esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Experimental Setup  
Two identical laboratory-scale sequencing batch reactors (SBRs) and an ANAMMOX reactor were constructed (Figure 1). The reactors were made from transparent Plexiglas, each having an effective volume of 10 L.  
One SBR was operated with an intermittent aeration (IASBR) strategy and the other was operated with continuous aeration (CASBR) in an 8 h cycle. For the IASBR, after a 60 min non-aeration period, the reactor was intermittently aerated with successive 50 min aeration/30 min non-aeration periods. For the CASBR, 250 min continuous aeration was adopted after a 180 min non-aeration period. Then, the settle phase lasted 40 min before the effluent was withdrawn in the last 10 min. Each SBR was stirred with a rectangular mixing paddle. The air was supplied using aquarium air pumps at an air flow rate of 0.8-0.9 L air/min.  
The characteristics of the livestock wastewater taken from a mesophilic manure digester were: 10,260 \(\pm\) 780 mg/L of COD; 3112 \(\pm\) 94 mg/L of NH\({}_{4}\)+-N; 3478 \(\pm\) 75 mg/L of total nitrogen (TN); 1.25 \(\pm\) 0.18 g/L of suspended solids (SS); and pH of 8.26 \(\pm\) 0.05. The wastewater had a COD to TN ratio of 2.96, and the BOD\({}_{5}\) to COD ratio was only 0.38, indicating a low proportion of biodegradable organic matter in the wastewater. The livestock wastewater was fed to SBRs with peristaltic pumps at loading rates of 3.08 kg COD/(m\({}^{3}\).d)  
Figure 1: Schematic diagram of SBRs and ANAMMOX reactor systems.  
and 0.93 kg NH\({}_{4}\)+-N/(m\({}^{3}\)-d). The temperature of two SBRs was controlled at 26 \(\pm\) 1 \({}^{\circ}\)C, simulating the digestate liquid temperature after anaerobic digestion.  
An upflow anaerobic sludge blanket (UASB) reactor was built for cultivating ANAM-MOX biomass (Figure 1). The effluents of IASBR were collected and adjusted with NH\({}_{4}\)Cl to obtain a desirable nitrogen ratio (NH\({}_{4}\)+-N: NO\({}_{2}\)\({}^{-}\)-N = 1: 1.32) for the influent of the ANAMMOX reactor [9]. The seed sludge fed to the UASB was a mixture of anaerobic denitrification sludge, anaerobic composing sludge, river sediment sludge, local soil and partial nitrification activated sludge. Before treating the raw effluents of IASBR, influent synthetic wastewater (30 mg/L NH\({}_{4}\)+-N and 40 mg/L NO\({}_{2}\)\({}^{-}\)-N from Day 1 to Day 100; 60 mg/L NH\({}_{4}\)+-N and 80 mg/L NO\({}_{2}\)\({}^{-}\)-N from Day 101 to Day 223; and 90 mg/L NH\({}_{4}\)+-N and 120 mg/L NO\({}_{2}\)\({}^{-}\)-N from Day 223 to Day 289) was continuously fed into the UASB at a hydraulic retention time (HRT) of 8 h for ANAMMOX biomass cultivation. The temperature of the UASB was controlled at 35 \(\pm\) 2 \({}^{\circ}\)C, and an optimum pH of 8.0 \(\pm\) 0.4 was controlled through adding NaHCO\({}_{3}\) into the influent.
## 2 Materials and Methods  
### Patient Dataset  
To predictidus obliteration, data from 119 patients with AVM who underwent radiosurgery (Novalis; Varian Medical Systems, Palo Alto, CA and BrainLAB, Feldkirchen, Germany) at our institution from January 2001 to January 2018 were included in this study. Exclusion criteria were: (a) under 6 months duration of follow-up patients, (b) previous treatment SRS patients, (c) insufficient baseline data. Appropriate approval from the clinical ethics committee of our institution (Inje University IlSan Paik Hospital, IRB No: 2019-12-016) was obtained.  
In all patients, computed tomography angiography (CTA), magnetic resonance imaging (MRI) with gadolinium enhancement, magnetic resonance angiography (MRA), and trans-femoral cerebral angiography (TFCA) were used to identify and evaluate cerebral AVMs. In this study, 66 patients (55.46%) who elected for radiosurgery as a therapeutic intervention for AVM presented with SM grade III or higher, while 53 patients (44.54%) had SM grade II or lower. The selection of a treatment method was contingent upon the precise location of the AVM, with patients being carefully assessed for suitability for surgical intervention/excision or radiosurgery by board-certified neurosurgeons. When performing radiosurgery, a stereotactic head frame and equipment were used. In addition, biplanar cerebral angiography and computed tomography (CT) were combined to develop radiosurgery plan. The image delineation foridus, feeding arteries, and draining veins were included, and radiation dose was calculated by the treatment planning system (iPlan, BrainLAB, Feldkirchen, Germany) using a 6 MV photon beam with 80% isodose line covered in total volume ofidus.  
To identify the prognostic factors that affect AVM nidus obliteration, the neurosurgery department of our hospital used their existing knowledge to rank the factors that affect patient outcomes. Previously reported prognostic factors were also included in this study [20; 21; 22; 23]. The factors selected as contributing to AVM nidus obliteration were age, total dose (Gy), AVM location (lobar or deep), AVM volume (mm\({}^{3}\)), preoperative neurological deficits, AVM nidus size, modified AVM score, Virginia radiosurgery AVM score (VRAS), deep vein drainage, eloquent area, Spetzler-Martin (SM) grade, hemorrhage after radiosurgery, and toxicity grade. Nidus obliteration was evaluated for all patients during the follow-up period from 4 to 194 months (mean 33.4 months) by radiographic assessment including TFCA, MRA, and CTA.
## 6 Conclusions  
The purpose of this article was to examine the security and capacity enhancement of E-FCR control from cyberattacks, particularly DoS and FDI attacks. As a point of vulnerability for the low-cost and blind data injection attack, we modeled the simplified MIDC system under DoS and FDI attacks. Two different types of DoS attacks were specifically suggested to assess their impact on the three stages of feature frequency responses. The emphasis was on improving the steady-state and frequency nadir contributions in terms of the balancing arrangement along with frequency stability control. Additionally, different power system sizes, such as those on Jeju Island and the mainland, were examined during contingency and cyber attack operations.  
The first contribution involves creating a normalized correlation concept as a method of cyber security detection based on the real DC current measurement in the MIDC system. Regarding the theoretical normalized correlation concept, simulations on three different types of attack were used to conduct a comparative study of three states of cyber attack operations, such as a normal, attack, and mitigation operation. The second contribution is described as an improvement to the mitigation control using the adaptive ramp-rate value. Without mitigation measures, network security may be compromised, for example, if the frequency nadir drops below a predetermined value and unintentional load shedding occurs. According to the study, the MIDC power system's response to a cyber attack on missing data was at its worst because there was no power available when the contingency occurred. The energy storage system (ESS) that is available in the Jeju Island power system should be taken into account along with other ancillary services when exploring development research. The specified mitigation strategy for a number of ancillary services may also be improved by the authors, as well as the system's stability and dependability.  
Conceptualization, U.F.R., J.L. and M.Y.; investigation, U.F.R.; methodology, U.F.R., J.L. and M.Y.; software, U.F.R.; validation, M.Y.; visualization, U.F.R. and M.Y.; supervision, M.Y.; writing--original draft preparation, U.F.R.; writing--review and editing, U.F.R., J.L. and M.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Electric Power Corporation grant (R21X001-1) and National Research Foundation Grant (2020R1F1A1076638) funded by the Korean government.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 29: Mainland frequency in the false data injection attack operation.
## 2 Method
### Point Feature Extraction
#### 2.1.2 Single Point Multi-Scale Multi-Feature Fusion  
Since the features of the single point are dependent on the selected neighborhood space, different neighborhood spaces have different expression capabilities for different classes of point clouds.  
Additionally, the structure descriptions of point clouds with different resolutions also have certain differences. The local feature description of the single scale for a point is relatively single, and there are some noise points in the point cloud, which can make the simple feature of the single scale unable to accurately describe the feature of the single point. Therefore, a multilevel features fusion method based on the multi-neighborhood space and multi-resolution is proposed. As shown in Figure 1c, the proposed method constructs the multi-scale space by changing the resolution and the number of neighborhoods of the point cloud. Following this, multi-scale features for each single point in the point cloud are extracted. Because the elevation feature is not affected by the scale changing, we select NAD, LSH, CF and PPR features to construct the multi-scale features. We extract the features of a single point in each scale by choosing \(\mu\) neighborhoods with different resolutions and \(\upsilon\) different neighborhood sizes under the original resolution. The multi-scale features of each point are expressed respectively as \(\text{F}^{\prime}_{\text{NAD}}\), \(\text{F}^{\prime}_{\text{LSH}}\), \(\text{F}^{\prime}_{\text{cov}}\), and \(\text{F}^{\prime}_{\text{plane}}\). Considering the validity of the features and the efficiency of the calculation, this generally results in \(2\leq\mu+\upsilon\leq 5\). In addition, the description of the single point feature only represents one characteristic of the point cloud. Therefore, it is necessary to fuse multiple features. After fusing the features, the multilevel features are expressed as follows:  
\[F=\left[F_{z},F^{\prime}_{NAD},F^{\prime}_{LSH},F^{\prime}_{cov},F^{\prime}_{ plane}\right] \tag{21}\]  
Because we aim at an ALS point cloud, the extracted elevation features are only two-dimensional and play an important role in the point cloud classification. In addition, when the point cloud features are extracted, the values of each feature have been normalized to [0, 1]. In order to reflect the role of the non-zero feature value, the feature should be normalized again according to Formula (22) when the extracted feature \(F\) is sparse. While the extracted feature \(F\) is not sparse, there is no need to normalize the feature. Therefore, the constructed feature is \(X=\left[F^{*}_{z},F^{*}_{NAD^{\prime}},F^{*}_{LSH^{\prime}},F^{*}_{cov},F^{*} _{plane}\right]\).  
\[F^{*}_{i,j}=\frac{F_{i,j}-\min(F_{j})}{\max(F_{j})-\min(F_{j})} \tag{22}\]  
where \(F^{*}_{i,j}\) is the value of the \(i\)-th row and the \(j\)-th column in the normalized feature matrix \(F^{*}\). \(F_{i,j}\) is the value of the \(i\)-th row and the \(j\)-th column in the feature matrix \(F\). \(F_{j}\) is the vector of the \(j\)-th column (for all the points) in the feature matrix \(F\).
# Comparative Impact Analysis of Cyclone Ana in the Mozambique Channel Using Satellite Data
## 1 Introduction
### An Introduction to Flooding  
A key consequence of tropical cyclones and depression systems is heavy precipitation. Flooding is defined as the event whereby precipitation falls more quickly than water can be absorbed into the ground or carried away by rivers or streams. River flow, rainfall, tide surge and topography all define the type of flooding that has occurred [8]. It is estimated that flood-related impacts will worsen by 187% by 2050 under the Hadley Centre Coupled Model (HadCM3) [9].  
As such, today, and increasingly so in the near future, flooding will induce landslides, destruction of buildings, the creation of stagnant bodies of water and much more [8]. One key human cause for concern is also its alteration to food supply, sanitation facilities, water contamination, and access to healthcare due to overcrowding or destruction of facilities [10].  
Although it is important to note that some floodings, particularly small- and low-magnitude flooding, can be beneficial through their capacity to recharge groundwater, wetlands, improve soil fertility and construct floodplains [11], most tropical cyclones tend to carry negative ecological impacts due to their high magnitude. Flooding tends to increase erosion, lead to eutrophication [11] and increase groundwater stores, thus making these areas more prone to further extreme flooding in the case of another high precipitation event [12]. In areas of high-exposure indexes, such as coastal areas in Mozambique and Madagascar [13], the impacts of climate change, including an increase in the frequency of storm events, is likely to feed this cycle of negative ecological impacts following flooding.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
###### Abstract  
This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system's effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristics of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount.  
f  
requency hopping spread spectrum (FHSS); quantum random number generator (QRNG); random processing units; quantum computing; drone communications; quantum enhanced communications +
Footnote †: journal:  
Article
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 2 Related Works
### LLM-Enabled Semantic Communication  
Recent breakthroughs in LLMs have significantly impacted semantic communication across various domains [13]. For instance, [PERSON] et al. addressed challenges in semantic communication for image data through a framework incorporating a segment-anything model-based knowledge base, attention-based semantic integration, and adaptive compression techniques [31]. Similarly, ref. [32] propose a semantic communication framework (LAM-SC) tailored for image data, leveraging LLMs as the core knowledge base. These approaches harness LLMs' deep understanding of human knowledge to construct robust knowledge bases for diverse communication tasks. [PERSON] et al. exploited the capabilities of LLMs in language understanding, planning, and code generation, combined with classical command strategies like task-oriented and communication-edge joint learning. They proposed an efficient multifunctional framework for coordinating edge AI models in executingtasks of edge intelligence [33]. However, current LLM-based semantic communication systems primarily focus on higher-level tasks such as user intent understanding, posing challenges in their application to physical-layer encoding and decoding. Nevertheless, LLMzip has achieved compression ratios surpassing previously known estimates of the entropy bounds by employing LLMs for lossless text compression [34]. [PERSON] et al. put forward a semantic communication system driven by LLMs (LLMs) that uses multimodal features to reconstruct raw visual information, thereby improving transmission quality of images [35]. This demonstrates exceptional source coding performance at the physical layer, notwithstanding complexity issues. However, the feasibility and benefits of utilizing LLMs for channel encoding/decoding and modulation/demodulation remain largely unexplored.
## 3 Results
### Production of Organic Acids in MRS Broth  
KCC-37 and KCC-38 produced a significant amount of fermentative organic acids, including lactic acid, acetic acid, and succinic acid. However, significant differences existed in the production of lactic acid  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S.No** & **Extracellular Enzymes** & **KCC-37** & **KCC-38** \\ \hline
1 & Alkaline phosphatase & +++ & +++ \\
2 & Esterase (C\({}_{4}\)) & ++ & ++ \\
3 & Esterase lipase (C\({}_{8}\)) & + & + \\
4 & Lipase (C\({}_{14}\)) & +++ & +++ \\
5 & Leucine arylamidase & +++ & +++ \\
6 & Valine arylamidase & + & + \\
7 & Cystine arylamidase & +++ & +++ \\
8 & Trypsin & + & + \\
9 & \(\alpha\)-Chymotrypsin & ++ & ++ \\
10 & Acid phosphatase & ++ & ++ \\
11 & Naphth-AS-biphosphohydrolase & +++ & +++ \\
12 & \(\alpha\)-Galactosidase & +++ & ++ \\
13 & \(\beta\)-Galactosidase & +++ & +++ \\
14 & \(\beta\)-Glucuronidase & ++ & ++ \\
15 & \(\alpha\)-Glucosidase & ++ & ++ \\
16 & \(\beta\)-Glucosidase & ++ & ++ \\
17 & N-Acetyl-\(\beta\)-glucosamineaminidase & +++ & +++ \\
18 & \(\alpha\)-Mannosidase & +++ & +++ \\
19 & \(\alpha\)-Fucosidase & +++ & +++ \\ \hline \hline \end{tabular}  
* +; Weak production: ++; moderate production: +++; strong production.  
\end{table}
Table 2: Production of extracellular enzymes by \(L\). _plantarum_ KCC-37 and \(L\). _plantarum_ KCC-38.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**S. No** & **Name of Antibiotics** & **ug/disc** & **KCC-37 KCC-38** \\ \hline
1 & Chloramphenicol (C) & 50 & S & S \\
2 & Kanamycin (K) & 30 & S & R \\
3 & Nitrofuranotin (NIT) & 50 & S & S \\
4 & Tetracycline (TE) & 100 & R & R \\
5 & Streptomycin (S) & 25 & S & S \\
6 & Sulphafurazole (SF) & 300 & S & S \\
7 & Colistin methane sulphonate & 100 & S & S \\
8 & Dicloxacillin (D/C) & 1 & S & S \\
9 & Ampicillin (AMP) & 10 & S & S \\
10 & Amikacin (AK) & 30 & R & R \\
11 & Gentamicin (GEN) & 10 & S & S \\
12 & cefoxitin (CX) & 30 & S & S \\
13 & Cefalexin (CN) & 30 & S & S \\
14 & Cefuroxime (CXM) & 30 & S & S \\
15 & Co-Trimoxazole (COT) & 25 & S & S \\ \hline \hline \end{tabular}  
* R—Resistant against tested antibiotics; S (\(>\)10 mm)—Sensitivity against tested antibiotics.  
\end{table}
Table 3: Antibiotic sensitivity patterns for \(L\). _plantarum_ KCC-37 and \(L\). _plantarum_ KCC-38 against commercial antibiotics.  
acid concentration among the investigated strains. KCC-38 produced a higher amount of lactic acid than the KCC-37 while other acid production was similar in both strains (Table 4).
# Cross-Layer-Aided Opportunistic Routing for Sparse Underwater Wireless Sensor Networks
## 7 Conclusions  
In this paper, a Cross-Layer-Aided Opportunistic Routing Protocol is proposed to reduce the congestion in multi-hop sparse UWSNs. The forwarder selection algorithm based on a fuzzy logic is first presented to select an optimal forwarder with good forward link quality and small probability of congestion. On this basis, to alleviate the collision problem, a back-off strategy is introduced in CLOR. Moreover, an adaptive transmission strategy based on inter-session network coding is adopted to improve the transmission reliability of the data packets from various sources. Furthermore, this paper theoretically analyzes the main strategies in CLOR, including the effectiveness of negotiation, effectiveness of burst transmission, and reliability of network coding. Eventually, we employ the Bellhop ray tracer to model an underwater acoustic channel of a specific sea region, and we compare the performance of CLOR with the existing Elfood, FBR, and CARP in the networks under different traffic rates. Simulation results indicate that the proposed CLOR outperforms other three protocols in terms of packet delivery ratio, energy efficiency, and Goodput at different traffic rates, especially at high traffic rates, while CLOR also performs outstanding advantages in an aspect of end-to-end delay at low traffic rates. The future work will be focused on improving the processing procedure of CLOR and further minimizing the delay of CLOR.  
Conceptualization, D.Z. and G.L.; Funding acquisition, D.Z.; Methodology, G.L.; Software, G.L.; Validation, D.Z., G.L., R.X. and Y.S.; Data curation, G.L.; Formal analysis, G.L.; Investigation, D.Z., G.L. and R.X.; Supervision, D.Z.; Writing--original draft preparation, G.L.; Writing--review and editing, D.Z., G.L. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Natural Science Foundation of China under grant 61371099.  
Not applicable.  
The authors declare no conflict of interest.