esa-sceva/satcom-chunk-collection huggingface dataset:
# Heavy Metals in Post-Exploitation Reservoirs--The Bagry Lake Case Study (Poland)
## 6 Equilibrium between the Metal Concentration in Supernatant and in Sediment  
The coefficients in equation of adsorption and/or absorption (6) and the correlation coefficients R between the metal concentration found in the sediment \([Me_{s}]\) and calculated from the model (6) are summarized in Table 6. Figure 20a-f shows the model (6) fit to the measured metal concentration \([Me_{s}]\). The analysis of the coefficients of the model (6) listed in Table 6 shows that:  
1. Iron in the supernatant does not affect its concentration in mineral and volatile fractions of the sediment (\(\alpha_{1}=0\),\(\alpha_{2}=0\)). At a substantial iron concentration in the sediments, model (6) did not detect its accumulation from the supernatant (\(\alpha_{1}=0\), \(\alpha_{2}=0\)), or there was the maximum accumulation not dependent on the metal concentration in the supernatant. Iron is mostly accumulated in the volatile fraction, while its concentration in the mineral fraction is 2.510 g/kg d.m. (64 times lower). 2. Manganese in the supernatant does not affect its concentration in mineral and volatile fractions of the sediment (\(\alpha_{1}=0\), \(\alpha_{2}=0\)). Model (6) did not detect any accumulation from the supernatant to the sediment (\(\alpha_{1}=0\),\(\alpha_{2}=0\)), or the maximum accumulation was not dependent on the metal concentration in the supernatant. Manganese is present mainly in the volatile fraction; its concentration in the mineral fraction is 62.57 g/kg d.m. (47 times lower). 3. Copper in the supernatant correlates with copper in mineral and volatile fractions of the sediments. Model (6) showed the possibility of adsorption (general accumulation) from the supernatant to both sediment fractions. Copper is mainly present in the volatile fraction; in the mineral fraction its concentration is 9.583 mg/kg d.m. (62 times lower). 4. Lead in the supernatant does not correlate with its concentration in the mineral and volatile fractions of the sediment (\(\alpha_{1}=0\), \(\alpha_{2}=0\)), or the maximum accumulation is not dependent on the metal concentration in the supernatant. Model (6) showed the possible adsorption (general accumulation) in the sediment volatile fraction. Lead is present mainly in the volatile fraction (302.8 mg/kg d.m. v.). 5. Zinc in the supernatant correlates with zinc in the mineral and volatile fractions of the sediment. Model (6) showed the possible adsorption (general accumulation) of zinc in both sediment fractions. Zinc is present mainly in the volatile fraction; in the mineral fraction its concentration is barely 0.1176 mg/kg d.m. m (10,878 times lower). 6. Model (6) showed that cadmium can be adsorbed (general accumulation) in both sediment fractions. However, its adsorption (general accumulation) in the mineral fraction is almost negligible. Cadmium is mainly adsorbed (general accumulation) in the volatile fraction. Its concentration in the mineral fraction is close to zero, while in the volatile one it reaches 12.788 mg/kg d.m. v.
## 3 Results  
### RF Switch  
The fabricated RF switch for the beamwidth reconfiguration is shown in Figure 7. The S-parameters of the designed RF switch according to the switch state of the antenna are shown in Figure 8. The switch states of ports 2 and 3 for the RF switch correspond to the states of antennas 1 and 2, respectively (Figure 2). The SPDT mode for antenna 1 and the PD for antennas 1 and 2 are shown. The designed RF switch for the SPDT/PD provides a 10-dB impedance bandwidth (BW) of 23.34-24.60 GHz and a fractional bandwidth (FBW) of 5.3% at a center frequency of 23.9 GHz. The RF switch serves as a SPDT with the insertion losses of 1.8-2.3 and 16.7-24.2 dB and an isolation of 20.9-33.4 dB for both outputs within the 10-dB BW. The RF switch acts as a PD with an insertion loss of 3.9-4.8 dB within the 10-dB BW. The DC power consumption was 3 mW (3 V and 1 mA) when each diode was switched on. The performance of the proposed RF was compared with that of published RF switches in Table 1. The designed K-band RF switch demonstrated a good performance compared with the other switches. Moreover, the DC power consumption was drastically reduced.  
Figure 8: S\(-\)parameters of the designed RF switch: (**a**) return loss, (**b**) insertion loss, and (**c**) isolation.  
Figure 7: Picture of the fabricated RF switch.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 1 Introduction
The key motivation has been to develop the capability of detecting and identifying malware in a cost-effective manner, and in real-time so that the effects of malware can be mitigated. Figure 1: Taxonomy of Malware Analysis. Different survey papers have been written in the domain of cyber security surveying the work done in malware detection. Unlike other survey papers, our paper is not focusing on a single strategy to be reported in this literature survey, instead, we have accumulated the research trends in malware detection from various application areas of data science as well as AI. Table 1 shows the comparison between our work and other survey papers. The contributions of this work are as follows:  
* Description of malware classification and identification strategies
* Mechanisms for classifying and detecting malware and a comparative analysis between these methods
* Potential issues and challenges in the different categories of proposed solutions
* The future direction of research in this domain  
This paper is organized in the following order (Shown in Figure 2): Section 2 describes the methods used in the case of the different trends in malware detection. Section 3 presents the comparative analysis of these trends. It also discusses the issues and challenges faced in each trend. Section 4 highlights future trends in the domain of malware identification and classification. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Coverage** & **Other Papers** & **Our Survey Paper** \\ \hline Survey of statistical based methods for malware & [13,14] & \(\surd\) \\ detection & [15] & \(\surd\) \\ \hline Survey of machine learning based algorithms for & [15] & \(\surd\) \\ malware detection & [13,16] & \(\surd\) \\ \hline Survey of deep learning based techniques to detect & [13,16,17] & \(\surd\) \\ malware & [18,18] & \(\surd\) \\ \hline Analysis of problems associated with statistical based & [18] & \(\surd\) \\ approaches of detecting malware & [15] & \(\surd\) \\ \hline Analysis of shortcomings of machine learning based & [15] & \(\surd\) \\ solutions for detecting malware & & \\ \hline Analysis of disadvantages of using deep learning & [13,16] & \(\surd\) \\ based methods to detect malware & [13,16] & \(\surd\) \\ \hline Survey of FSL methods in the domain of malware & & \(\surd\) \\ detection & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Related survey Papers on Malware Detection Approaches. Figure 2: Organization of Paper.
## 5 Experimental Evaluation
### Application-Level Latency Results  
In this section, we experimentally evaluate the measured application-level latency of the ACCA service in order to demonstrate the effectiveness of interoperability in V2X communications using OBUs from different vehicle OEMs. The application-level latency is defined as the elapsed time since a DENM is transmitted by an OBU, or by the TMS, until the reception of that DENM by another OBU.  
We have measured the application-level latency in two different scenarios based on the ACCA service architecture that is presented in Figure 1. In the first scenario, a road hazard is detected by one vehicle, and its OBU transmits a DENM message to the edge cloud application. In the second scenario, a road hazard is identified by the TMS, and it subsequently transmits a DENM message towards the edge cloud applications. It is worth noting that the TMS communicates with other traffic information sources outside the ACCA service; therefore, the TMS is capable of providing alerts on roadside hazardous events that are relevant to the connected vehicles within the ACCA service. Sections 5.3.1 and 5.3.2, respectively, present the results for application-level latencies in both scenarios.
# Review of Indoor Positioning: Radio Wave Technology
## 2 Parameter Based Positioning
### Rtt  
Wi-Fi-based two-way ranging approaches have been proposed for indoor positioning and tracking systems to improve the positioning accuracy. These positioning systems are based on fine time measurement (FTM) of the RTT of a signal between a smartphone (target) and an access point. The RTT or RTOF technique estimates the distance by the broadcast timestamp of the FTM message and the response of its acknowledgement [63] in Figure 5. This measurement approach is based on the TOF and develops to solve the synchronization problem subjected to the use of TOA. The RTT measurement does not need the clock synchronization between the nodes. This means less complexity and high reliability. In addition, the ranging error and range between a couple of devices are nearly independent when the clock operates at the same rate on the nodes. The FTM can give a large range estimation and a large update rate compared to the scene analysis system. However, the RTT ranging measurement has limitations with respect to its reflection, fading, shadowing, and unstable clock speed due to phase noise as well as a different processing time delay. Moreover, the FTM protocol has a concurrent processing capacity problem and an access point cannot concurrently reply to higher amounts of FTM inquiries [64].  
One approach is the Wi-Fi RTT-based indoor positioning system in car parks [65]. The proposed system used a trilateration method and a probabilistic method to estimate the car's location. The result of this system shows that the Wi-Fi RTT is suitable for industrial indoor positioning in a dynamic environment. This system achieves an average accuracy of 2.33 m and the accuracy can be improved with higher radio communication or a larger number of access points. Another approach is a hybrid algorithm based on the RTT and RSS, which was exploited to solve the restrictions of the Wi-Fi RTT ranging technique [64].  
This approach presents the RTT estimation with a clock skew and investigates the RTT range error distribution. It also removes the RTT ranging offset at the emitter end by using the calibration method. The proposed system achieves scalability and accuracy in static and dynamic experiments in both the outside and inside environment. The average location accuracy of this work is 1.435 m and an update rate is 0.19 s in a real environment. Although the RTT-based indoor positioning system can achieve standard deviations of 1-2 m, in some applications, for example, an emergency worker in a multi-story building, this can impact the position error due to the signal bandwidth, the delay of the signal, and the noise gain. Therefore, the frequency diversity method was introduced for the accurate position estimation using weighted averages of evaluations with uncorrelated errors acquired in various networks [66].  
Figure 5: RTT with a skewed clock.
# State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise
## 2 Optimal Feature Selection  
The selection of input features can significantly impact a model's training time and prediction accuracy. Thus, choosing the appropriate input features to optimize the algorithm is crucial. In this study, we utilize a random forest (RF) algorithm to improve the electrical characteristics of batteries during discharge.  
RF consists of different decision trees [25], where each tree selects a feature as the splitting feature for a node. The importance of each feature on the prediction accuracy can be estimated by the out-of-bag error rate [26; 27]. In particular, we employ random forest to predict the state of charge (SOC) and select the following features as candidates: voltage, current, temperature, voltage change rate (\(dV/dt\)), and current change rate (\(dl/dt\)). Thesefeatures are used to construct the feature matrix, \(F=[f_{1},f_{2},\ldots,f_{N}]\). The feature \(f_{i}\) scoring function is shown in Equation (1):  
\[F_{i}=\frac{e_{T}}{e_{i}} \tag{1}\]  
where \(e_{i}\) is the out-of-bag error after random rearrangement of the feature \(f_{i}\), and \(e_{T}\) is the sampling error in the decision tree \(T\). Figure 1 displays the results, which show that the voltage, current, and ambient temperature are influential factors for predicting the SOC. In contrast, for predicting the rates of \(dV/dt\) and \(dl/dt\), only factors of 0.765 and 0.382, respectively, are influential. Although these influencing factors are small, the data acquisition system can directly obtain the battery's working voltage, current, and temperature in practical applications. These electrical characteristics are chosen as input features for the model.
###### Abstract  
Streptomycin is used primarily to treat bacterial infections, including brucellosis, plague, and tuberculosis. Streptomycin resistance easily develops in numerous bacteria through the inhibition of antibiotic transfer, the production of aminoglycos-modifying enzymes, or mutations in ribosomal components with clinical doses of streptomycin treatment. (1) Background: A transposable insertion sequence is one of the mutation agents in bacterial genomes under oxidative stress. (2) Methods: In the radiation-resistant bacterium _Deinococcus geothermalis_ subjected to chronic oxidative stress induced by 20 mM hydrogen peroxide, active transposition of an insertion sequence element and several point mutations in three streptomycin resistance (SmR)-related genes (_rsmC, rpsL,_ and _mthA_) were identified. (3) Results: ISDge6 of the IS5 family integrated into the _rsmG_ gene (_dgeo_2335_), called _SrsmG_, encodes a ribosomal guanosine methyltransferase resulting in streptomycin resistance. In the case of _dgeo_2840-disrupted mutant strains (S1 and S2), growth inhibition under antibiotic-free conditions was recovered with increased growth yields in the presence of 50 ug/mL streptomycin due to a streptomycin-dependent (SmD) mutation. These mutants have a predicted proline-to-leucine substitution at the 91 st residue of ribosomal protein S12 in the decoding center. (4) Conclusions: Our findings show that the active transposition of a unique IS element under oxidative stress conditions conferred antibiotic resistance through the disruption of _rsmG_. Furthermore, chronic oxidative stress induced by hydrogen peroxide also induced streptomycin resistance caused by point and frameshift mutations of streptomycin-interacting residues such as K43, K88, and P91 in RpsL and four genes for streptomycin resistance.  
Deinococcus geothermalis; chronic oxidative stress; insertion sequence; phenotypic variation; ribosomal protein S12 (RpsL); streptomycin-dependent (SmD) and -resistant (SmR); transposition +
Footnote †: journal: Molecular Sciences
# Genetic Engineering Approaches for the Microbial Production of Vanillin
## 4 Genetic Engineering Approaches
### Genetic Engineering in Model Microorganisms
#### 4.3.1 Implementation of Nature-Inspired Bioconversion Pathways Vanillin from Phenylpropanoids
Such an engineered strain was cultivated under the required inducing conditions and with the addition of phenylalanine as a precursor, which led to 4-coumaric acid, 4-hydroxybenzoic acid, PCA, and vanillic acid being detected in the culture supernatants, but the corresponding aldehydes were not detected [97]. To deal with the undesired oxidation of vanillin and its intermediates (aldehydes) into the corresponding acids, the inventors proposed the replacement of the _ADH6_ gene by _car_ and _entD_ genes from _Nocardiia_. These two genes encode an aryl aldehyde carboxylic acid reductase (ACAR, catalysing the reduction of vanillic acid into vanillin) and a phosphopantetheinyl transferase (PPTase, responsible for the phosphopantetheinylation of ACAR). The ability of the yeast strain (YOCPVAN) harbouring these heterologous genes to produce vanillin when fed with phenylalanine was not demonstrated [97]. A patent application from Rhodia operations [102] later confirmed the involvement of the host strain endogenous enzyme(s) for the conversion of 4-hydroxybenzaldehyde into 4-hydroxybenzoic acid and described that the engineered strain could produce only 1 \(\upmu\)M of vanillin from endogenous phenylalanine. Figure 10: **Vanillin production from L-phenylalanine by a benzoate biosynthetic pathway. ** (**A**) Schematic representation of the biosynthetic pathway and of the constructions used to implement it. Different transcriptional terminators are represented by different colors. (**B**) Table summarising the function and origin of the genes/enzymes used.
## 5 Discussion
### Practical Implications for Crisis Response
#### 5.3.3 Computational Efficiency for Low-End Equipment  
To address high computational costs and limited access to high-end equipment, the framework includes optimizations for low-tier hardware:  
1. Optimized inference: Mixed-precision training (FP16) and dynamic graph optimization (Section 4.6) reduce computational load by 50% and eliminate 31% of redundant operations, enabling reliable performance on entry-level GPUs (e.g., NVIDIA MX550) and standard CPUs.
2. Distributed training: Via PyTorch's Distributed Data Parallel, multiple low-end devices (e.g., 4\(\times\) entry-level GPUs like NVIDIA MX550) aggregate to match mid-tier GPU efficiency--reducing reliance on expensive hardware for both inference and training. For resource-constrained organizations, aggregating 4-6 such GPUs achieves training efficiency comparable to a single RTX 3060 when fine-tuning on crisis-specific datasets, avoiding dependence on high-end clusters (rarely accessible in remote disaster zones). Complemented by transfer learning with pre-trained backbones (e.g., CLIP), fine-tuning requires 60% fewer steps than training from scratch, further lowering hardware demands for model updates.
3. Lightweight modules: Building on modular deployment (Section 5.3.2), CPU-friendly functionalities (e.g., text-only classification) minimize computational demands, ensuring usability for resource-constrained users. These strategies ensure the framework remains effective and cost-efficient for users without high-end equipment.
## 4 Materials and Methods
### Experimental Dataset and Setup  
To create the dataset for hate speech analysis, a structured approach was followed. Initially, relevant keywords representing different types of hate speech, such as religious, political and social hate speech, were identified using various sources, including specialized databases including HATEBASE [50] and an Arabic lexicon [30]. [PERSON] et al. [51] underscore the critical role of corpus design and evaluation in Arabic hate speech detection, which aligns with our focus on constructing a dialectically balanced dataset. These keywords were then used to extract tweets from the social media platform X, using the X API integrated with the Tweepy library. After retrieving approximately 9700 tweets, a filtering process removed duplicates and irrelevant entries, resulting in 8341 tweets for analysis. Preprocessing involved cleansing, tokenization and normalization using tools such as PyArabic for Arabic text processing and NLTK for further text analysis. These steps ensured a high quality dataset that was suitable for annotation and subsequent classification. These steps produced an accurate dataset that was required for annotation and further classification. The preprocessing techniques were based on literature review on text pre-processing and feature extraction to use in modeling hate speech in the social media. Studies such as [18; 40; 44; 45] show that there is lot of efficaciousness in diacritic stripping, stemming and tokenization, for example, while handling the challenges of Arabic language in computational processes. This radically minimizes the noise, density and superiority representation of features or configurations in the dataset that feeds machine learning algorithms.  
Following the dataset creation and preprocessing, the annotation phase was critical for preparing the data for supervised machine learning. This process involved two key tasks. Initially, tweets were annotated as either \"hate speech\" or \"not hate speech\", providing a binary classification. Tweets identified as hate speech were further categorized into one of three subtypes. To ensure quality and reliability, three native Arabic-speaking annotators were selected, based on their expertise and willingness to participate. Clear annotation guidelines were provided and disagreements in the binary classification were resolved by majority voting. The average text length of tweets in the dataset was approximately 22 words, and inter-annotator agreement was validated with a Fleiss' kappa score of 0.78, indicating substantial agreement. For the subcategories, tweets with complete disagreement among annotators were excluded, resulting in a final dataset of 7923 tweets, as shown in Figure 3. The reliability of these annotations was evaluated using Fleiss' kappa.  
The experiments were conducted on macOS Monterey for initial tests and on Google Colab Pro with an A100 GPU for efficient model training. Python 3 was used in both setups to ensure smooth implementation and faster processing.
# Simulation of Calibrated Complex Synthetic Population Data with XGBoost
## Appendix B Details on the Simulated Annealing Algorithm for the Calibration of Populations  
_Appendix B.1. Detailed Calibration Algorithm_  
Algorithm A1 presents the Simulated Annealing algorithm in more detail.  
```
1:Duplicate the synthetic population \(K\) times to include \(N^{\prime}=N\cdot K\) individuals, increasing the likelihood of finding a composition \((\delta_{1},\ldots,\delta_{N})\).
2:Set the initial conditions: \(\bullet\) Start temperature \(T>0\) \(\bullet\) Cooling rate \(r_{T}\in(0,1)\) \(\bullet\) Minimum temperature \(T_{min}\) \(\bullet\) Maximum number of cooldowns \(C>100\) \(\bullet\) Counter for number of cooldowns \(N_{C}=1\) \(\bullet\) Initial number of selections/deselections \(n=Obj:\frac{\text{med}_{base}}{5}\) \(\bullet\) Reduction rate for number of swaps \(r_{n}\in(0,1)\) \(\bullet\) Allowed relative error \(\epsilon>0\) \(\bullet\) Number of iterations until cooling \(c\) \(\bullet\) Counter for iterations until next cooldown \(n_{c}=1\)
3:Initialize a first selection \(\Delta=(\delta_{1},\ldots,\delta_{N^{\prime}})\), calculate initial synthetic margins \(\mathbf{m}\), and the value of the objective function: \[Obj=f(\mathbf{t},\mathbf{\hat{t}})=\sum_{m=1}^{M}|t_{m}-\hat{t}_{m}|\]
4:Randomly add and remove \(n\) persons, including all household members, from the selection \(\Delta\) by setting \(\delta_{i}=0\) or \(\delta_{i}=1\) for each member of the drawn household, resulting in a new selection \(\Delta_{new}\).
5:Calculate the new value of the objective function \(Obj_{new}\) using \(\Delta_{new}\).
6:if\(\frac{Obj_{new}}{N}\leq\epsilon\)then
7:Convergence reached, terminate the algorithm and return \(\Delta_{new}\).
8:endif
9:if\(Obj_{new}<Obj\)then
10: Update \(\Delta\leftarrow\Delta_{new}\).
11:else
12: Update current selection \(\Delta\) with new selection \(\Delta_{new}\) if \(u<\exp\Bigl{(}-\frac{Obj_{new}-Obj}{T}\Bigr{)}\) where \(u\) is drawn from \(U(0,1)\); otherwise, keep \(\Delta\).
13:endif
14:Increment \(n_{c}\); if \(n_{c}>c\), start the next cooldown, otherwise, repeat from step 4: \(\bullet\)\(n\leftarrow\max(1,\lfloor n\cdot r_{n}\rfloor)\) \(\bullet\)\(T\gets T\cdot r_{T}\) \(\bullet\)\(N_{C}\gets N_{C}+1\) \(\bullet\)\(n_{c}\gets 1\)
15:if\(N_{C}>C\) or \(T<T_{min}\)then
16: Terminate the algorithm and return current selection \(\Delta\).
17:else
18: Go to step 4.
19:endif
```  
**Algorithm A1** Detailed calibration algorithm