esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Model Poisoning Attack on Federated Learning
## 2 Background and Related Works
### Attacks against Machine Learning  
A machine learning system can be viewed as a generalized data processing pipeline. A primitive sequence of in implementing a machine learning system can be viewed as: (1) data processing: including collection, tagging label and transformation of the input data, (2) model training: including model training and fine-tuning the trained model, (3) model evaluation: including model testing, and (4) system deployment: including model deployment and model prediction. Typically, it exhibits to the public an overall process including: (1) data processing, where the adversary may put elaborate poisoning samples to the training data leading to poisoning attack, (2) model training, where it converts a large volume of data into a trained model and the adversary may influence the training procedure, and (3) system deployment, where the model can be used for prediction, as per input data, and the adversary may launch the evasion attack in this stage. Prediction tasks are widely used in different fields, e.g., image classification, speech recognition, natural language processing, and malware detection are all pertinent applications for deep learning. The attack surface, in this case, can be defined with respect to the data processing pipeline. An adversary can attempt to manipulate either the collection or the processing of data to corrupt the target model, thus tampering the original output. The main attack scenarios identified by the attack surface are sketched below:  
* [noitemsep,topsep=0 pt]
* Evasion Attack: this is the most common type of attack in the adversarial setting. The adversary tries to evade the system by adjusting malicious samples during testing phase. This setting does not assume any influence over the training data.
* Poisoning Attack: this type of attack, which is known as contamination of the training data, takes place during the training time of the machine learning model. An adversary  
Figure 1: Flowchart of centralized learning and federated learning framework. (**a**) Clients upload local dataset to a trust central server, (**b**) while, in the federated learning system, clients keep their private data locally.  
tries to poison the training data by injecting carefully designed samples to compromise the whole learning process eventually.
* Exploratory Attack: these attacks do not influence training dataset. Given black box access to the model, they try to gain as much knowledge as possible about the learning algorithm of the underlying system and pattern in training data. The definition of a threat model depends on the information that the adversary has at their disposal.
# Lignocellulosic Biomass as a Substrate for Oleaginous Microorganisms: A Review
## 2 Oleaginous microorganisms  
### Important Oleaginous Species  
The microorganisms characterized as oleaginous share the common feature of being able to accumulate chemical energy in the form of lipids. The metabolism of these organisms is adapted to convert under specific growth conditions the carbon substrate into storage lipids which are stored in the cytoplasm as lipid bodies with preserved structures in prokaryotes and eukaryotes [25]. Representative oleaginous cells containing lipid bodies are shown in Figure 1. In eukaryotes, and rarely in prokaryotes, lipid bodies consist of neutral lipids, mainly triacylglycerlycerols (TAGs) [26].  
Oleaginous microorganisms can be found among species of microalgae, fungi (filamentous and yeasts), bacteria and protists. microalgae strains associated with _Chloella, Scenedesmus, Chlamydomonas, Nanochloropsis, Chlorococcus, Isochrysis, Cylindrical, Tetraselntis, Auxenochlorella, Botryococcus_ species are referenced to have excellent lipid production ability [6; 27; 28; 29; 30; 31; 32; 33]. Among bacteria, the known species are _Rhodococcus_, Streptomyces, _Nocardi, Mycobacterium, Dietzia_ or _Gordonia_[34; 35; 36; 37]. Representative oleaginous species of filamentous fungi and yeasts belong to _Umblepsis (Mortierella), Microspharopsis, Fusarium, Candida, Meyerozyma, Rhodotorula, Rhodosporidium, Pichia, Cryptoccus, Lipomyces, Trichospor and Yarrowia_[20; 26; 33; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57].  
The microbial consortia of microalgae and bacteria [58; 59; 60; 61] or microalgae and yeast species [62; 63; 64] have been reported as a strategy to increase lipid production [23; 62]. Although studies on consortia have shown that synergism may occur between different species, allowing increased accumulation of  
Figure 1: Lipid droplets visualized under light microscope (400\(\times\)) within yeast cells and the mycelium of a filamentous fungus as shown through fluorescent filter after Nile red staining (**A–D**) and without fluorescence filter (**E–H**). (**A,E**) _Meyerozyma guilliermondii_, (**B,F**) _Scheffersomyces coipomensis_, (**C,G**) _Umbleopsis (Mortierella) isabellina_, (**D,H**) _Yarrowia lipolytica_ (Original pictures).  
lipids, further studies are needed to determine the individual contribution of each partner to lipid production process. Since the physiology of oleaginous microorganisms varies among species [65; 66; 67; 68], the ability to accumulate lipids is affected by different factors that are species specific.
## 3 Results and Discussion
### Adsorption Isotherm
The linear form of the D-R adsorption isotherm is described by the following Equation (7):  
\[\rm{lnq_{e}=lnq_{s}-B\varepsilon^{2}} \tag{7}\]  
where qs (mg\(\cdot\)g\({}^{-1}\)) represents the saturation capacity of adsorption. Figure 5d shows the D-R isotherm graph, obtained by plotting lnq\({}_{\rm{e}}\) vs. \(\varepsilon^{2}\), where \(\varepsilon\) is polyanyi potential, which can be calculated according to Equation (8):  
\[\varepsilon=\rm{RTlog(1+\frac{1}{C_{e}})} \tag{8}\]  
where R (J\(\cdot\)mol\({}^{-1}\)K\({}^{-1}\)) is the general gas constant, C\({}_{e}\) (mg\(\cdot\)L\({}^{-1}\)) refers to the equilibrium concentration, and T is the Kelvin scale temperature. Energy of adsorption can be calculated by Equation (9) as expressed below:  
\[\rm{E_{ads}=\frac{1}{\sqrt{(1-2B)}}} \tag{9}\]  
Using Equations (7) and (9), values of qs and E\({}_{ads}\) for Cr(VI) and Ni(II) adsorbed on Fe\({}_{3}\)O\({}_{4}\), PANI, and PANI/Fe\({}_{3}\)O\({}_{4}\) composite were calculated which indicated physical adsorption (Table 1). Similar results were shown by the already reported work which suggested that if the values of E\({}_{ads}\) are less than 40 kJ\(\cdot\)mol\({}^{-1}\), the adsorption is physical, but values higher than 40 kJ\(\cdot\)mol\({}^{-1}\) suggest chemical adsorption [67]. Moreover, the typical range of ion-exchange process is 8-16 kJ\(\cdot\)mol\({}^{-1}\)[60]. In the present study, the E\({}_{ads}\) value for adsorption of both Cr(VI) and Ni(II) were not in this range, which indicate that the ion-exchange process was not involved. The Elovich isotherm linear form is represented as follow:  
\[\rm{ln\ \frac{q_{e}}{C_{e}}=ln(k_{E}q_{max})\ -\frac{1}{q_{max}}q_{e}} \tag{10}\]  
where q\({}_{max}\) is the maximum capacity of adsorption, K\({}_{E}\) (L\(\cdot\)mg\({}^{-1}\)) refers to the Elovich constant and q\({}_{e}\) is the amount of heavy metals adsorbed at equilibrium. From the slope and intercept of Figure 5e, values of q\({}_{max}\) and K\({}_{E}\) can be calculate (Table 1) [68]. R\({}^{2}\) values show that the Freundlich isotherm fit more closely than all other isotherms to the adsorption data.
# The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis
## 3 Results
For each group, we calculated the percentage of events exceeding varying \(\Delta H\) thresholds and evaluated the corresponding average SNR values with 95% confidence intervals. The results reveal that larger-magnitude events tend to produce higher \(\Delta H\) values and SNRs, increasing the detection confidence, Figure 11. This stratified analysis confirms that the entropy-based thresholding method is sensitive to event strength and can maintain a consistent performance. Figure 11: Threshold statistics across magnitude bins: M < 4.0 (**a**), 4.0–4.5 (**b**), 4.5–5.0 (**c**) and M > 5.0 (**d**). (Left axis) The percentage of events exceeding the spectral entropy threshold \(\Delta H\). (Right axis) The corresponding average signal-to-noise ratio (SNR) with 95% confidence intervals for events exceeding each \(\Delta H\) threshold. The plots illustrate that higher-magnitude events more reliably exceed the entropy thresholds and exhibit higher SNRs, confirming the robustness of the detection strategy. Based on our findings, we propose a criterion for pinpointing the onset of a seismic event. First, the baseline (pre-event) spectral entropy should be below 0.45, and the growth in normalized spectral entropy should be at least 0.2 (with a mean value \(\gtrsim\) 0.1) for the method to be reliably fitting. Under these conditions, the technique yields a definitive detection result for 65% of the dataset. If the minimum (initial) entropy exceeds this threshold, the detection criterion should instead follow a linear relationship, as described by the fitted equation. In this case, consistent detection results are observed for 87% of the dataset, with a standard deviation within 1 sigma.
# MS Location Estimation Based on the Artificial Bee Colony Algorithm
## 6 Conclusions  
In this paper, a novel ABC-based location algorithm was presented to estimate the MS location in the NLOS environment. The objective function of the proposed location algorithm is according to the geometrical relationships between MS and three BSs. It can be observed that the NLOS interference can be ameliorated when the sum of the distance from MS to three feasible intersections is minimized. Therefore, the problem of MS location estimation can be regarded as a constrained optimization problem. The ABC algorithm was applied to search for the MS location in this constrained optimization problem. The ABC algorithm is a swarm intelligence algorithm, based on the behavior observed from the food source searching procedure of the honey bee. Three kinds of bees search for the best food source in the searching range, which overlap the three circles. During cycles, the value of the objective function is decreased gradually. Lastly, the optimum solution, which equals the estimated MS location, could be obtained by the ABC algorithm without any NLOS error prior information. The simulation results of the location error show that the positioning accuracy of the proposed ABC-based location algorithm is better than LLOP, TSA, RSA, and GA. Compared to GA, the ABC-based algorithm is more accurate and efficient, converges faster, and is capable of handling complex optimization problems. Another important advantage is that the ABC algorithm is relatively simpler compared to other well-known metaheuristic algorithms like GA, as only two parameters (colony size and limit) are needed in the ABC algorithm. In this paper, the proposed ABC-based location algorithm provides the estimated MS location not only with best location accuracy but also the shortest execution time.  
Conceptualization, C.-S.C. and K.-S.C.; methodology, J.-F.H. and N.-C.H.; software, N.-C.H.; validation, C.-S.C. and J.-F.H.; formal analysis, N.-F.H.; investigation, K.-S.C.; resources, C.-S.C. and J.-F.H.; writing--original draft preparation, C.-S.C. and K.-S.C.; writing--review and editing, K.-S.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Performance Evaluation and Discussion
During the real-time experiment, a random sample of data is presented to explain the behavior of a cluster with a single hop is shared in Table 2. The number of packets sent by each sensor within a total number of rounds is shown in Figure 9. More than 20,000 attempts are observed when non-cluster heads perform no transmission due to the threshold conditions. The nodes' behavior in a cluster containing eight alive non-cluster head nodes is shown in Table 2. The second, sixth, and seventh nodes broadcast values from the humidity sensor while other nodes transmit updated values from the temperature sensor. The first, fourth, sixth, and eighth nodes do not send updated information to the CH as similar information was sent in the recent rounds. However, these nodes update their counters. All other nodes broadcast the newly sensed values to the CH and reset their counters. The third node broadcast data due to the hard threshold condition that exceeds the accepting range of values. The remaining second, fifth, and seventh nodes transmit newly sensed values due to soft threshold conditions that demonstrate the system's capabilities of managing quick or unpredictable changes in the environment. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network size & 100 m \(\times\) 100 m \\ BS position & 50 m, 50 m \\ Total nodes \(N\) & 100 \\ First level nodes \(m\) & 30 \\ Second level nodes \(n\) & 70 \\ Normal nodes in \(n\) (80\%) & 56 \\ Advanced nodes in \(n\) (20\%) & 14 \\ \(m\) nodes/\(n\) nodes & 30/70 \\ Initial energy normal nodes & 0.5 J \\ Initial energy advanced nodes & 0.75 J \\ \(\alpha\) & 0.5 \\ Desired percentage of CHs & 0.1 \\ Data aggregation energy cost & 50 pJ/bit J \\ Packet size & 4000 bit \\ \(T_{hard}/T_{soft}\) & 20C–35C/2C \\ \(H_{hard}/H_{soft}\) & 64\%–80\%/3\% \\ EelecRx, EelectTx & 50 nJ/bit \\ Transmit amplifier (Eamp) & 100 pJ/bit/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Node** & \(\mathbf{RND}\) & **Active Sensor** & \(\mathbf{T_{old}\Rightarrow T_{new}}\) & \(\mathbf{H_{old}/H_{new}}\) & **Counter** & **Transmit?** \\ \hline
1 & 1 & T & 27 \(\Rightarrow\) 28 & 67 \(\Rightarrow\) - & 2 \(\Rightarrow\) 3 & No \\
2 & 0 & H & 23 \(\Rightarrow\) - & 64 \(\Rightarrow\) 67 & 3 \(\Rightarrow\) 0 & Yes \\
3 & 1 & T & 34 \(\Rightarrow\) 36 & 66 \(\Rightarrow\) - & 2 \(\Rightarrow\) 0 & Yes \\
4 & 1 & T & 30 \(\Rightarrow\) 31 & 68 \(\Rightarrow\) - & 1 \(\Rightarrow\) 2 & No \\
5 & 1 & T & 25 \(\Rightarrow\) 27 & 64 \(\Rightarrow\) - & 2 \(\Rightarrow\) 0 & Yes \\
6 & 0 & H & 30 \(\Rightarrow\) - & 75 \(\Rightarrow\) 74 & 1 \(\Rightarrow\) 2 & No \\
7 & 0 & H & 28 \(\Rightarrow\) - & 68 \(\Rightarrow\) 70 & 1 \(\Rightarrow\) 0 & Yes \\
8 & 1 & T & 29 \(\Rightarrow\) 30 & 70 \(\Rightarrow\) - & 0 \(\Rightarrow\) 1 & No \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Temperature (T) and humidity (H) from 09:00 to 10:00.
## 3 Simulation and Results
### Simulation Results on Unimodal Test Function \(F_{1}\) to \(F_{7}\)  
Seven objective functions F\({}_{1}\) to F\({}_{7}\) were selected as unimodal objective functions to evaluate the performance of the GBGBO and other algorithms. Complete information on these objective functions is given in Table 1 in Appendix A. These objective functions had only one optimal solution and were therefore suitable for evaluating the exploitation power of optimization algorithms. The results of the optimization of these objective functions after twenty independent implementations are presented in Table 2. The optimization results of these objective functions showed that the proposed GBGBO offered better solutions than other algorithms. This indicates that the GBGBO had a high exploitation power in achieving the optimal solution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Parameter** & **Value** \\ \hline GA & & Real coded \\  & Type & Roulette wheel (Proportionate) \\  & Crossover & Whole arithmetic (Probability = 0.8, \\  & Mutation & \(\alpha=[-0.5,\,1.5]\)) \\ \hline PSO & & \\  & Topology & Fully connected \\  & Cognitive and social constant & (\(C_{1},\,C_{2}\)) 2, 2 \\  & Inertia weight & Linear reduction from 0.9 to 0.1 \\  & Velocity limit & 10\% of dimension range \\ \hline GSA & & \\  & Alpha, \(G_{0},\,R_{warm},\,R_{power}\) & 20, 100, 2, 1 \\ \hline TLBO & & \\  & \(T_{F}\): teaching factor & \(T_{F}\) = round \([(1+rand)]\) \\  & random number & \(rand\) is a random number in \([0,\,1]\). \\ \hline GWO & & \\  & Convergence parameter (\(a\)) & \(a\): Linear reduction from 2 to 0. \\ \hline WOA & & \\  & Convergence parameter (\(a\)) & \(a\): Linear reduction from 2 to 0. \\  & \(r\) is a random vector in \([0,1]\). & \\  & \(l\) is a random number in \([-1,1]\). & \\ \hline TSA & & \\  & \(P_{\min}\) and \(P_{\max}\) & 1, 4 \\  & \(c1,\,c2,\,c3\) & Random numbers lie in the range from 0 to 1. \\ \hline MPA & & \\  & Constant number & \(p=0.5\) \\  & Random vector & \(R\) is a vector of uniform random numbers in \([0,\,1]\). \\  & Fish Aggregating Devices (FADs) & \(FADS=0.2\) \\  & Binary vector & \(U=0\) or 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values for the comparative algorithms.
## 4 Experiment and Results
### Connection Scenario 2: One Anchor  
In connection scenario 2, three anchor nodes and three target nodes are set up, with each target node connecting to one anchor node, and the target nodes connecting to each other. Figure 5 depicts the location distribution (a), communication relationship between nodes (a), positioning error of various algorithms (b-e), and overall positioning error to constrast (f). As the diagram depicts, SBPVEM can obtain accurate locate results equivalent to SPAWN and NBP. Nevertheless, PVSPA can hardly reduce the position error.  
Figure 4: Location results of connection scenario 1. (**a**) exhibits the setting of scenario 1. Anchors are represented by cyan nodes, whereas yellow nodes represent targets. Blue dotted lines link two nodes with communication. PVSPA, SPAWN, NBP, and SBPVEM iterative positioning results and corresponding positioning errors are shown in (**b–e**). (**f**) comparing the prediction errors of various algorithms in the iterative process.
# Efficiency Comparison of AC and DC Distribution Networks for Modern Residential Localities
## 2 Comparison with Previous Research  
DC distribution has been the subject of [26; 27], but their work lacks comparison of AC and DC at the distribution level. However, the theme of current research is on the comparison of AC and DC distribution systems. The authors of [28] have focused on commercial buildings with DC loads and assumed DC as an available medium of power, where the conversion stages and associated losses were ignored. This research is solely focusing on residential localities and highlighting the effect of converter efficiencies on residential distribution networks. The work presented in [29] assumes that all loads in residence are inherently DC. In contrast to the prior-art research, this paper offers work based on practical load data, containing both AC and DC loads that exist in a typical residential building. [8; 30] are two of our earlier studies on DC power distribution. In [30], we presented the efficiency analysis of DC distribution system but did not carry out its comparison with AC distribution system. Furthermore, the power consumed by loads was arbitrarily divided into three-time zones constituting a day. However, in the current work, a typical hourly consumption of loads is utilized to derive a comparison of AC and DC distribution systems. In [8], we took variable converter efficiencies without considering load variation, whereas the theme of the current effort is based on changing efficiencies of converters with loading. The authors of [23] presented the comparison of both distribution schemes with quite high values of converter efficiencies (up to 99.5%); the authors themselves claimed that such high-value converters are quite rare. A time-based study is missing in [31], where the authors have presented DC distribution, its efficiency analysis and comparison with AC counterpart with certain assumptions. In addition to the comparison of AC and DC distribution networks considering daily load variation based on actual data, this research effort features the study of the effect of solar irradiance variations throughout the day on DC and AC distribution systems. In the past, studies have been presented with solar PVs in the DC distribution due to the shifting trend towards renewable resources, i.e., [13; 28]. However, the authors of both research efforts in [13; 28] did not compare AC and DC distribution at the residential level considering hourly load as well as solar irradiance variations.
## 3 Methodology
### Predictive Optimal Control Mechanism of Indoor Temperature  
The architecture and configuration of the system consists of the main-part and the sub-part, which are remotely connected using Modbus TCP/IP-based communication protocols. The main-part runs AI algorithms to supervise the overall operation of the system, while the sub-part collects sensor data on temperature and humidity and automatically adjusts room temperature by controlling the heater and air conditioner via a power relay. In addition, the sub-part implements a hysteresis control mechanism to ensure stable operation of the heating and cooling devices. In this experiment, a Raspberry Pi 4B served as the controller of the sub-part and also functioned as a simple experimental server to facilitate comparative experiments, including real-time sensor data monitoring. Figure 2 shows the configuration of the entire system proposed in this paper.  
The NVIDIA Jetson Orin Nano 8 GB serves as the AI Main-part in the system and acts as a central controller via Modbus TCP. It is a compact, high-performance embedded platform designed to support efficient AI computing tasks, featuring NVIDIA Ampere architecture-based GPUs, and is ideal for environments that require simple yet effective AI-based decision-making, optimized for real-time inference and deep learning model training. The processor integrates a 6-core ARM Cortex-A78 AE CPU that supports multi-threading tasks and real-time data processing, along with 8 GB of LPDDR5 memory for managing datasets. It also offers expandable storage options for long-term data storage, including a 500 GB SSD installed for this purpose. The Jetson Orin Nano provides various I/O ports, including USB, GPIO, I\({}^{2}\)C, and SPI, to facilitate smooth communication with external sensors and control devices. While in sub-part, the physical sensors and actuators such as the heater and air conditioner are connected and are controlled by a Raspberry Pi microcontroller, which handles the physical control by receiving sensor inputs.  
Figure 1: (**a**). The 24 h measured outdoor temperature data sourced from the Korea Meteorological Administration Open Weather Data Portal in Cheonan City, South Korea. (**b**). Average (indoor) room temperature simulation data of a South Korean household for 24 h on 31 December 2023.