esa-sceva/satcom-chunk-collection huggingface dataset:
# Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings
## 9 Threat Analysis  
A comprehensive threat analysis is essential in addressing the distinctive security concerns associated with the federated smart building scenario. As illustrated in Figure 17, three discrete threats were identified: client-side, MiTM, and server-side threats. In particular, a client with malicious intent can corrupt its data to negatively impact the overall performance of the global model. This corruption can take many forms, ranging from injecting noise to flipping labels, and can have various effects. A targeted poisoning attack can be viewed as a backdoor attack, where the goal is to alter the prediction of the global model for a specific type of data or sample. The mitigation of this threat is achieved using secure aggregation methods. In the case of the MIMT threat, a malicious actor positioned  
Figure 16: Experiment 3 settings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Rounds of NW CNN** & **Rounds of Sensor CNN** \\ \hline
1 & 2200 & 800 \\ \hline
2 & 2500 & 1200 \\ \hline
3 & 2300 & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Experimental results.  
between the server and a client can intercept and alter client/server weight updates, compromising the model's performance. Secure connection protocols such as SSL and IPsec must be established between the server and clients to ensure confidentiality, integrity, and non-repudiation. The first two properties prevent sniffing and tampering, while the third helps the server avoid a situation where a client poisons their data and falsely claims that the weights were modified in transit. Server-side threats originate from a server with malicious intent that could potentially obtain information about the client's training data by analyzing the provided weights. The most commonly used countermeasures against these threats are Homomorphic Encryption (HE) and Differential Privacy (DP) [28; 74]. Homomorphic encryption allows the server to perform aggregation without decrypting data, while differential privacy involves each client injecting some noise over their weights. However, homomorphic encryption cannot be used with secure aggregation methods such as clipping and zeroing, while differential privacy can noticeably affect the overall model performance.  
In the considered FL scenario, the server is a trusted entity that coordinates distributed learning across multiple smart buildings. For this reason, we specifically target poisoning attacks and employ defenses tailored to detect and mitigate this type of threat, while attacks involving information leakage are not considered central to the analysis. However, techniques such as DP and HE could be used to strengthen privacy protection when confidentiality becomes an issue in future works, where the strong assumption of a trusted aggregator will be relaxed.
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 4 Diversity of Halophilic Fungi  
Mycopiota inhabiting natural saline environments consists of halotolerant and halophilic fungi, which are represented not only by previously known species but also by new and rare species [33]. For the most part, halophilic fungi, with the exception of a few obligate halophiles such as _Wallemia ichthyophaga_, do not require sodium chloride for growth, and they can adapt to a wide range of salt concentrations, from low concentrations characteristic of marine waters to nearly saturated NaCl solutions [38].  
Research shows that halophilic fungi occur in a variety of hypersaline environments located almost all over the world, including Slovenia, Romania, Thailand, China, India, Brazil, and many others [9; 39; 40; 41; 42; 43]. They were isolated from environments with different salinity levels such as marine environments (sea water, marine plants, and mangroves) [41; 43; 44], solar salterns that are installations for production of salt by evaporating sea water [39], natural salt lakes, salt mines [42], saline soil, salt deserts, sebkhas which are areas resulting from the evaporation of salt lakes characterized by a large variety of soluble salts [10], and salted foods and fermented products with a high salt content [45].  
Their dominant representatives are yeast-like fungi and different related melanized species of the genus _Cladosporium_, anamorphic _Apergillus_ and _Penicillium_, the teleomorphic _Eurotium_ and _Emericella_, filamentous species of genus _Wallemia_, _Alternaria_, and _Scopulariosis_[46]. The majority of halophilic fungi have been classified into Ascomycota, and several species belong to Basidiomycota [44]. Within Ascomycota, the main genera include Capnodiales, Dothideales, and Eurotiales. Many halophilic fungi from those orders show a xerotolerant tendency, growing well in low \(\mathrm{a}_{\mathrm{w}}\) conditions beside halophilic capabilities [9]. Halophilic fungi which are important species of ascomycetes are _Apergillus sydowii_, _Apergillus versicolor_, _Aurebosidium pullulans_, _Hortaa verneckii_, _Penicillium chrysogenum_, _Phaetotheca triangularis_, and _Trimmatostroma salinum_[46; 47]. Basidiomycota contains Trichonosporales, Sporidiales, and Wallemia orders with significant species: _Wallemia ichthyophaga_, _Wallemia sebi_, and _Wallemia muriae_[48].  
The best-known fungi considered to be model halotolerant and halophilic organisms include Hortaea werneckii, Wallemia ichthyophaga, Debaryomyces hansenii, Aurebosidium pullulans, and Trimmatostroma salinum [33].
# Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives
## Appendix A Spectral Indices  
### RGB Combinations  
In 2019, [PERSON] et al. [63] proposed different RGB combinations to target Li-bearing pegmatites using atmospherically corrected, surface reflectance ASTER, Landsat-5, Landsat-8, and Sentinel-2 products (Table A1).
## 2 Materials and Methods
### Effect of Initial Concentrations  
It was only the concentrations of Pb(II)/Cd(II) metal ions that varied from 5 to 15 mg\(\cdot\)L\({}^{-1}\), but the other experimental conditions and procedures were no different from the effect of pH. In addition, to study the adsorbability of O-MWCNT\({}_{\text{S}}\) for coexisting metals, the competitive test of Pb(II) and Cd(II) was conducted, as described in this subsection. It was designed such that the same mass of Pb(II) and Cd(II) was mixed in the vials and then initiated as a single system.  
The following describes the structure of the experimental design utilized in this investigation:  
\[\text{Removal}\ (\%)=(\frac{\text{C}_{0}-\text{C}_{\text{e}}}{\text{C}_{0}}) \times 100 \tag{1}\]  
\[\text{Adsorption\ capacity}:\ \text{q}_{\text{e}}=\frac{(\text{C}_{0}-\text{C}_{ \text{e}})\text{V}}{\text{m}} \tag{2}\]  
\[\text{Quasi-primary\ kinetic\ fitting}:\ \text{q}_{\text{t}}=\text{q}_{\text{e}}(1- \text{e}^{-\text{k}\text{l}\text{t}}) \tag{3}\]  
\[\text{Quasi-secondary\ kinetic\ equations}:\ \text{q}_{\text{t}}=\frac{\text{K}_{ 2}\text{q}_{\text{e}}^{2}\text{t}}{1+\text{K}_{2}\text{q}_{\text{e}}\text{t}} \tag{4}\]  
\[\text{Langmuir\ isothermal\ equation}:\ \frac{\text{C}_{\text{e}}}{\text{q}_{ \text{e}}}=\frac{1}{\text{K}_{\text{L}}\text{q}_{\text{L}}}+\frac{\text{C}_{ \text{e}}}{\text{q}_{\text{L}}} \tag{5}\]  
\[\text{Freundlich\ isothermal\ equation}:\ \text{Inq}_{\text{e}}=\text{InK}_{ \text{F}}+\frac{1}{\text{n}}\text{InC}_{\text{e}} \tag{6}\]  
\[\text{Arrhenius\ equation}:\ \text{Ink}_{\text{obs}}=-\frac{\text{Ea}}{ \text{RT}}+\text{InA} \tag{7}\]  
where C\({}_{0}\) and C\({}_{\text{e}}\) are preliminary and equilibrium concentrations, respectively (mg\(\cdot\)L\({}^{-1}\)); Q\({}_{\text{e}}\) denotes the adsorption amount equilibrium (mg\(\cdot\)g\({}^{-1}\)); [PERSON] et al. reported that k\({}_{1}\) is a constant denoting the rate of the quasi-primary kinetic reaction, i.e., the physical adsorption rate (min\({}^{-1}\)); and k\({}_{2}\) represents the quasi-secondary kinetic reaction rate constant, i.e., chemisorption rate (g\(\cdot\)mg\({}^{-1}\cdot\)min\({}^{-1}\)) [27]; q\({}_{\text{L}}\) is the maximum theoretical adsorption capacity (mg\(\cdot\)g\({}^{-1}\)); k\({}_{\text{L}}\) is Langmuir constant; k\({}_{\text{F}}\) and n are Freundlich constants; A is the Arrhenius constant; k\({}_{\text{obs}}\) is the rate constant [28; 29; 30].
# Dynamic Phase Comparison Planar Direction-Finding Algorithm on Satellite Radio Receiver
## 2 Description of the Algorithm
### Phase Ambiguity  
In the process of direction finding using both the phase interferometer algorithm and the dynamic phase comparison algorithm, the phase difference between the signals received by the moving receiver twice is periodic with a cycle of \(2\pi\). However, the phase difference between the two signals is measured modulo \(2\pi\) within the range of \([-\pi,\ \pi)\). If the actual phase difference exceeds this range, these DF algorithms will experience ambiguity in the phase calculation process.  
If the actual phase difference is given by  
\[\varphi=2N\pi+\varphi^{*}\ (N\in\mathbb{N}^{*}), \tag{16}\]  
where \(N\) represents the phase ambiguity number and \(\varphi^{*}\in[-\pi,\ \pi)\), then the unambiguous azimuth angle range for the DF system is  
\[\alpha=\bigg{[}-\arccos\frac{\lambda}{2\pi d},\ \arccos\frac{\lambda}{2\pi d }\bigg{)}. \tag{17}\]  
From the equation, it is evident that to expand the DF range of the comparison phase system, it is necessary to shorten the baseline length. However, this requirement contradicts the need to improve DF accuracy.  
Hence, in the phase interferometer algorithm, there is an inherent contradiction between the DF accuracy and the maximum unambiguous angle. To overcome this issue, multi-baseline configurations are typically adopted, including the use of long and short baselines, virtual baselines, and other disambiguation algorithms [18; 19; 21; 22; 23]. In practical applications, the method of combining long and short baselines for VLF signals has specific limitations. Firstly, for VLF signals, the wavelength is very long. Such long baselines not only make the computational load relatively large when using multi-baseline disambiguation algorithms but also increase the system's complexity. Secondly, long baselines pose certain challenges for the installation locations of ground-based DF array antennas and are even more difficult to implement in space-borne plasma imager DF systems.  
However, in the dynamic comparison phase algorithm, its principle takes advantage of the high-speed motion of satellites to be equivalent to the array antennas, with the interferometer baseline length determined by the velocity of the satellite and the time difference between two signal receptions. Hence, in the process of dynamic direction finding, after completing the first signal reception, the corresponding maximum baseline length can be calculated through the measurement capability of the target signal frequency. Then, based on the velocity of the satellite, the required time interval for the second signal reception is directly calculated, thus achieving the maximum DF accuracy under the condition of phase ambiguity elimination.
## 3 Numerical Calculation Application and Experimental Setup
### Experiment Setup  
#### 3.5.1 Hydraulic Performance Test  
To verify the numerical results, a hydraulic performance test of the industrial centrifugal pump was carried out in an industrial test rig, during which vibration signals were collected at the bearing end cover. The test system was shown in Figure 9, which agree with the level-1 standards in ISO 9906-2012 [31]. The test rig mainly includes two valves, two connected water tanks, a pump, an electromotor and some measuring instruments. During test, the water temperature was controlled within 19.8\({}^{\circ}\)C \(\pm\) 0.3 \({}^{\circ}\)C. The random uncertainty in the process of data acquisition was within \(\pm\)0.15%. The flow rate was measured by electromagnetic flowmeter with a measuring error of \(\pm\)0.2%. Two pressure gauges were utilized to obtain the pump head, and the measuring error of each one was \(\pm\)0.1%. A torque transducer and a laser tachometer were applied to measure the torque and rotation speed of the main shaft respectively, and their measurement errors were both \(\pm\)0.1%. Thus, based on uncertainty analysis, the total measurement uncertainties for the head and efficiency are 0.21% and 0.38% respectively.
## Theory  
### Environmental Influences on Temperature Measurement  
In temperature measurement and non-destructive testing using infrared thermography, the temperature measurement is affected due to radiation from the surrounding environment. This is influenced by emissivity and reflectance. In active thermography, which utilizes an inspection environment under daylight conditions or an external heat source, radiant energy is reflected from the surrounding environment on the surface or flows directly into the infrared (IR) camera. Additionally, measurement errors occur due to the effects of moisture, gases, and convective heat transfer contained in the atmosphere. Figure 1 shows the radiant energy incident on the IR camera due to external heat sources and the surrounding environment during LIT inspection and can be expressed as Equation (1) [4; 12].  
Figure 1: Principle of radiation emission from surrounding environment and heat source in thermographic testing.  
\[W_{m}=\tau\varepsilon W_{obj}+\tau W_{L}+\tau\rho W_{L}+\tau W_{amb}+\tau\rho W_{amb} \tag{1}\]  
where \(W_{m}\) is the energy detected by an infrared camera and is measured as the apparent temperature of \(T_{m}\). In addition, \(W_{obj}\), \(W_{L}\), and \(W_{amb}\) correspond to the temperatures of \(T_{obj}\)\(T_{L}\) and \(T_{amb}\), which are the inspection target, heat source, and surrounding radiant energy, respectively. \(\varepsilon\) and \(\rho\) are the emissivity and reflectance of the inspection target, and \(\tau\) represents the transmittance of the atmosphere. In order to accurately measure the radiant energy of the test object, it is necessary to minimize the influence of the atmosphere and surroundings.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 2 Motivation and Local Security  
In 5G networks, supporting the high concurrent connections of many low-cost devices is very important. International mobile telecommunication (IMT) expects massive access of at least 1 million devices per squared kilometer to be supported in 5G networks [15]. As such, 5G should cover densely populated areas, such as residential buildings or business centers in urban environments [16], where a large signaling overhead induced by conventional cryptographic authentication mechanisms may cause inevitable delays. Furthermore, when massive devices access 5G networks simultaneously, severe signaling congestion can be incurred over the network nodes, such as mobility management entity (MME) and home subscriber server (HSS) because of the conventional centralized security managed at the core networks. Alternatively, a new security architecture, which imposes a burden of security (i.e., authentication) into radio access networks (RANs) in a distributed manner is considered in this paper, referred to as a local security. A base station (BS) in RANs authenticates IoT devices on its own to reduce the excessive network traffic to the MME in the local security. Meanwhile, PLA is suitable for IoT devices, due to its fast authentication with low computational complexity. So, PLA employs authentication between a BS and an IoT device to reduce burden at a BS. However, as mentioned earlier, PLA methods may have poor authentication performance under bad communication conditions (e.g., low signal-to-noise ratio (SNR) and correlated channels). Thus, a cross-layer authentication protocol that integrates PLA in RANs and cryptography-based authentication in core networks is considered. However, conventional cross-layer authentication protocols [11; 12; 13; 14] cannot provide both reliable authentication performance and a small signaling overhead for 5G networks. For example, as mentioned earlier, the authentication protocol in [11], which supplements the computational security of cryptography-based authentication by using PLA (i.e., information theoretical security), has an excessive signaling overhead and would be limited for applications. In contrary, the cross-layer authentication protocols in [12; 13; 14] have limitations in terms of authentication performance, due to uncontrollable physical features in PLA. In addition, there is no theoretical analysis for the conventional cross-layer authentication protocols in terms of the authentication performance and signaling overhead. In 5G networks, both the authentication performance and signaling overhead should be considered. Consequently, it is necessary to design a sophisticated authentication protocol with a novel integration strategy to reduce network traffic in MIoT, while guaranteeing a reasonable authentication performance under poor communication environments.
## 3 Results and Discussion
### Flood Inundation in Poyang Lake Basin During the Extreme Precipitation Event
Figure 4: Correlations between CYGNSS SR and SMOS/SMAP T\({}_{\rm bc}\) for (**a,c**) DOY 137 and (**b,d**) DOY 182 respectively, as a function of (**a,b**) soil moisture and (**c,d**) nadir vegetation opacity. Black dashed line: threshold (SR = 17 dB) for extracting flood inundation area; SR: Surface reflectivity; T\({}_{\rm bc}\): Total brightness temperature at circular polarization.
## 5 Importance of Ubiquitous Cellular Coverage in Hospitals
### Impact on Medical Technologies  
Modern hospitals increasingly rely on wireless systems to monitor patients, manage medical devices, and access electronic health records (EHRs). These technologies are indispensable for ensuring timely interventions and efficient workflows. However, their effectiveness is directly tied to the availability of reliable cellular coverage.  
**Wireless Body Area Networks (WBANs)--**WBANs are a ground-breaking innovation that enables continuous monitoring of patients through wireless sensors. These sensors transmit vital signs such as heart rate, blood pressure, and oxygen levels in real-time, allowing healthcare professionals to respond quickly to changes in a patient's condition [18; 19; 20]. For example, early detection of abnormal heart rhythms can prevent cardiac arrest. However, coverage gaps can lead to delays or interruptions in transmitting this critical data, potentially compromising patient safety and outcomes.  
**Connected Medical Devices--**Many hospital devices, such as infusion pumps, ventilators, and vital signs monitors, rely on wireless networks to operate seamlessly [21; 22]. These devices often transmit data to centralized systems for analysis and decision-making. Weak or inconsistent cellular signals can result in data transmission errors, delayed readings, or even device malfunctions. In critical areas such as intensive care units or operating rooms, such disruptions can have life-threatening consequences.  
**Electronic Health Records (EHRs)--**The digitization of medical records has transformed healthcare by enabling doctors and nurses to access patient histories, test results, and prescriptions from anywhere within the hospital. This facilitates faster decision-making and improves the quality of care [23]. However, any failure in cellular coverage can hinder access to these records, delaying treatment and increasing the risk of medical errors.  
In summary, robust cellular coverage is essential to ensure the uninterrupted operation of these technologies, which are integral to modern healthcare delivery.
## 4 Mitigating the Data Protection Risks
### Protection against Member Inference Attacks  
With respect to the risk of member inference attacks, it has been said that regularization techniques, such as dropout [58], which are used to overcome overfitting in machine learning, can help address them in some cases. Moreover, if the training process is differentially private, then the models are, in principle, secure against membership inference attacks that are solely on the outputs of the model [47]; however, it is known that differentially private models may significantly reduce the model's prediction accuracy. Another approach has been proposed in [59] based on models that depend on the causal relationship between input features and the output.
## 5 Results and Discussion
### Convergence Curves  
Figure 4 shows the convergence for three years. The results indicate that the CA converged toward more energy produced than other algorithms based on a smaller number of iterations. For example, the CA converged to 106.20 (10\({}^{8}\) kwh) after 400 iterations during normal years, while the PSO, GA, and MBF converged after 500 iterations. Thus, the CA could achieve an optimal performance goal based on a smaller number of iterations during the optimization process. The parameters for the other methods computed were based on sensitivity analysis, such as CA. For example, the population size for PSO, CA, and MBF was 80, 60, and 30. The mutation probability for the GA was 0.10, and the crossover probability for the GA was 0.5. The inertia coefficient for PSO was 0.8, and the acceleration coefficient for PSO was 2.  
Figure 4: The convergence curve for (**a**) wet years, (**b**) normal years, and (**c**) dry years.
## 3 Experiments
### Remote Sensing Investigations of Large Rivers Deltas
#### 3.5.3 The Yellow River Delta  
The Yellow River ranks second globally in sediment load, and the second-largest oilfield (Shengli Oilfield) in China is located in the Yellow River Delta. Similar to the Yangtze River Delta, the runoff and sediment load have exhibited declining trends in the first decade of this century. These trends are mainly due to water and soil conservation measures, reservoir and dam construction, increased water consumption, and inter-annual fluctuations of rainfall caused by climate change. Although the runoff and sediment load has decreased, the Yellow River Delta area and the length of the coastline in the delta have increased [435]. Ground subsidence has also occurred in the Yellow River Delta as monitored by InSAR analysis and spirit levelling [255; 256]. It is mainly caused by oil extraction, sediment consolidation, and compaction. The Yellow River Delta also experiences the highest worldwide erosion rate. By analyzing Landsat archives acquired over four decades, net erosion areas of shoreline retreat have been observed to be greater than 13 km, and net accretion areas of shoreline advance are greater than 21 km [163].
# Hybrid Encoding Scheme for AMBTC Compressed Images Using Ternary Representation Technique
## 1 Introduction
Their method divides pixels of complex blocks into three or four groups during encoding, which may improve the image quality insignificantly but requires more bits for encoding. In this paper, we propose a ternary representation technique, which uses two thresholds to classify image blocks into three types, namely flat, smooth, and complex. We use the bitmap omission technique [15] to code flat blocks. The adjusted quantized values and an index pointing to one of the representative bitmaps are used to encode the smooth blocks. The complex blocks are encoded using three quantized values and a ternary bitmap. Compared with the AMBTC and [PERSON] et al. 's work, the proposed method achieves a higher reconstructed image quality with a smaller bitrate. The reminder of this paper is organized as follows: Section 2 introduces AMBTC and [PERSON] et al. 's methods. Section 3 introduces the algorithms of this paper in detail. Section 4 presents the experimental results of the proposed method, and concluding remarks are provided in the final section.