on two real captured datasets with scanning steps of 4.5 and 2 mm, respectively. When the image scanning step was 4.5 mm, the recovered HR image stripe width was able to be resolved up to 101 \(\upmu\)m; and when the scanning step was 2 mm, the HR image stripe width was able to be resolved up to 63.5 \(\upmu\)m. Finally, we further processed the images with partition reconstruction, which was even able to resolve Group 25 with 40 \(\upmu\)m line width with 12.5 lp/mm resolution and a resolution gain of 4\(\times\).  
Figure 14: Diagram of the relationship between the MTF of the lens and the frequency.  
**Author Contributions:** Conceptualization, M.Y. and H.Z.; methodology, M.Y.; software, M.Y.; validation, M.Y. and H.Z.; formal analysis, M.Y. and Y.W.; investigation, M.Y., X.F., Y.W. and H.Z.; resources, M.Y.; data curation, M.Y. and Y.W.; writing--original draft preparation, M.Y.; writing--review and editing, M.Y. and H.Z.; visualization, M.Y.; project administration, X.F. and H.Z.; funding acquisition, X.F. and H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Major Project on the High-Resolution Earth Observation System (GFZX04014307).  
**Acknowledgments:** The authors thank [PERSON], [PERSON], and [PERSON] for their help with the experiments and discussion.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
## 3 Results and Discussion
### Adsorption/Desorption Studies  
After adsorption of Ni\({}^{2+}\) or Cd\({}^{2+}\) at a concentration of 80 ppm, desorption of the metal from the bead was performed using 0.1 M HNO\({}_{3}\). Nitric acid at a concentration of 0.1 M has been used before for desorption of heavy metals from the SCGs with high desorption efficiency [47]. The effect of time in the percent of desorption was studied in the first cycle of desorption and it can be seen in Figure 15. Desorption of the adsorbed cadmium from the CA-SCGs beads reached 102.25% (s = 1.67%) and desorption of nickel reached 101.87% (s = 2.20%). The equilibrium of desorption from this adsorbent was achieved in one hour. Desorption from the CA beads reached 88.6% (s = 1.21%) for cadmium, and 87.4% (s = 1.86%) for nickel; the equilibrium was achieved in two hours. Figure 16 shows the results of percent of metal removal from the solution for each cycle and the percent of metal desorption from the bead for each cycle.  
Figure 16 shows a decrease in the metal adsorption by the CA beads and CA-SCGs beads in the second cycle. The larger decrease is seen in the CA-SCGs beads, but the adsorption percentages in the second and third cycle are similar, especially in the case of the CA-SCGs beads. This fact suggests that the decrease in the adsorption is due to the effect of the nitric acid in the CA-SCGs beads during the first time that the acid is added to the adsorbent, not degradation due a repeated addition of nitric acid.  
Figure 14: Beads at a dose of 0.025g of adsorbent in a solution with a concentration of 20 ppm of metal: (**a**) CA beads before adsorption, (**b**) CA–SCGs beads before adsorption, (**c**) CA–SCGs beads after adsorption of Ni\({}^{2+}\), (**d**) CA–SCGs beads after adsorption of Cd\({}^{2+}\), (**e**) CA beads after adsorption of Ni\({}^{2+}\) and (**f**) CA beads after adsorption of Cd\({}^{2+}\) (inside of the circle).  
Figure 15: Effect of time in the first cycle of desorption.
# Recent Advances in Geographic Information System for Earth Sciences
## 4 GIS for Hydrology  
In hydrological studies, GIS has facilitated the development of a dynamic model for analyzing runoff phenomena as well as a distributed parameter model that considers spatial variability in parameters related to the runoff process. The topography-based hydrological MODEL (TOPMODEL) is a distributed parameter model that uses a digital elevation model (DEM) in GIS. However, TOPMODEL is affected by the resolution of the DEM used. A reliable DEM grid-size resolution that exhibits low sensitivity to changes in input parameters during runoff simulations is investigated by [PERSON] et al. [9]. A case study in the Dongkok and Ieemokjung watersheds in South Korea shows that the efficiency of TOPMODEL rarely changes up to a DEM grid-size resolution of approximately 40 m, but changes more noticeably with coarser resolution. The findings of this study are important for understanding and quantifying the modeling behaviors of TOPMODEL under the influence of varying DEM resolution.  
Social media data collected through Twitter, Facebook, Flicker, and Weibo can be used to improve understanding of urban hydrology. [PERSON] et al. [10] examine rainstorm-related micro-blogging activities in response to rainstorms in an urban environment at fine spatial and temporal scales. The study collected hourly precipitation data and a total of 3.32 million Weibo blogs geotagged with Beijing, China from June to September 2017. The consistency between rainfall amount and human activities can be explained by the distribution of water ponding sites and major transportation hubs. The results show that human responses to the rainstorm event are consistent, though with certain time lags, in virtual and physical spaces at both grid and city scales.
# A Robust \(H_{\infty}\) Application for Motor-Link Control Systems of Industrial Manipulators
## 4 The Plant Dynamics Cancellation Issue  
At the basis of the \(H_{\infty}\) theory, there is the resolution of the optimization problem (1), according to one of the possible, well-known ways [22]. The adopted \"LMI approach\" [22] satisfies the imposed requirements by inserting the inverse of the plant in the controller function definition. As a matter of fact, when the closed loop function is computed, the plant dynamics are canceled. This action does not guarantee satisfying results in a case like the considered one, where the transmission zeros are the only way to represent the system vibration modes and, consequently, to control the triggered vibrations. Their omission would make the system blind in front of the excitation of their frequencies. In case of a direct link control, this problem would not appear, since the link vibrations would be captured by a gyroscope sensor, and the application of the \(H_{\infty}\) control through the standard LMI approach to solve the optimization problem would lead to good results. A possible solution to successfully design and implement the \(H_{\infty}\) controller is developed in the next section.  
Figure 11: Motor and Link velocity time responses—Black line: Motor Response; Red line: Link Response.  
Figure 12: Motor and Link velocity time responses with variation in the link inertia—Black line: Motor Response; Red line: Link Response.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 3 Experiments and Analysis
### Empirical Analysis of Computational Efficiency  
To comprehensively evaluate the computational complexity of the Pi-GRU model, we have made detailed records and comparisons of its inference latency, training computing power, and inference computing power on the Jetson TX2 hardware. Table 5 presents the specific performance data of the Pi-GRU model on Jetson TX2 hardware. It can be seen from the table that the Pi-GRU model performs well in terms of inference latency, which is only 0.21 ms, far below the upper limit of hardware capacity of 80 ms. This means that the Pi-GRU model can respond quickly in real-time application scenarios and meet the requirements of real-time trajectory correction. Furthermore, the training computing power and inference computing power of the Pi-GRU model are 0.29 GFLOPS and 0.093 GFLOPS, respectively, both of which are far lower than the maximum computing power of 1.3 TFLOPS of the Jetson TX2. This indicates that even on embedded devices with limited resources, the Pi-GRU model can efficiently complete training and inference tasks.
## 3 Methodology
### Preliminary Images Generation  
According to the analysis in Section 2, it is important to collect a set of cleaner images such that the denoised instances obtained from the DIP can have diverse reconstruction effects. To this end, we first adopt a pre-trained state-of-the-art image denoising method, FFDNet, to reconstruct the given corrupted image \(y\). FFDNet is one of the best DNN-based denoising models and has obtained remarkable denoising results for real-time noisy images. Clearly, reconstructed image \(p\) generated by FFDNet has a higher image quality than \(y\), and using \(p\) as the learning target of DIP can achieve a much better denoising  
Figure 4: The overall flowchart of the proposed method.  
Figure 3: Visual comparison of the denoised instances generated from two different DIP optimization processes. The quantitative PSNR results are listed behind the noisy image and two instances.  
effect. However, in the proposed method, we use only \(p\) to produce more diverse, cleaner instances. We develop an up- and down-sampling strategy to obtain diverse instances of \(p\). Specifically, we first employ the up-sampling method proposed by [24] to obtain a super-resolution version of \(p\), denoted by \(p_{\text{h}}\). The up-sampler can reshape the input image of size \(H\times W\times C\) into \(2H\times 2W\times C\). Where \(H\) and \(W\) are the image height and width, respectively, and \(C\) denotes the number of channels, that is, \(C=3\) for the color image and \(C=1\) for the grayscale image. Then, we adopted a randomly selected down-sampler to generate multiple down-sampled instances of \(p_{\text{h}}\). The details of using a randomly selected down-sampler \(G=(p_{\text{h}},n)\) to generate a set of down-sampled images from image \(p_{\text{h}}\) are as follows. First, image \(p_{\text{h}}\) is divided into \(\lfloor 2H/k\rfloor\times\lfloor 2W/k\rfloor\) cells with size \(k\times k\). Here \(k=2\). Subsequently, one location is randomly selected in the \((i,j)\)-th cell n times. The points corresponding to \(n\) locations are taken as the \((i,j)\)-th point of the down-sampler \(G=(p_{\text{h}},n)\). For all \(\lfloor 2H/k\rfloor\times\lfloor 2W/k\rfloor\) cells, the random location selection is repeated, \(n\) down-sampled images \(\{p_{1},p_{2},\ldots,p_{n-1},p_{n}\}\) of size \(\lfloor 2H/k\rfloor\times\lfloor 2W/k\rfloor\) are derived. We provide a visual example of generating instances with a randomly selected down-sampler in Figure 5. In Figure 5, image \(p_{\text{h}}\) contains \(6\times 6\) pixels and is divided into \(3\times 3\) cells. For each cell, one point was randomly selected to generate a down-sampled image. For example, we select point \"26\" in the (1,3)-th cell and point \"66\" in the (3,3)-th cell as the (1,3)-th pixel and the (3,3)-th pixel of down-sampled image 1, respectively. The randomly selected down-sampled process was repeated six times, and six down-sampled images were obtained.
## 3 E2E Latency and Distance Formulation
Finally, the E2E latency of the \(n\)-th eCPRI traffic stream that is carried by a fronthaul link between the RRH and the BBU pool is formulated using Equation (4):  
\[Del_{induct}=Del_{Encp}+[Del_{Que}+Del_{Proc}]\times Link_{n} \tag{2}\]  
\[Del_{det}=Del_{Prop}+Del_{Tan} \tag{3}\]  
\[E2E_{latency}=Del_{induct}+Del_{det} \tag{4}\]  
Along with the latency, the distance of the fronthaul links between the RRHs and the BBU pool is another important performance indicating parameter for the EFBNs [24; 25; 26]. The E2E latency limits the maximum distances in the EFBNs. For commercially deploying the cloud-RANs, improvement in the link distances between the RRHs and the BBU pool is essential. The distance (km) of a fronthaul link in the considered scenario is calculated using Equation (5):  
\[Distance=\frac{\left(Max_{threshold}~{}-~{}WE2E_{latency}\right)}{d/s} \tag{5}\]where \(Max_{threshold}\) is the maximum allowed E2E latency (i.e., 250 \(\upmu\)s) for the fronthaul link, \(WE2E_{latency}\) is the worst-case E2E latency experienced by a stream over the fronthaul link that can be computed from the Equation (4), and \(d\)/s is the propagation delay.
## 3 Experiments and Analysis
### Condition Adaptability Experiments  
The sensor was subjected to environmental adaptability testing to account for the high temperature and humidity conditions prevalent in underground environments. The test results are shown in Figure 7. As illustrated in Figure 7a,b, the experiment was conducted at 200 rpm. When the relative humidity is high, surface charges on the material dissipate with moisture loss. At higher temperatures, intensified thermal motion of electrons increases resistance. Consequently, the sensor's output voltage amplitude tends to decrease with increasing temperature and relative humidity. When the temperature increased from 0 to 160 degrees Celsius, the sensor's output voltage signal experienced a significant attenuation, decreasing from an initial value of 20.1 V to 13.3 V, representing an approximate 34% reduction. Similarly, when the relative humidity increased from 0% to 90%, the sensor's output voltage amplitude decreased from 19.8 V to 10.6 V, a reduction of approximately 46%.  
Figure 6: Power generation performance experimental results. (**a**) Output voltage of the sensor at different rotational speeds; (**b**) output current of the sensor at different rotational speeds; (**c**) output voltage and current of the sensor under different loads; and (**d**) output power of the sensor under different loads.  
The sensor outputs a pulse signal directly compatible with the pulse signal input port of a subsequent microprocessor. Both hardware ports adhere to the Transistor-Transistor Logic (TTL) level standard. TTL logic level recognition is based on the amplitude of the input pulse signal. An input pulse signal amplitude exceeding 2 V is considered valid (high level); otherwise, the input signal is deemed invalid. According to the TTL logic level standard, although the output voltage amplitude of the sensor shows a significant decrease across the temperature range of 0 to160 degrees Celsius and the relative humidity range of 0 to 90%, the attenuated amplitude still significantly surpasses the TTL signal recognition threshold. Therefore, the sensor remains operational under these conditions.  
Further testing assessed the sensor's long-term stability, with results shown in Figure 7c,d. Figure 7c displays the output voltage waveform under different cycle numbers, where each rotation for 10 s is considered one cycle, while Figure 7d presents a fitted curve of the data in Figure 7c. They are also the stability of the sensor. Figure 7c,d reveal that after 50,000 cycles of working the output voltage amplitude decreased from an initial 19.2 V to 17.4 V, a reduction of approximately 9%. However, the attenuated amplitude still exceeds the TTL logic level recognition threshold, demonstrating the sensor's long-term stability.