esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource-Efficient Multicast URLLC Service in 5G Systems
## 4 Performance Evaluation
### Analysis of Precoder Selection Algorithms
Interestingly, the performance of the QR and SBFC algorithms (which are the best in the M-MIMO case) significantly degrade for a low number of antennas because they use orthogonal projections of UE channels on the precoder null-space. This procedure significantly reduces SINR because the UE channels become non-orthogonal when the number of antennas reduces. In contrast, the IU and MU algorithms do not use this orthogonalization procedure and show much better performance. Multicast adaptations of linear beamforming algorithms (mMRT, mZF, and mMMSE) provide the lowest computation time but the worst performance, which is explained as follows. In the case of a low number of antennas, the mZF algorithm requires the computation of the pseudoinverse of an underdetermined matrix, while the mMMSE algorithm requires the inversion of an ill-determined matrix. Because of the lower number of antennas, the complexity of the SCA-ADMM algorithm with convergence threshold \(\epsilon=10^{-1}\) significantly reduces. Thus, it can be considered as the candidate solution. From the two considered above cases, we can see that the performance and complexity of various precoders significantly depends on the number of antennas: for 4G systems with low number of antennas, IU, SCA-ADMM \(\epsilon=10^{-1}\), and FF-C2 provide a good balance between performance and complexity, while, for 5G M-MIMO systems, QR, SBFC, and IU  
Figure 3: Scenario with \(N=20\) UEs, \(M=4\) antennas at gNB, 3 kmph mobility, and \(T_{SRS}=20\) ms: (**a**) LOS channel, (**b**) NLOS channel. Figure 2: Scenario with \(N=20\) UEs, \(M=64\) antennas at gNB, 3 kmph mobility, and \(T_{SRS}=20\) ms: (**a**) LOS channel, (**b**) NLOS channel. show better results. Thus, a particular algorithm shall be selected taking into account the configuration of antennas and the complexity constraints at the gNB. For that, a preliminary link-level evolution similar to that presented in Figures 2 and 3 can be carried out based on the real channel measurements. Let us analyze the performance of the best precoder selection algorithms (SBFC, QR, IU, and FF-C2) using system-level simulations that take into account the channel aging effect, RB allocation, and MCS selection algorithms. In particular, in Figure 4, we consider 5G M-MIMO systems with 64 antennas, the One TX scheme with NACK-only feedback, and MCS is selected based on OLLA with \(p_{1}=PLR^{QoS}\). We can see that the conclusions for 20 UEs coincide with those of Figure 2: the performances of the SBFC, IU, and QR precoders are close to each other (the difference is below 5%), while FF-C2 provides higher DL resource consumption because of lower SINR. So, proper configuration of the precoder algorithm can reduce resource consumption by more than 25% and allows the precoder to be computed in real time (i.e., precoder construction time is comparable to \(D^{QoS}\)).
###### Abstract  
The stratovolcanoes of Mt. Scenery at Saba and The Quill at St. Eustatius in the Caribbean Netherlands, with a total population of about 5200 people, are part of the active volcanic arc of the Lesser Antilles but lacked a multiparameter volcano and earthquake monitoring system until the beginning of 2018. The permanent seismic network on the islands has been built up since 2006 and was expanded in 2018 with one permanent Global Navigation Satellite System (GNSS) sensor at each volcano and a temperature logger on Saba. We provide technical details of all equipment and the installation procedures, and we show the preliminary results of GNSS data processing. Deploying a remote, permanent network of different sensor types under tropical island conditions and sustaining access to real-time high-quality data to monitor the state of volcanoes is an underappreciated challenge. Despite the problems encountered, we operated the network with an overall availability of 84.5% in the first half of 2018 compared to 70.3% in the years before. The main unresolved problem affecting seismic data quality is related to sudden out-of-balance seismometer mass positions. We provide a complete overview of our monitoring network, the various challenges encountered, and the solutions applied, and we address future plans.  
Keywords: volcano monitoring; seismic network; Global Navigation Satellite System (GNSS) measurements; hot spring; The Quill volcano; Mount Scenery volcano; Caribbean Netherlands +
Footnote †: journal: Geosciences
Most of these studies [10; 11; 12; 13; 14; 15; 16; 17; 18] focus on translating fundus structure image to late phase FFA images. Although the late phase FFA image is critical for ophthalmic diagnosis and disease research, whether or not the filling time of retinal vessels is normal is an important diagnostic basis for retinal vascular occlusion diseases [19], such as central retinal artery occlusion [20] and retinal vein occlusion [21]. Translating fundus structure image to more phases of FFA images is essential to provide information about the filling process of retinal vessels. One approach is to use several independent generative adversarial networks (GANs) to parallelly translate fundus structure image to their corresponding phases of FFA images. Although this method is direct and effective for this task, it does not make good use of mutual information between the different phases of FFA images, resulting in the algorithm's low efficiency. To use the mutual information between the different phases of FFA images, [PERSON] et al. [19] proposed Sequence GAN that connects three GANs in series to translate fundus structure image to three phases of FFA image. However, the first translated FFA image still suffered from a low utilization rate of mutual information. In this paper, we present a series-parallel GAN architecture for translating fundus structure image to fluorescence angiography to improve the utilization rate of mutual information between the different phases of FFA images. The proposed algorithm, called SPFSAT (Series-Parallel Fundus Structure to Angiography Translation), uses a series-parallel architecture-based generator and a single discriminator to achieve a high utilization rate of mutual information between the different phases of FFA images. In the series-parallel architecture-based generator, three decoders share the same encoder that extracts all the features including mutual information from the input fundus structure image at one time with the supervision of all the three phases labeled FFA images. Instead of using three parallel discriminators to judge three FFA images independently [19], the single discriminator judges the three FFA images together, which improves the utilization rate of mutual information between the three phases of FFA images. To show the advancements of our algorithm, we make a comparison among FFA images translated by our algorithm, Sequence GAN [19], pix2 pix [22], and cycleGAN [23]. In detail, the typically translated FFA images are shown and the peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index [24], and mean-squared error (MSE) are evaluated. Some typical FFA images translated by our algorithm are shown to demonstrate the performance of our method.
## 3 Challenges in China's Weather Routing Services
### Insufficient Marine Meteorological Observation Capabilities  
Marine meteorological observation serves as the foundational pillar for weather routing services. Currently, China faces significant deficiencies in marine meteorological observation infrastructure. The existing network of observation stations--including buoys, offshore platforms, island-based stations, and sea-based installations--remains sparse, with most concentrated within 100 nautical miles of China's coastal waters [28]. Critical global shipping routes, major ports (e.g., the Strait of Malacca, Suez Canal), Arctic routes, and key oceanic regions exhibit extensive observational gaps. The consequences of such gaps can be significant; for example, undetected rapidly developing localized storms or inaccurate sea-state forecasts due to data gaps in busy lanes like the Malacca Strait could lead to unexpected vessel damage, cargo loss, or significant delays, impacting supply chain reliability. While approximately 8000 global marine observation platforms (e.g., international Voluntary Observing Ships, buoys) are accessible via the Global Telecommunication System (GTS), these data remain insufficient in terms of spatial continuity, temporal resolution, and observational synchronicity. Recent advancements in satellite remote sensing--such as China's FY (Fengyun) series, the U.S. GOES, and Europe's MeteoSat--have provided indispensable support for monitoring tropical cyclones, sea fog, surface winds, and polar sea ice. However, most satellites lack direct quantitative observation capabilities. Satellite-derived data cannot substitute in situ measurements due to limitations in spatial resolution, retrieval inaccuracies, and inadequate temporal coverage [37].  
Ocean-going vessels are typically equipped with onboard meteorological instruments, and real-time acquisition of their observational data could effectively supplement existing systems through programs like the Voluntary Observing Ship (VOS) scheme [38]. However, operational challenges persist. Anecdotal reports and operational experience suggest that some nations frequently detail vessels carrying meteorological sensors or restrict port access under the excessive invocation of national security concerns. Consequently, ship captains widely resist installing automated weather stations with telecommunication modules, citing privacy and operational risks.
# ARMONIA: A Unified Access and Metro Network Architecture
## 3 ARMONIA Algorithmic Mechanisms
### ARMONIA Deployment Consideration  
ARMONIA leverages machine learning algorithms that enable the dynamic and efficient optimization of the network's resources. The most significant challenges of this proposal will emerge in an actual operating environment. In such an environment the inaccuracies of the machine learning algorithms may cause (unforeseen) service level agreement breeches. To prevent these, a thorough investigation of the accuracy and generally the behavior of the algorithms is needed. Furthermore, the proposed QoT estimator is suitable for brownfield scenarios, where there are historical QoT data to train the algorithm. In the case of a greenfield scenario (a completely new network) a QoT model can be leveraged as mentioned in [22; 24]. As far as the architecture is concerned, it will be realized utilizing open and standardized interfaces (NETCONF, RESTCONF, OpenConfig, Open Transport API) and protocols, and integrating state of the art platforms (ONOS SDN Controller, Open Source Mano, gRPC, Prometheus, Apache Kafka), implementing all the required extensions. Moreover, the selected modular architecture along with the well-defined interfaces will enable the production deployment of the proposed unified access and metro network orchestration and control platform along with the easy implementation of future extensions. In any case, we consider that our proposal is far better than the static and isolated operation of the different network domains as is mainly the case today. The challenges that may arise are probably relatively easy to overcome. The end outcome can result in significant savings as we will show in the simulation experiments in the following section.
## 6 Experimental Results
### Attenuation  
Figure 11 presents mmW for different levels of fog visibilities. The noise appearing in the graph was reduced by averaging. Visibility distance is measured using the optical laser systems and detectors. The fog level was changed from 1000 m down to 80 cm.  
The calculation of the cumulative attenuation along the path of propagation in different fog densities is carried out from the scattering parameter S\({}_{21}\) measured by the VNA. The attenuation is calculated using Equation (9):  
\[L(f)=G_{r}\Bigg{(}\frac{c}{4\pi d\cdot\big{|}S_{2,1}\big{|}f\Bigg{)}^{2}}\cdot G _{t} \tag{15}\]  
Here, the gain of both transmitting and receiving antennas are \(G_{r}=G_{t}=24\) dB at \(f=28\) GHz. The overall distance is d = 36 m. According to the model, the power which is estimated to be received at clear visibility is -43.53 dBm. The experiment demonstrates -43.79 dBm, which is almost the same.  
Using a simulation in Matlab, a comparison is made between the attenuation measured in the experiment and its estimation using the model. The comparison revealed a good match as shown in Figure 12. The solid line represents the model estimation and the dots are for the experimental results.  
Figure 11: Received signal power at 28 GHz for variable fog visibility.
# Post-Earthquake Damage Inspection of Wood-Frame Buildings by a Polarimetric GB-SAR System
## 4 Discussions  
### Strengths and Limitations of This Work  
As shown in Figure 7, radar polarimetry has shown a large sensitivity to the shape and orientation of the wooden members. Polarimetric radar technique is able to provide richer information than the conventional single-polarization subsurface penetrating radar. This has also been verified by [PERSON] et al., when they use radar polarimetry to detect the cavity embedded inside a masonry wall [20]. Moreover, radar polarimetry can suppress clutter and enhance the target signal [26]. We have not conducted a polarimetric calibration to the developed polarimetric GB-SAR system, since the polarimetric calibration over a ultra-wideband is extremely challenging [27]. This limits the accurate analysis of the polarimetric signature from the RGB Pauli image in Figure 10. Further investigation would be carried out to fully explore the potential of radar polarimetry for quantitative interpretation of the earthquake damage to the wood-frame buildings in our future work.  
The survey of the developed GB-SAR system is similar to a 3D survey of ground penetrating radar (GPR) [28]. The main superiority of the GB-SAR over a 3D GPR system is its high resolution, as a result of the employed UWB Vivaldi antenna. We have shown that the 3 mm metal nails can be detected by the UWB GB-SAR system. In contrast, the cracks shown in Figure 9 are hardly recognized. This is because the metal nail, as a perfect electric conductor, can reflect strong electromagnetic energy. While, the crack on the crushed brace in Figure 9 is less than 1 mm in aperture that its reflection is hardly detectable, since the reflection energy from a crack is proportional to its aperture size [29].  
We have demonstrated that SAR processing can greatly improve the lateral resolution in the reconstructed image. Nevertheless, the lateral resolution would be degraded if we increase the distance from the antenna tip to the wall surface (\(z_{a}\)), due to the limited scan aperture of our GB-SAR system. Furthermore, less energy can be penetrated into the wooden wall as a result of wavefront spreading if we increase \(z_{a}\). From these two points of view, the GB-SAR sensor has to work in its near range.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.10 LoRaWAN v1.0-1.1  
First stable version of the protocol (LoRaWAN v.1.0) released in 2015 [7]. In the following years, LoRa alliance released a few minor updates to the standard [48,49]. The two most important updates are LoRaWAN v1.0.3 [50] adds full support of unicast & multicast Class B devices and LoRaWAN v1.1 which enables handover roaming and enhancements in security [51].  
Security enhancements are handled in Over-the-Air Activation (OTAA) procedure by JoinAccept MIC to prevent replay attacks. Also, new Session keys, Rejoin-Request messages are introduced.  
Roaming is a key feature introduced in version 1.1 which enables LoRa nodes to benefit from multiple Network Servers. In addition to the protocol specification, LoRa Alliance introduced LoRa Backend Interfaces Specification document [55]. Since multiple network servers are involved in the roaming process, the document addresses Over The Air Activation and Roaming procedures of end-nodes. These include;  
* Passive and Handover Roaming for existing sessions
* Activation of New Roaming based on Passive and Handover Roaming
## 4 Conclusions  
We have fabricated complementary organic integrated circuits including inverter circuits and ring oscillators based on a p-type D-A polymer semiconductor with a TG structure for both p-type and n-type OTFTs. This complementary organic integrated circuit fabrication process using printed electrodes could dramatically reduce the number of steps compared to the previous fabrication process with a stacked structure. The fabricated inverter circuit and the ring oscillator were driven satisfactorily. The fabricated inverter circuit achieved a gain of 9 at an operating voltage of 10 V. The fabricated ring oscillator circuit oscillated at an operating voltage of 1 V to 20 V, and low-voltage driving was achieved. The delay time was 3.1 ms at an operating voltage of 5 V, 1.1 ms at 10 V, 0.54 ms at 15 V, and 0.34 ms at 20 V. This is in a comparatively high-speed class among complementary integrated circuits using printed electrodes and semiconductors. These results demonstrate the simplification of the fabrication process of electronic devices using printed complementary integrated circuits.  
[PERSON] and [PERSON] fabricated the devices. [PERSON] designed the device structure. [PERSON] optimized the printing condition of the p-type polymer semiconductor. [PERSON] characterized the polymer semiconductor. [PERSON] characterized the OTFT and integrated circuits. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] carried out the data analysis. [PERSON] and [PERSON] designed the research project and wrote the manuscript. All authors contributed to the discussion.  
This research was funded by KAKENHI Grant grant number 18K13797 from the Japan Society for the Promotion of Science (JSPS), and the Center of Innovation (COI) Program from Japan Science and Technology Agency (JST). The APC was funded by KAKENHI Grant grant number 18K13797.  
This study was partly supported by KAKENHI Grant (Grant-in-Aid for Young Scientists) Number 18K13797 from the Japan Society for the Promotion of Science (JSPS), and the Center of Innovation (COI) Program from Japan Science and Technology Agency (JST).  
The authors declare no competing interests.  
Figure 4: **(a)** Top-view photograph of a printed organic five-stage ring oscillator with a 10 \(\times\) 10 device layout. Scale bar, 25 mm. **(b)** Circuit diagram of the five-stage ring oscillator. **(c)** Top view micrograph of the fabricated ring oscillator device. Scale bar, 1 mm. **(d)** Oscillation frequency (blue) and signal propagation delay per stage (red) as a function of operating voltage. **(e)** Ring oscillator output signal at V\({}_{\text{DD}}\) = 2.5, 5.0, 10.0, and 20.0 V.
## 3 Effects of Arsenic Contamination
### Effects on Health  
Groundwater contamination by arsenic has resulted in numerous serious health consequences in Bangladesh [47; 48; 49]. Globally, arsenic contamination in drinking water has reportedly led to the exposure of about 150 million people leading to serious health effects [50]. Of these, about 110 million people live in Bangladesh, Cambodia, China, India, Laos, Myanmar, Nepal, Pakistan, Taiwan and Vietnam [51]. In addition, many people are affected through consumption of arsenic-contaminated foods produced from the use of arsenic contaminated groundwater [50; 51]. It has been found that rice grains contribute about half of the daily intake of arsenic in Bangladesh [52]. A survey conducted within arsenic-affected villages in Bangladesh collected hair, nail, urine and skin-scale samples totaling more than 10,000 which were subsequently analyzed for arsenic [53]. The results of the survey indicated that the arsenic concentrations in 93.8% and 95.1% of nails and urine samples, respectively, exceeded the normal level. For nail, the acceptable arsenic concentration range is 0.43-1.08 mg/kg [54], while for urine the range is 0.005-0.04 mg/day [55]. Furthermore, 83.2% and 97.4% of hair and skin-scale samples, respectively, gave arsenic contents that far exceeded the toxic level of 1 mg/kg [56]. These results therefore revealed a serious impact on health that has arisen from human exposure to arsenic through groundwater contamination in Bangladesh.  
A recent comprehensive overview on the adverse effects from inorganic arsenic exposure details the major health issues [57]. Carcinogenic effects are particularly prevalent and the most common consequences resulting from poisoning from drinking arsenic contaminated water are skin diseases, including hyperkeratosis, keratosis, leuco-melanosis and melanosis (hyper pigmentation) [57; 58; 59; 60; 61; 62; 63; 64]. Arsenicosis is initially manifested by melanosis which usually occurs all over the body [57; 62]. The hardening of the melanosis spots leads to the commencement of keratosis [62]. A long-term exposure to arsenic results in the hyperkeratosis of the palms and soles [59]. Other associated health effects resulting from ingestion of arsenic include cardiac failure, chromosomal abnormality, cirrhosis, diabetes mellitus, gangrene, goiter, hypertension (high blood pressure), liver enlargement, myocardial degeneration, peripheral neuropathy and skin cancers [59; 62; 63].  
Also, the development of cognitive and psychological functions in children has been shown to be affected by extended consumption of arsenic contaminated drinking water [65]. Furthermore, areas where highly contaminated groundwater is used have been shown to be more prone to higher fetal loss and infant deaths [66].