esa-sceva/satcom-chunk-collection huggingface dataset:
# Defacement Detection with Passive Adversaries
## 5 Results: Experiments, Full-Scale Deployment and Discussion
### Production Environment  
#### 5.3.1 Setup and Context  
The production environment targets a single Web Site of a large company in the industrial sector, where about 260 active URLs are being monitored by the system, as part of an outsourced security maintenance contract.  
The activity started in a moment when the company was changing its Web Site completely, including graphics, CMS and content. Our defacement monitoring was started on the development environment for the new Web Site, and continued in the early stages of its production deployment--it is currently still active as the company has decided to use it as an additional and ongoing security control. This context implies that:  
* The test is interesting, as there has been significant content changing activity in the early stages of the transition to the new Web Site, potentially causing a large number of false alarms (false alarm rate--FAR)
* This is a real-world test, but it cannot be used to measure the percentage of defacements that were not recognized (undetected defacement rate--UDR): in fact, as one would expect and hope, no real defacement ever occurred during the reference period. To evaluate UDR, we have then used the experiment in the simulated, testing environment described in the previous subsection.  
The 15 top level URLs in the Web Site's tree are monitored with a frequency of 5 min, and the full site is monitored once per hour. The exact time when the test is performed is, however, randomized and made unpredictable by using our secret key, based on the keyed scheduler.
## 2 Biosynthesis of Monoterepenes in Macroalgae
### Mevalonate (MVA) Pathway and Methylerithritol Phosphate (MEP) Pathway  
Two pathways are known for the biosynthesis of monoterepenes, and the first step includes the formation of the central building blocks of all isoprenoids (known as active isoprenes) isopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP). They can be formed by mevalonate (MVA) pathway and methylerythritol phosphate (MEP) pathway which is also known as deoxyxylulose-5-phosphate (DXP) pathway (Figure 1). Each of these pathways can occur in the algae cells. For instance, green algae possess only MEP pathway, while several red algae contain both or only one of the mentioned pathways. [PERSON] et al. [36] also suggested that further genomic sequencing of species is expected to provide more evidence for distribution of these two pathways. The MVA pathway occurred in the cytosol, while the MEP pathway is localized in the plastids, but both have the same task-to form IPP and DMAPP for the synthesis of the key intermediate geranyl pyrophosphate (GPP) [37].  
Even though the MVA pathway was discovered in 1950s in yeasts and animals, it is still considered to be the main route of IPP and DMAPP synthesis. It begins with condensation of three molecules of acetyl-CoA followed by the enzymatically assisted conversions for the formation of IPP, all occurring in the cytosol. Six different enzymes are involved in the formation of IPP with the phosphorylation of mevalonate by mevalonate kinase as the main reaction, while the seventh enzyme is responsible for the generation of DMAPP from IPP [36; 38].  
In contrast to the MVA pathway, the MEP pathway was discovered in 1990s in bacteria and plants. It begins with the reactions between glyceraldehyde-3-phosphate (G3P) and pyruvate and it is capable of producing both IPP and DMAPP as final products due to the presence of the enzyme 4-hydroxy-3-methylbut-2-enyl diphosphate reductase (HDR) [36; 39]. IPP is isomerized to DMAPP, which than reacts with IPP within the precursor pool and forms geranyl pyrophosphate (GPP), known as the common precursor for all monoterenes [40].
# Biodegradation of Sulfamethoxazole in Milkfish (_Chanos chanos_) Pond Sediments
## 1 Introduction  
Intensive farming is usually used for aquaculture. Due to efforts to increase productivity, aquaculture systems are suffering from overcrowding and disease outbreaks. Most infectious diseases in aquaculture are bacterial [1]. Antibiotics such as sulfonamides were usually used in aquaculture to prevent and treat bacterial infectious diseases [2]. As a consequently, residues of antibiotics remain in fishery products, and concerns have been raised because dietary consumption is the main way that humans are exposed to organic pollutants [3; 4]. Moreover, antibiotics such as sulfonamides are difficult to remove through conventional wastewater treatment. Environmental pollution will occur if sulfonamide-containing wastewater is discharged into rivers and oceans [5; 6].  
Coastal aquaculture is a vast industry because of the large demand for marine products. Milkfish (_Chanos chanos_) is an important coastally cultured fish among the south Asian countries [7]. Milkfish are eurylhaine fish that are usually cultured in a mixture of fresh water and sea water [8]. To prevent environmental pollution, wastewater after fish breeding must be properly treated. To perform biodegradation/bioremediation of the residual sulfonamide antibiotics in the water environment, sulfonamide-degrading bacteria that are tolerant of high salinity are necessary. Moreover, little isknown about the degradation of sulfonamide and the change in the bacterial community of milkfish culture pond sediments.  
The aim of this study was the isolation, identification, and characterization of sulfamethoxazole (SMX)-degrading bacteria from milkfish culture pond sediments. Batch and SMX continuous addition experiments were performed to assess SMX degradation with degrading bacteria in milkfish culture pond sediments. The effects of these bacteria on bacterial community composition in the sediment of milkfish culture ponds were revealed.
## 4 Overview of DSRC, LTE C-V2X, and 5G NR V2X  
The purpose of this section is to present a brief overview of recent technologies, DSRC, LTE C-V2X, and 5G NR V2X, deployed in connected and autonomous vehicles. Table 1 compares and contrasts the specifications of DSRC, C-V2X, and 5G NR.
## 4 Experiments and Analysis
### Experimental Results and Analysis  
#### 4.4.1 Bibc Model Comparison Experiment  
As shown in Table 3, we compare different baseline models with the BIBC model proposed in this paper. The IDCNN-CRF model (IC), which combines the IDCNN model and the CRF model, first performs local feature extraction through the IDCNN layer and then obtains the best output sequence through the CRF layer. The BERT-IDCNN-CRF model (BIC), with the addition of the pre-trained model, can better represent the before and after information of the text, thus improving the performance of the model. The BiLSTM-CRF model (BC) is a classical named entity recognition model. The BERT-BiLSTM-CRF model (BBC) is currently the most commonly used deep learning model for natural language processing tasks and has good performance. From the experimental results, it can be seen that the precision, recall, and F1 values of the BIBC model are 1.77%, 0.45%, and 1.11% higher than those of the BBC model, respectively. This is because, after adding the IDCN model, the BIBC model can better represent the information of Chinese medical texts, thus achieving relatively better model recognition results. By comparing the experimental results, it is found that adding the pre-trained model BERT to the model can better represent the deep information contained in the text, which can improve the entity recognition rate to some extent. The F1 value of the BIC model is lower than that of the BBC model because the BiLSTM can better extract the long-range context of the input text information, which makes it more advantageous than the inflated convolutional neural network. However, the advantage of the expanded convolutional neural network is that it is faster. Experimentally, it is proven that the BIBC model has a significant advantage over other baseline models in Chinese medical entity extraction. Moreover, in the improved Tri-Training algorithm, the three base classifiers use the better-performing BIC model, BBC model, and BIBC model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Super Parameter** & **Value** \\ \hline BERT max input size & 128 \\ Filter & 100 \\ LSTM dim & 128 \\ Dropout rate & 0.5 \\ Learning rate & 0.005 \\ Batch size & 50 \\ Epoch & 25 \\ Optimizer & Adam \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental hyperparameter settings (unit: %).
# Study of Data Transfer in a Heterogeneous LoRa-Satellite Network for the Internet of Remote Things
## 3 Heterogeneous Network Architecture  
### Conceptual IoT-Satellite Network Architecture  
A conceptual architecture of an IoT-terrestrial-satellite network that will be taken as the basis for our further discussion is shown in Figure 1. A set of remote endpoint devices collect information and pass it through a terrestrial relay network to a satellite gateway, which then forwards it through a LEO constellation to a receiving station and then further to a data collection system.  
The scalability, reliability and energy efficiency of the network all depend on the choice of data transfer technologies and underlying protocols. Such choice also defines constraints on the amount of information transmitted in a single message and the format of transmitted messages. Currently, there are many technologies and protocols that differ in communication range, speed, frequency bands and modulation used during data transmission.  
Figure 1: Conceptual Internet of things (IoT)-satellite network architecture.
## 2 Materials and Methods
### Biochemical Profiling
#### 2.5.2 Extraction and Quantification of Pigments  
Red seaweeds contain chlorophyll \(a\), carotenoids, and soluble phycobilins, mostly phycocerythrin and phycocyanin [13]. To quantify phycoerythrin and phycocyanin, 1 g of fresh, previously frozen seaweed (\(-\)20 \({}^{\circ}\)C) was weighed. The solvent and mortar were also cooled to preserve temperature-sensitive pigments. Phosphate buffer (0.1 M, pH 7.0) was added at a ratio of 1:20 (biomass), and the biomass was grounded for 10 min in a cold mortar on ice. The samples were then stirred in the dark for 30 min, centrifuged at 12,500\(\times\)_g_ at 4 \({}^{\circ}\)C for 20 min, and afterwards the liquid fraction was collected. Absorbance was read between 280 and 900 nm, on a UV-visible spectrophotometer (Epoch 2, BioTek, Winooski, VT, USA).  
Phycoerythrin (R-PE) and phycocyanin (R-PC) concentrations were calculated as described by [PERSON] and [PERSON] [57], using Equations (6) and (7), respectively.  
\[R-PE\ (mg/mL)=[(Abs565-Abs592)-(Abs455-Abs592)\times 0.20]\times 0.12 \tag{6}\]  
\[R-PC(mg/mL)=[(Abs618-Abs645)-(Abs592-Abs645)\times 0.51]\times 0.15 \tag{7}\]  
For chlorophyll \(a\) and carotenoids, 1 g of fresh frozen algae was weighed, and 90% acetone was added at a ratio of 1:20. Samples were grounded in a mortar for 10 min, stirred in the dark for 30 min, and then centrifuged at 8000\(\times g\) for 20 min at room temperature. Absorbance was read at 280-900 nm using a UV-visible spectrophotometer. Chlorophyll \(a\) (Equation (8)) and total carotenoid (Equation (9)) concentrations were calculated as per [PERSON] and [PERSON] and [PERSON] et al. [58; 59].  
\[Chl\ a\ \left(\upmu\frac{g}{mL}\right)=-0.3319\times(Abs630-Abs750)-1.7465 \times(Abs647-Abs750)+11.9442\times(Abs664-\] \[Abs750)-1.4306\times(Abs691-Abs750) \tag{8}\]  
\[Carotenoids\ \left(\upmu\frac{g}{mL}\right)=Abs480+0.114\times Abs663-0.638 \times Abs645 \tag{9}\]
## 3 Results and Discussions
### Effect of Operating Variables
#### 3.2.5 Effect of Coexisting Cations on Mn(II) Sorption  
Industrial wastewater is usually a complex system of multiple ions, and the cations present in the solution will compete with the heavy metal ions to be removed for sorption sites, thus affecting the sorption capacity of the adsorbent for heavy metal ions. The effect of coexisting cations on the sorption of manganese by DX is shown in Figure 6. Without the addition of other metal ions, the sorption and removal rates of Mn(II) by DX were better, as high as 5.38 mg:g\({}^{-1}\) and 90.22%, respectively. The addition of other metal ions (Pb\({}^{2+}\), Zn\({}^{2+}\), Cu\({}^{2+}\) and Fe\({}^{2+}\)) had an inhibitory effect on the sorption of Mn(II). In particular, the inhibitory effect of Zn\({}^{2+}\) was the most obvious, resulting in a decrease of 2.01 mg:g\({}^{-1}\) and 29.97% in the removal and sorption of Mn(II). The inhibition effect of Pb\({}^{2+}\), Fe\({}^{2+}\), Cu\({}^{2+}\) metal ions on Mn(II) sorption was similar, and the removal efficiency was reduced by 18.30-22.37%. The phenomenon resulted from the easy formation of covalent bonds with the sorption sites during Zn\({}^{2+}\) sorption, which formed a strong competition with Mn(II). Meanwhile, DX showed a purification effect for all heavy metals in aqueous solution, with sorption amounts of Pb\({}^{2+}\) (9.01 mg:g\({}^{-1}\)) \(>\) Cu\({}^{2+}\) (2.89 mg:g\({}^{-1}\)) \(>\) Zn\({}^{2+}\) (2.15 mg:g\({}^{-1}\)) \(>\) Fe\({}^{2+}\) (1.51 mg:g\({}^{-1}\)) in that order. The competitive sorption of heavy metal ions in the soil was significantly correlated with the electronegativity of the ions, the primary hydrolysis coefficient, the ratio of charge to the radius, and the magnitude of the Misono softness coefficient [44]. The adsorption process of heavy metal ions is dominated by physical adsorption, chemisorption and ion exchange. Compared with the single Mn(II) system, metal ions competing with Mn(II) for adsorption sites exist in the mixed cationic composite solution, thus affecting the adsorption efficiency of Mn(II). Therefore, exploring the adsorption-desorption process of the actual wastewater from the manganese mining area still needs to be explored in-depth with the actual wastewater composition.  
Figure 5: Effect of pH on the sorption effect of manganese.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 3 Compliance Architecture
### Adding Workflow Verification to an Execution Entity
#### 3.3.1 Client  
The client is modeled as an agent targeting the execution of workflow queues on our robotic system. This can be a human interacting via a user interface or the triggering of a pre-programmed workflow or also an autonomous agent such as an internal planning module or an external fleet management system. In both cases, the client needs to adhere to the compliance and accountability requirements, which may take different forms and details depending on the application context (so we leave the details open here). Considering this, we need an interface for retrieving signed workflow queues on the one hand and an interface for executing workflow queues on the robotic system on the other hand.  
Robot programs to be executed by the execution entity can be stored local and triggered externally or the robot can be externally controlled. In case the program is already stored on the robot controller, the verification should be done before execution. In case of external control, the verification is performed on the fly as the workflows are transmitted. In general, we look at the latter case since it is the more complex one. However, transferring our approach to a local storage scenario is quite  
Figure 2: Compliance architecture: A client wants to execute a workflow or a queue of workflows. It requires a signature of the compliance service. With its signature, the workflows are sealed. Upon execution, the authorization is verified by the authorization handler which also validates the signature. Only if both checks are successful, the workflow is passed on for execution. Numbers in round brackets show the order of steps in the process.  
straightforward. We mention that it is necessary that the deployment of the program needs to also contain the signature step and that the signature must be stored alongside the program.
###### Abstract  
Zinc oxide polymeric nanoparticles (ZPPs) of poly (styrene-co-acrylic acid) P(St/AA), containing oleic acid modified zinc oxide nanoparticles (OA-ZnO NPs), were synthesized via minieemulsion polymerization. By simply adjusting the quantity of reactants, i.e., sodium dodecyl sulfate (SDS) surfactant, potassium persulfate (KPS) initiator, and divinyl benzene (DVB) crosslinking agent, the location of ZnO NPs were altered from the inner (core) to the outer (shell), leading to core-shell and Pickering-like morphologies, respectively. The Pickering-like ZPPs were obtained when using SDS at below or equal to the critical micelle concentration (CMC). At above the CMC, the complete encapsulation of OA-ZnO NPs within the ZPPs depicted a kinetically controlled morphology. The transition to Pickering-like ZPPs also occurred when reducing the KPS from 2 to 0.5-1%. Whereas the DVB accelerated the polymerization rate and viscosity in the growing monomer-swollen nanodroplets and, hence, contributed to kinetic parameters on particle morphology, i.e., an increase in the DVB content increased the rate of polymerization. A hollow structure was obtained by replacing styrene with the more hydrophilic monomer, i.e., methyl methacrylate. All ZPPs-incorporated poly (vinyl alcohol) (PVA) films greatly improved shielding performance over the UV region and were relatively transparent on a white paper background. Due to the large number of ZnO NPs in the central region and, hence, the ease of electron transfer, composite films containing core-shell ZPPs possessed the highest UV blocking ability. ZnO NPs in the outer part of the hollow and Pickering-like ZPPs, on the other hand, facilitated the multiple light scattering according to the difference of refractive indices between the inorganic shell and organic/air core. These results confirm the advantage of structured ZPPs and their potential use as transparent UV shielding fillers.  
ZnO polymeric particle; core-shell; Pickering-like morphology; hollow particle; UV shielding; coating +
Footnote †: journal: Physics Letters  
Article
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 2 Background
### Covert Channel Methodology
#### 2.1.2 Methods of Encoding Data  
There are three primary encoding schemes that are commonly used in covert channels: (1) on-off keying (OOK), (2) Manchester encoding, and (3) Binary Frequency Shift Keying (BFSK) [31; 33; 34; 37].  
On-off keying is the simplest form of general amplitude-shift keying (ASK) modulation [34]. The presence of a signal, or carrier wave, for a certain duration encodes a logical one (\"1\"), while no signal or carrier wave for the same duration encodes a logical zero (\"0\"), as shown in Figure 3a.  
Figure 3: Common encoding methods for covert channels: (**a**) On–off keying, (**b**) Manchester Encoding, and (**c**) Binary Frequency Shift Keying.  
Manchester encoding is a scheme where each binary value is sent using two physical bits, with the transition occurring in the middle of the original bit. Each data bit is either low then high, or high then low, for equal time [34]. Depending on the convention used (Thomas or IEEE 802.3 [39]) a logic 1 can either be represented by a logic low followed by a logic high or vice versa, as depicted in Figure 3b. Manchester encoding's transfer rate is half of that of OOK, since it uses two physical bits for each logical bit. This type of encoding is considered more reliable because of the redundancy of each transmitted bit [34].  
In binary frequency shift keying (BFSK), frequency is modulated at varying rates to produce a logic \"1\" or logic \"0\" [34]. Binary Frequency Shift Keying uses a pair of discrete frequencies to transmit binary information. The instantaneous frequency of the carrier is switched between two values in relation to the binary values being transmitted, as illustrated in Figure 3c.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 4 Conclusions and Recommendation  
This paper proposes an efficient receiver architecture for implementing a digital channelized receiver for low SNR chirp signals intercept applications. By optimizing the factor \(F\) in the relation \(K=FD\), the channel arbitration logic is shown to be reliable for cross-channel signals. Based on the channel arbitration logic, the proposed receiver architecture can adapt to the instantaneous bandwidth limited only by the system sampling rate and the band-pass sampling theorem. A CORDIC-based instantaneous frequency measurement algorithm is also proposed. When the input SNR is 0 dB, the absolute frequency RMSE of bandwidth and the center frequency are 0.57 MHz and 1.05 MHz, respectively. In addition, an adaptive threshold generation algorithm is proposed to detect signals without prior information. Simulation shows that the proposed algorithm is reliable and robust for low SNR and wideband chirp signal detection.  
The contributions of this paper are summarized as follows.  
1. A non-critical sampling digital channelized receiver architecture is proposed to detect wideband low SNR chirp signals. The proposed receiver architecture can adapt to a wide instantaneous bandwidth with high frequency estimation accuracy when the SNR is greater than 0 dB.
2. An adaptive threshold generation algorithm is proposed to detect signals without prior information.
3. A CORDIC-based instantaneous frequency measurement algorithm is also proposed, improving low SNR chirp signals' frequency estimation accuracy.  
The efficient frequency measure algorithms for lower SNR chirp signals in the future need further research. It is possible to combine instantaneous frequency measurement algorithms with existing high-precision parameter estimation algorithms to achieve high-precision estimation with high efficiency.  
Conceptualization, W.C. and Q.Z.; methodology, W.C. and W.L.; validation, W.L., H.W. and W.C.; software, H.W. and W.C.; writing--original draft preparation, W.C.; writing--review and editing, Q.Z.; visualization, W.C.; supervision, X.L.; project administration, Q.Z. and X.L.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the editors and reviewers for their efforts to help the publication of this work.  
The authors declare no conflict of interest.
## 5 Results and Discussion
### Measurements and Interpretation
while in pattern B, the load reduces to approximately 10 W to 50 W. The deviation of 50 W to 65 W fits well to the consumption of a conventional notebook, which suggests some randomness in the usage patterns of the notebook. Lastly, singular power-draw events appear. Such events could, e.g., be caused by the charging of devices or the switching on of electrical appliances for a short time period. Based on the obtained measurements, first conclusions regarding user behavior can be drawn and typical patterns be derived. Analyzing concurrency of energy use and occupancy consequently enables improved forecasting of, e.g., electric power consumption. The obtained data not only facilitates a qualitative understanding of behavioral patterns, but also allows for the training of data-driven models. In the context of model-based control, these models can be integrated directly into an energy system model, or be used to provide forecasts as external inputs to the controller. The application of data-driven forecasting is the subject of ongoing research at the JSL test-site, where multi-input/multi-output approaches based on ANN and LSTM models for predicting electric power consumption (building-wide and on the room level) are compared and evaluated. Such enhanced forecasts can boost MPC approaches significantly, unlocking further energy-saving potential in building automation and control.