esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 3 System Architecture
### System Description  
The repeller system is an IoT device, which provides animal detection and repelling capabilities through the usage of a PIR sensor and an ultrasound generator respectively. The IoT device board uses an ATSAMD21G18A [54] 32-bit ARM Cortex(r) M0+ core architecture clocked at 48 MHz and paired with 32 kB of RAM and 256 kB of flash. It also integrates the RN2483A LoRa module supported by the LoRaWAN. All the pins of the micro-controller are exposed, allowing a developer to interface sensors and actuators over digital and analog interfaces. Moreover, the device is equipped with a PIR sensor to detect targets and activate animal repelling function. The power of the ultrasound produced by the tweeter is 110 dB at about \(\sim\)1 m, in a wide band of 18 kHz-27 kHz. Frequencies can be tuned according to the animal to repel. To transmit, process and store the information retrieved from the devices, we used a proxy software that collects the \"activities\", i.e., animal detected events, and transmits this information via LoRa first to the LoRa gateway, and then to the back-end system. Monitoring data about the status of IoT devices are periodically generated and transmitted via a LoRa gateway to the back-end cloud services. Figure 4 shows the sketch of the repeller board in (a) with the lid open and (b) in an enclosure. In order to conduct more LoRa transmission tests in the 433 MHz and 868 MHz frequencies band in respect to the [7], we provided a new contribution by introducing another prototype device that we designed and created for the experimental tests. The prototype is a packet generator integrated with LoRa module RN2483 for the LoRa transmission, which is the same LoRa module for the repeller device. Section 3.2 provides in detail the description of the new prototype device.  
Figure 3: System Architecture.
# Evolutionary Reinforcement Learning: A Systematic Review and Future Directions
## 6 Open Issues and Future Directions
### Future Directions
#### 6.2.3 Model Scalability  
To achieve scalability in EvoRL, a promising approach is the use of hierarchical models, which allow for the better management of complexity by dividing the problem into multiple levels. For example, in large-scale robotics tasks, a high-level model can focus on managing overall strategies and long-term goals, while low-level models are dedicated to handling more granular actions or immediate decisions. This hierarchical approach enables the EvoRL system to handle larger environments more efficiently, as the complexity is distributed across various levels. However, a significant challenge in this approach is the coordination between different levels, which can introduce inefficiencies in communication and decision-making. This could potentially slow down the evolution process and hinder scalability. A preliminary solution is to use message-passing algorithms [115]. These algorithms facilitate efficient communication between different levels of the hierarchy, ensuring that high-level and low-level models cooperate effectively. For example, the graph-based message-passing technique [116] can be used to allow the high-level model to send strategic updates to the low-level models and vice versa. Additionally, reinforcement learning-based routing protocols [117] could enable the models to adjust their interactions based on the evolving task, ensuring smoother coordination and improving the overall performance.
## 3 Results
### Comparing the GPPs Estimated by Different Resolutions of Remote Sensing Data  
The GPPs estimated by NIRv derived from MODIS, Landsat-8 and Sentinel-2 were shown in Table 2. The total GPP in Suzhou in 2021 estimated by MODIS, Landsat-8 and Sentinel-2 was 8.43, 8.48, and 9.30 Tg C yr\({}^{-1}\) (Tg = 10\({}^{12}\) g), respectively. Our estimations of GPP were higher than the results of MOD17A2 (4.37 Tg C yr\({}^{-1}\)), which ignores the contribution of the urban forest in built areas (black areas in Figure 4a). In contrast, our results included this ignoring component and fell within the range of previous studies which considered the contribution of urban forest (Table 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product** & **Resolution** & **Time** & **Model** &
\begin{tabular}{c} **Total GPP** \\ **(Tg C)** \\ \end{tabular} & **Remark** \\ \hline This Study & 10 m & 2021 & & 9.30 & Sentinel-2 \\  & 30 m & 2021 & NIRv-GPP & 8.48 & Landsat8 \\  & 500 m & 2021 & & 8.43 & MODIS \\ MOD17A2 & 500 m & 2021 & LUE & 4.37 & \\ Zhang [50] & 0.050\({}^{\circ}\) & 2016 & VPM & 9.37 & \\ Ju [51] & 0.073\({}^{\circ}\) & 2019 & BEPS & 6.72 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of urban contribution to GPP in different GPP products.  
Figure 3: Monthly averages (red, blue and green cycle) and standard deviations (red, blue and green bar) of NIRv in Suzhou derived from MODIS, Landsat-8 and Sentinel-2.  
The spatial patterns of GPP in Suzhou estimated by MODIS, Landsat-8 and Sentinel-2 in 2021 were shown in Figure 4b-d. All three kinds of GPP showed the same high GPP region located around Tai Lake (grey area in the southwest of Figure 4). In many areas of this region, the GPP ranged from 2500 to 3000 g C m\({}^{-2}\) yr\({}^{-1}\). The estimated GPPs had some differences in the northeast, along the Yangtze River (grey area in the northeast of Figure 4). In this region, the GPP of MODIS was lower than the GPP of Landsat-8 and Sentinel-2. According to our MODIS GPP results, most built areas were capable of photosynthesis from the vegetation within them. Additionally, GPP in built area of Suzhou ranged from 250 to 500 g C m\({}^{-2}\) yr\({}^{-1}\). Ignoring the contribution of GPP from the built area (black area in Figure 4a) induced the lower GPP in MOD17A2. Furthermore, high-resolution results from Landsat-8 and Sentinel-2 also indicated the contribution of urban forests, which were scattered over the built area due to their spatial distribution being highly discrete in Suzhou (Figure 4c,d).  
Figure 4: The spatial distribution of GPP from MOD17A2 product (**a**) and estimated by MODIS (**b**), Landsat-8 (**c**), Sentinel-2 (**d**). Areas in panel (**a**) with no data are due to the MODIS product not calculating GPP if the land cover type is built area. Additionally, the no vegetation (veg.) areas in panels (**b–d**) are the regions where the NDVI is less than 0.2 and, as a result, is recognized as bare land with no GPP.
# Development of a Spatial Heterodyne Terahertz Raman Spectrometer with Echelle Gratings
## 3 Experiment
### Calibration  
First, a standard mercury lamp was used to perform spectral calibration and thus obtain the spectral resolution and the spectral detection range of the system. Figure 2a presents the obtained interferogram of mercury lamp, and Figure 2b presents the positions of the characteristic wavelengths of mercury lamp in two-dimensional spatial frequency. Since the characteristic wavelengths of 576.961 nm and 579.067 nm are close to each other, and on the same level (m = 14), these two wavelengths are taken as known wavelengths. Figure 2c presents the fringe diagram corresponding to the characteristic wavelengths of mercury lamp at the spatial frequency after Fourier transform, from which the fringe numbers corresponding to the characteristic wavelengths can be found. The resolution of the system can be calculated according to Equation (13) as being equal to 1.37 cm\({}^{-1}\), and the spectral detection range of a single level is 701.61 cm\({}^{-1}\). Nine diffraction levels are used in this work, and the total spectral detection range reaches 6314 cm\({}^{-1}\).
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## 7 Discussions and Conclusions
### Considered Assumptions  
Lastly, our study was based on a set of simplifying assumptions, including negligible turbulence effect, and perfect channel estimation and time synchronization. If these assumptions are not met in practice, the effect will be more or less the same for the three considered signaling schemes. In particular, the turbulence-induced channel fading and time synchronization errors will impact the link performance in the same way for the three schemes. Nevertheless, the impact of channel estimation errors can be more important for the case of LACO-OFDM due to the successive detection used at the Rx and the risk of error propagation between layers due to imperfect channel state information, which needs further investigation.  
Our on-going work concerns practical implementation of the considered transmission schemes using a dedicated laboratory test-bed to assess their feasibility for use in a real system.  
Methodology, software, investigation T.E.; methodology, validation, formal analysis, supervision, funding acquisition M.A.K.; validation, formal analysis, supervision S.H. and H.A. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the French PACA (Provence, Alpes, Cote d'Azur) Regional Council and the Ecole Centrale Marseille.  
The authors would like to acknowledge the support from the French Embassy in Canada under the France Canada Research Funds program. They are also grateful to [PERSON] from SubseaTech Co., Marseille, [PERSON] and [PERSON] from IFREMER, La-Seyne-sur-Mer, [PERSON] and [PERSON] from Fresnel Institute, Marseille, France, and [PERSON] from McMaster University, Hamilton, Canada, for the fruitful discussions.  
The authors declare no conflict of interest.
## 7 Conclusions  
In this paper, the ambiguity resolution method of triple-frequency medium baseline RTK are presented in detail, including the geometry-free EWL AR and the fast geometry-based WL AR. The DD troposphere delay and ionosphere delay residuals are presented and their impact on AR is analyzed. Furthermore, based on six medium baselines, the RTK positioning performance using dual-frequency data, triple-frequency-only data, and both dual-frequency and triple-frequency data is evaluated in terms of the number of satellites and PDOP, positioning accuracy, fixing rate, convergence performance, and computation cost time. The experimental analysis shows the following:  
* For medium baselines of 45-66 km at latitude 30\({}^{\circ}\), the RMSE of DD slant troposphere delay is about 6.2 cm, and the RMSE of DD slant ionosphere delay is about 10.7 cm. They can be neglected for geometry-based WL ambiguity resolution, but cannot be neglected when fixing the raw ambiguity;
* In the Yangtze River Delta region of China, when performing BDS/Galileo/GPS triple-system positioning, 90% of the time includes more than 10 available satellites with 3 frequencies (BDS B11/B2a/B3I, GPS L1/L2/L5, Galileo E1/E5a/E5b). The average  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Baseline** & **Scheme 2** & **Scheme 3** \\ \hline HZDQ-HZLA & 36.00 s & 42.85 s \\ HZDQ-HZXS & 32.74 s & 40.91 s \\ HZXS-HZLA & 34.14 s & 42.77 s \\ WHJA-ZEEC & 32.07 s & 40.61 s \\ E2 EC-WHJX & 30.33 s & 39.17 s \\ WHJX-WHJA & 32.23 s & 40.79 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: The average duration of the 48 sessions for the 6 baselines.  
PDOP value of triple-frequency-only case during the entire time period is less than 2.0, indicating a good geometric configuration;
* Compared to dual-frequency RTK, the improvement in accuracy after convergence is not obvious for triple-frequency RTK, but the convergence speed is improved. Furthermore, compared to dual-frequency RTK, the probability of completing convergence within 180s is increased by about 8.0% for triple-frequency-only RTK;
* Compared to the scheme of using both dual-frequency and triple-frequency data simultaneously, the computation cost time of the scheme using triple-frequency-only data is reduced by 8.26 s, improving by approximately 20%.  
Conceptualization, X.D. and C.G.; methodology, X.Y.; software, Y.Z.; validation, J.W. and Y.L.; writing--original draft preparation, X.D. and X.Y.; writing--review and editing, C.G.; visualization, X.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Innovation project of China Railway First Group (2022A-031), the Research and Development Fund Project of Zhejiang A&F University (2021 LFR030), and the National Natural Science Foundation of China (42074014).  
All data can be made available by contacting the corresponding author.  
The authors declare no conflict of interest.
## 2 Methodology  
The MLI shown in Figure 1 is designed for a liquid hydrogen container. It is composed of 60 layers of reflecting shields and the layer density is 23 N/cm. The materials of reflectors and spacers are aluminum foil and glass fiber, respectively. Table 2 shows the basic parameters and properties of the MLI.  
However, for a liquid hydrogen container with the capacity of hundreds and thousands of cubic meters, the vacuuming process of the huge wrapping of MLI blankets is very difficult and time consuming. In order to achieve an ideal vacuum, a MLI blanket is baked until it is as outgassed as possible and the moisture and chemical volatiles in the interlayer have escaped, and then the blanket in an insulation chamber is vacuumed to create an ideal vacuum environment.  
When the interlayer pressure is stabilized at the predetermined value, the vacuum chamber is refilled with helium gas to achieve a slight positive pressure in the MLI blanket. This is to saturate the interior of the blanket with helium gas and regain the ambient pressure in the MLI [18]. Then, the vacuum chamber with the MLI blanket is vacuumed to less than \(10^{-3}\) Pa. If the interlayer pressure of the MLI can be monitored or predicted, it would be an indicator to determine the optimal vacuuming process, MLI structure and the arrangement of adsorbents.
## 3 Sensor Selection
### Indoor and Outdoor Fixed Distance Tests
Notably, the JRT BB2X Laser showcased high precision, with an almost negligible standard deviation, as depicted in Figure 2 and Table 6. Although the LiDAR Lite V4 sensor (Figure 3 and Table 7) manifested accurate readings, it did not match the laser sensor's precision, with its highest standard deviation being under 0.5 m--still notably superior to the WaveShare TOF sensor. Figure 3: Depicts the mean distance measured by the LiDAR Lite V4 from the 0.5 m to 5.0 m range. The red bars represent the standard deviation of the measurements at each interval. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **0.5 m** & **1.0 m** & **1.5 m** & **2.0 m** & **2.5 m** & **3.0 m** & **3.5 m** & **4.0 m** & **4.5 m** & **5.0 m** \\ \hline Indoors & 0.01 & 0.02 & 0.05 & 0.15 & 0.19 & 0.03 & 0.03 & 0.18 & 0.16 & 0.18 \\ Outdoors & 0.02 & 0.02 & 0.12 & 0.20 & 0.46 & 0.16 & 0.04 & 0.21 & 0.10 & 0.49 \\ \hline \end{tabular} Note. We noticed an anomaly where the standard deviation for the LiDAR Lite V4 sensor around 2.0 m to 2.5 m spikes. This could be due to an internal algorithm in the sensor.  
\end{table} 
Table 7: Standard deviation of measurements at different intervals for LiDAR Lite V4. Figure 2: Illustrates the mean distance measured by the JRT BB2X Laser across distances of 0.5 m to 5.0 m. The lack of red bars indicate an extremely small standard deviation. Figure 4 and Table 8 shows that the A02 YYUW ultrasonic sensor's reliability is effective up to 4.5 m and 3.5 m for indoors and outdoors, respectively. Unlike other sensors, its measurement inconsistencies are not due to environmental sunlight conditions but rather its high sensitivity. While dependable at short ranges with a 10 Hz frequency, the sensor's effectiveness diminishes beyond 2.5 m, demanding precise positioning to accurately capture data. Even slight deviations can lead to entirely incorrect readings. Given these limitations, the A02 YYUW's sensitivity and requirement for exact alignment make it an impractical choice for experiments necessitating robust performance, especially in dynamic settings like motion-based cycling. The findings from our tests indicate that the manufacturers' specifications for the sensors hold true only under optimal conditions. In real-world scenarios, the sensors exhibited increased unreliability, underscoring the necessity of these tests to ascertain whether the sensors meet our criteria.
# A Receiving Antenna Allocation Scheme for Downlink MU-MIMO-OFDM Transmission
## 4 Performance Evaluation
### Experimental Measurements of Channel Responses  
Simulated or measured channel responses can be used to evaluate the proposed scheme. Since we consider that it is commercially important to show the effect in practical, existing usage scenarios, we used measured MIMO-OFDM channel responses captured between the AP and STAs in an indoor static line-of-sight (LOS) school environment, as shown in Figure 4. AP sets were at the front, as shown in Figure 4. We measured MIMO-OFDM channel responses at 22 STA locations. The measurement conditions are shown in Table 1. In the measurement, the receiver (Digital Signal Analyzer DSA90804A [22]) installed at the STA location captured beacon signals, including short and long preambles, transmitted from the AP, and calculated channel responses from the captured signals. The measurements were conducted at 5.18 GHz. The bandwidth and the number of subcarriers were 20 MHz and 52, respectively. The transmit power was 13 dBm. The AP had four dipole antennas with an element spacing of 0.5 wavelengths. The STA had two dipole antennas with an element spacing of 0.5 wavelengths. The heights of the AP and STA were 1 m above the floor. The measured channel responses were normalized and yielded an average signal to noise ratio (SNR) of 20 dB because of the basic evaluation in terms of the spatial correlation.
# Open-Source MQTT-Based End-to-End IoT System for Smart City Scenarios
## 2 Technological Background
In [15], the authors propose a VRU warning system, where users exchange among themselves warning messages about potential issues they may encounter when in motion. Such messages are exchanges through commercial smartphones. In [16], the authors consider the possibility of detecting VRUs outside the field of view through wireless communications by considering BLE and WiFi Direct. Apart from the smartphone, the integration of sensor nodes has been considered a promising approach. As an example, in [17], the authors propose the Cooperative Safety System for Vulnerable Road Users (CS4 VRU) architecture, which aims to alert cyclists about potential cars approaching them through wearable devices in the helmet. In addition, the developed smartphone app allows the cyclists to share their position through a VANET. In contrast to the previous approaches, we aim at: (i) developing an E2E solution able to convey the VRU data acquired through external sensors and/or smartphone-embedded sensors toward a centralized premise; (ii) exploiting multiple wireless technologies so as to optimize different links; (iii) exploiting a pub-sub paradigm through the MQTT protocol, hence enabling a logical decoupling between source and destination; (iv) using open-source technologies in order to build a core system where additional plug-in can be added for enabling scenario-specific solutions.
## 1 Introduction  
Underwater communication [1] has attracted much attention in recent years driven by the ever-increasing demand for ocean exploration and investigation. Compared to other communication methods, underwater wireless optical communication has been demonstrated to be of highest transmission data rate and lowest link delay [2; 3]. Although most of the underwater optical links are implemented in line-of-sight configuration and more difficult to be eavesdropped, the potential security vulnerabilities still exists [4]. Fortunately, quantum communication provides a reliable way to achieve unconditional secure communication [5; 6], and the feasibility of quantum communication over seawater has been demonstrated both theoretically and experimentally [7; 8; 9]. A famous application of quantum communication is quantum key distribution (QKD), in which any eavesdropping attemptsby a eavesdropper [PERSON] will unavoidably bring disturbance and be captured by the legitimated parties, [PERSON] and [PERSON] [10].  
According to different implementation methods, QKD can be divided into discrete-variable (DV) QKD [11; 12] and continuous-variable (CV) QKD [13; 14; 15; 16]. Compared to DVQKD, CVQKD is easier to integrate with the standard off-the-shelf optical communication components and has higher secret key rates [17; 18; 19]. Recently, some analyses on underwater free space CVQKD have presented by researchers, such as channel parameter estimation for satellite-to-submarine links [20], security simulation for different kinds of seawater [21], and performance improvement via photon substraction [22]. All these studies used the well-known Beer's law to model the attenuation effects of the seawater due to its simplicity. However, it has two implicit assumptions that the transceiver setup is perfectly aligned and all scattered photons are lost, which may severely underestimate the received optical power since some of the scattered photons can still reach the receiver after being scattered multiple times and contribute to the final received power [2; 23].  
In this paper, we propose an underwater CVQKD performance evaluation method by using Monte Carlo simulation, which simulates the trajectories of the emitted photons in water. Specifically, we consider two main areas: the transmittance of the channel in different water types and the detection efficiency determined by different receiver aperture and FOV. In addition, the light source parameters, such as beam width and maximum beam divergence, are also taken into consideration to evaluate the secret key rate of the underwater CVQKD system. Simulation results show that the secret key rate of the system is obviously different from that calculated by simple Beer's law. By comprehensively considering the practical system parameters, this Monte Carlo-based model provides a feasible way to estimate the performance of various CVQKD systems with different system configurations.
## 1 Introduction
### Contributions of This Study
#### 1.1.3 Benchmarking Roman Urdu Offensive Language Detection
* The first such extensive benchmarking of ML, DL, and transformer-based approaches for offensive language detection in Roman Urdu on a real-world dataset comprising comments from YouTube news channels.
* Our results serve as a reference for future research on offensive text classification for under-researched languages.  
Through the combination of conventional machine learning, deep learning, and a transformer-based fine-tuning approach, this work provides a solid framework for Roman Urdu offensive language classification and opens up new avenues for future research into classifying offensive languages in less-resourced languages that have been largely ignored.  
The remainder of this paper is structured as follows: Section 2 discusses the related work and previous research dealing with offensive language detection. Section 3 outlines the methodology comprising the dataset, preprocessing, and model implementations. Section 4 outlines the results and analysis. In the end, Sections 5 and 6 make up the closing and future works.