esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Throughput Cross-Technology Communication via Chip-Level Side Channel
## 2 Preliminaries
### How ZigBee Signal Impacts WiFi Preamble Detection  
In the WiFi preamble detection, if the received signal is a ZigBee signal, the phase offset would be different from noise and WiFi preamble. In Figure 5, the left part in red rectangle is the phase offset of noise and the right part in green rectangle is phase offset of a ZigBee signal. Even though ZigBee signal is totally different from WiFi preamble, there is still a strong pattern in the phase offset values, as shown in Figure 5. Despite the fluctuation, there are still some stable periods in the phase offset values. Now, we can prove that the values of these stable phase offset are \(\pi\) and \(-\frac{2\pi}{5}\).  
Figure 4: Architecture of WiFi Preamble Detection.  
Figure 3: WiFi Preamble Structure.  
**Theorem 1**.: _When WiFi preamble detection receives a ZigBee signal, the values of the phase offset in the stable periods are \(\pi\) and \(-\frac{2\pi}{5}\)._  
Proof.: In this paper, we use \(Z(k)\) to denote the \(k_{th}\) sample of raw ZigBee signal and \(\Delta f\) to denote the difference of center frequency of ZigBee sender and WiFi receiver. Further, \(T_{s}\) stands for duration of sample at WiFi receiver, which is equal to 0.05 \(\upmu\)s. Then, we have \(S(k)=Z(k)e^{j2\pi\Delta f/T_{s}}\). We also use \(P(k)\) to denote the multiplication of \(S(k)\) and conjugate of \(S(k+16)\):  
\[\begin{array}{rcl}P(k)&=&S(k)S(k+16)^{*}\\ &=&Z(k)e^{j2\pi\Delta f/kT_{s}}Z(k+16)^{*}e^{-j2\pi\Delta f(k+16)T_{s}}\\ &=&Z(k)Z(k+16)^{*}e^{-j2\pi\Delta f/T_{s}},\end{array} \tag{1}\]  
where \(S(k+16)^{*}\) is the conjugate of \(S(k+16)\). The phase offset of ZigBee signals at WiFi receiver is \(\angle P(k)\). Since \(e^{-j2\pi\Delta f/T_{s}}\) is a constant complex value and would not affect the fluctuation of \(\angle P(k)\), whether \(\angle P(k)\) is stable or not is determined by \(Z(k)Z(k+16)^{*}\). Moreover, \(\angle P(k)=\angle(Z(k)Z(k+16)^{*}-32\pi\Delta f/T_{s}mod\pi\)  
When two consecutive chips on I-phase are the same and two consecutive chips on Q-phase are the same, there would be a stable period. For instance, as shown in Figure 6, chips on I-phase are two '1's and chips on Q-phase are two '0's, \(\angle(Z(k),Z(k+16))\) is equal to \(\frac{\pi}{5}\). When two consecutive chips on I-phase are different and two consecutive chips on Q-phase are different, there also exists a stable period. For instance, as shown in Figure 7, chips on I-phase are '1' and '0' and chips on Q
## 2 The Automatic Plates Recognition System
### Data Preprocessing  
Data preprocessing involves two key steps: interpretation marker establishment and dataset annotation. Interpretation markers, used to differentiate between clean and non-clean plates, are critical in reducing the subjectivity of human judgment and ensuring the accuracy of data annotation. Nearly 40 interpretation markers have been established based on extensive image data. The food waste determination standard adopts a combination of visual quantification and threshold grading: when only inedible parts (such as bones, shells), seasonings (such as garlic, ginger, and scallions), or edible residue covers less than 15% of the tray, it is classified as a \"clean plate\" (clean state); conversely, if the coverage of edible leftover food (such as leftover rice or vegetables) reaches or exceeds 15%, it is classified as a food waste event (See Figure 3). This design addresses the FAO's threefold requirements for food waste monitoring: edibility definition, operability quantification, and local adaptation. The specific threshold of 15% coverage rate is validated through local experiments. The overall classification framework refers to the authoritative standards for dining scenarios outlined in the FAO's \"_Global Initiative on Reducing Food Loss and Waste_\" published in 2015, aligning with the global collaborative efforts to reduce food waste.  
Figure 3: Interpretation markers of clean and non-clean plates.  
Figure 2: Real-time data acquisition platform. **(a)** Overview of the real-time data acquisition system deployed at cafeteria waste collection points. **(b)** Fixed camera setup at the collection point, showing the typical field of view. **(c)** Side-mounted cameras on tripods capturing diners’ facial features for identity matching. **(d)** Top-view camera mounted directly above the collection area, capturing clear imagery of plates.  
In the dataset labeling process, firstly, one frame was extracted from the video recording every 0.5 s, and the images were screened based on manual visual inspection, discarding blurred or duplicated low-quality images. Due to the use of fixed camera shooting, the image quality was relatively stable, and there were fewer low-quality images. Next, the screened images were imported into Labelme v3.16.7 labeling software, and each image was labeled with clean and non-clean plates according to the preset labeling criteria. The corresponding json labeling files were then generated. The annotation work was carried out by multiple annotators, and regular training and cross-checking of the annotation results were conducted among the annotators to ensure standardization and consistency of the annotation. Finally, all the.json annotation files were converted into.png format annotated images for training and accuracy verification of the deep learning network. A total of 2085 images were acquired and annotated, with a division of 4:1 between the training set and the validation set.
# Delegation-Based Personal Data Processing Request
## 3 Delegation-Based Personal Data Processing Request Notarization Framework  
This section proposes a delegation-based personal data processing request notarization framework that can notarize requests by the DS for the processing of personal data and its corresponding response. This was done to guarantee reliability and integrity for the GDPR audit. The DS delegates the request for the processing of personal data to the proposed framework, which forwards the request to the SP's DC by e-mail such that the DC responds to the request. For GDPR compliance, e-mail was used for a formal request proof of the processing of personal data from DS to SP. Herein, the request and response were recorded in the BC ledger. The ledger was notarized via nodes in the BC network. Consequently, the right of the DS to process personal data as stipulated by GDPR is guaranteed. Personal information may be included in the transaction sent by DS or DC to the proposed framework, so a method to protect personal data is required. The proposed framework protects personal data by using a session key-based encryption method. In order for the user to use the proposed framework, he/she must consent to the delegation of authority for personal data processing when he/she sign up for the service. This process is the same as general consent processing, so it is omitted from the proposed framework architecture.  
To design a framework for GDPR compliance, we set the following design security. Based on the framework, the auditor can perform a GDPR compliance audit of security design goals to ensure reliability and credibility of data processing among network participants.  
* Confidentiality: Network participants must be able to trust the transaction data that are evidence related to the processing of personal data.  
Figure 2: Proposed solutions for GDPR compliance.  
Integrity: It must be guaranteed that the created and managed transaction data are not illegally forged or altered.
* Non-repudiation: Denying related facts based on the subject of transaction data creation and management is not possible.  
To design a new notarization framework, first, this section presents a derivation of the required features for GDPR compliance data processing, and thereafter, it proposes a private BC-based notarization framework that can satisfy the derived features.
## 2 Methodology  
In recent decades, data assimilation methods have attracted increasing attention in various fields, such as traffic estimation, hydrologic forecasts, and so on. Sequential data assimilation is a general framework whereby system states and parameters are recursively estimated/corrected when new observations are available. In a sequential data assimilation process, the evolution of the simulated system states can be represented as follows:  
\[x_{t}^{f}=f(x_{t-1}^{a},u_{t},\theta)+\omega_{t} \tag{1}\]  
where \(f\) is a nonlinear function expressing the system transition from time \(t\)-1 to \(t\), in response to model input vectors \(x_{t-1}^{a}\)\(u_{t}\) and \(\theta\); \(x_{t-1}^{a}\) is the analyzed (i.e., posteriori) estimation (after correction) of state variable \(x\) at time step \(t-1\); \(x_{t}^{f}\) is the forecasted (i.e., priori) estimation of state variable \(x\) at time step \(t\); \(\theta\) represents time-invariant vectors, and \(\omega_{t}\) is considered as process noise.  
When new observations are available, the forecasted state can be corrected by assimilating the observations into the model, based on the output model responding to the state variables and parameters. The observation output model, in general form, can be written as:  
\[y_{t}^{f}=h(x_{t}^{f},\theta)+v_{t} \tag{2}\]  
where \(h\) is the nonlinear function producing forecasted observations and \(v_{t}\) is the observation noise.  
The essential methods for updating states is based on Bayesian analysis, in which the probability density function of the current state, given the observation, is approximated based on the recursive Bayesian law:\[p(x_{t},\theta_{t}|y_{1:t})=\frac{p(y_{t}|x_{t},\theta_{t})p(x_{t},\theta_{t}|y_{1: t-1})}{p(y_{t}|y_{1:t-1})} \tag{3}\]  
where \(p(x_{t},\theta_{t}|y_{1:t-1})\) represents the prior information, \(p(y_{t}|x_{t},\theta_{t})\) is the likelihood, and \(p(y_{t}|y_{1:t-1})\) represents the normalizing constant. If the model is assumed to be Markovian, the prior distribution can be estimated via the Chapman-Kolmogorov equation:  
\[p(x_{t},\theta_{t}|y_{1:t-1})=\int p(x_{t},\theta_{t}|x_{t-1},\theta_{t-1})p(x _{t-1},\theta_{t-1}|y_{1:t-1})dx_{t-1}d\theta_{t-1} \tag{4}\]  
Similarly, the normalizing constant \(p(y_{t}|y_{1:t-1})\) can be obtained as follows:  
\[p(y_{t}|y_{1:t-1})=\int p(y_{t}|x_{t},\theta_{t})p(x_{t},\theta_{t}|y_{1:t-1}) dx_{t}d\theta_{t} \tag{5}\]  
The optimal Bayesian solution (i.e., Equations (3) and (4)) is difficult to determine since the evaluation of the integrals might be intractable [25].Consequently, approximate methods are applied to treat the above issues. The ensemble Kalman filter (EnKF) and particle filter (PF) are two of the most widely used methods. The central idea of EnKF and PF is to represent the state probability density function (pdf) as a set of random samples, and the difference between these two methods lies in the way of recursively generating an approximation to the state pdf [26].
## 3 Results
### Wall Shear Stress  
The wall shear stress \(\tau_{\omega}\) is a significant parameter for the understanding of the flow separation. According to [PERSON] et al. (2009), higher values of wall shear stress are directly related with the delay or avoidance of the separation of the flow.  
The wall shear stress of all VG height cases and four incident angles, \(\beta\) = 10\({}^{\circ}\), 15\({}^{\circ}\), 18\({}^{\circ}\), and 20\({}^{\circ}\), was calculated. For higher values of the shear stress, the boundary layer gets better attached to the surface. In Figure 8, the wall shear stress \(\tau_{\omega}\) is shown for four incident angles depending on the distance from the TE of the VG.  
Figure 7: CFD results of the half-life radius \(R_{0.5}\) for four different incident angles, (**a**) corresponds to \(\beta\) = 10\({}^{\circ}\), (**b**) 15\({}^{\circ}\), (**c**) 18\({}^{\circ}\), and (**d**) 20\({}^{\circ}\). EXP (blue triangles) indicates the experimental results obtained by [PERSON] (1984) for a VG height of 0.9H. Represented values are normalized with respect to the boundary layer thickness (\(\delta\)) at the VG position. The y-axis represents the normalized half-life radius \(\frac{R_{0.5}}{\delta}\) and the x-axis represents the normalized axial distance \(\frac{x}{\delta}\).  
Figure 8: _Cont._  
The wall shear stress shows the highest value for the 0.6H VG height case for all the incident angles. For the incident angle \(\beta=10^{\circ}\), the 0.4H VG height case starts with a big value and decreases as the cutting planes are farther from the VG trailing edge. The wall shear stress varies with the value heights  
Figure 8: Wall shear stress at different planes normal to the streamwise direction and located at distances proportional to the local boundary layer thickness \(\delta\) from the trailing edge is shown for six vortex generator heights (0.2H, 0.4H, 0.6H, 0.8H, H, and 1.2H). Four different incident angles are represented, (**a**) corresponds to \(\beta=10^{\circ}\), (**b**) \(15^{\circ}\), (**c**) \(18^{\circ}\), and (**d**) \(20^{\circ}\).  
and the different incident angles. This variation is even more obvious for incident angles of \(\beta=18^{\circ}\) and \(\beta=20^{\circ}\), where higher values of wall shear stress are observed for the 0.4H and mainly the 0.6H VG height cases, and as long as the downstream distance from the TE is bigger, the wall shear stress curve decreases. However, for the 0.8H, H, and 1.2H VG height cases, the wall shear stress is almost constant for the four incident angles and all along the streamwise direction, with a small variation for the incident angle \(\beta=10^{\circ}\).
## 4 Avenues and Approaches for Improving the Biotransformation of Limonene and Alpha Pinene
### Pathways of Terpenes Biotransformation
#### 4.3.1 Bicyclic Monoterepenes  
The isomers \(\alpha\)-pinene and \(\beta\)-pinene (C10H16) are the primary components of wood resins, particularly conifers. _Pseudomonas Rhodesia_ (CIP 107491) and _P. fluorescens_ (NCIMB 11671) were found to grow on \(\alpha\)-pinene as the sole carbon source. \(\alpha\)-pinene undergoes oxidation to \(\alpha\)-pinene oxide via a NADPH-dependent \(\alpha\)-pinene oxygenase and then experiences ring cleavage by a specific \(\alpha\)-pinene oxidasely, yielding isonoval, which is subsequently isomerised to novala [6]. The cleavage reaction of \(\alpha\)-pinene oxide has also been observed in [PERSON] sp. strain P18.3 [37]. An alternative degradation pathway for pineen via a monocyclic p-menthene derivative has been described in Pseudomonas sp. strain PIN [49]. Bacillus pallidus BR425 degrades \(\alpha\)-pinene and \(\beta\)-pinene, yielding limonene and pinocarveol. While \(\alpha\)-pinene is transformed into limonene and pinocarveol, \(\beta\)-pinene exclusively produces pinocarveol. Both intermediates can be further converted into carveol and carvone. Although the activity of specific monooxygenases has been suggested, experimental evidence is lacking [5]. Serratia marcescens utilises \(\alpha\)-pinene as the sole carbon source, leading to the formation of trans-verbenol. In glucose and nitrogen-supplemented medium, this strain produces \(\alpha\)-terpineol. The two oxidation products are considered dead-end products as they accumulate in cultures [6]. It should be noted that for many biotransformation studies, caution must be exercised as monoterepenes often contain impurities and oxidation products that can serve as substrates, resulting in traces of monoterepene and monoterenoid transformation products that are not further metabolised.
# Message Action Adapter Framework in Multi-Agent Reinforcement Learning
## 7 Results
### Joint Interpretation of Base and Adapted Policies  
In this section, we propose two visualizations utilizing base and adapted policies:  
1. Separate trajectories obtained by a base and adapted policies.
2. A trajectory is obtained by a policy while the other provides counterfactual action.  
Figure 11 shows the trajectories of two types of actions: base and adapted. Recall that base and adaptation policies have different branches and combine the outputs only at the levels of action distribution by adding logit. The adapted policy can be interpreted as the direction of residual action. This visualization provides users insights (if visualized) by showing how communication changes the original trajectory.  
Figure 11: Trajectories for the base and adapted policies (with message cases) in the spread environment. Black and blue stars are the locations of three agents with and without communication, respectively. The darker color represents later time steps. Diamond is the target location where three agents (represented by stars) must be close to obtain rewards while separating each other.  
Figure 9: Box plots of normalized returns for different environments. Each plot is obtained by running three seeds. In a spread environment, we observed no difference between message types, indicating that the contribution of messages is weak. In the tag environment, the performance increases as communication message types become complex. In SMAC environments, simple messages, such as only having observation or action, provide better performance than having complex message types. These results indicate that feature selection on message types is necessary for MARL communication.  
Rather than a trajectory-level comparison, we can visualize a single-point analysis. Figure 12 shows trajectories obtained by sampling actions with a base policy and visualizing an action with a maximum likelihood for an adapted policy. This visualization provides how the counterfactual case (having messages) can change the action direction. We acknowledge that visualization is possible only when the environment is simple enough. These interpretations may not work for the cases of SMAC, where the more diverse actions are conducted.
###### Abstract  
Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.  
Keywords:Harmony Search Algorithm; Task Distribution; Energy Efficiency; Virtual Machine Placement; Cloud Datacenters +
Footnote †: journal: Applied Sciences
## 6 Global Genome Repair (GGR)
### Two Types of ABC ATPase Sites on UvrA Power Damage Detection and UvrB Binding
The nucleotide-binding domains that make up the proximal and distal nucleotide-binding sites from Protein Data Bank (PDB) entries 2R6F [37], 3 UX8 [21], 3 UWX [21], 3 PHI [19], 3 ZQJ [45], 2 VF7 [44], 2 VF8 [44] and 6N9L [46] were superimposed on the ATP-binding (ATP) domain. The structures are colored in gray save for 3 UX8 (orange), 3 PHI (red) and 6N9L (cyan) in the proximal site alignment; and 3 PIH (red) in the distal site alignment. The colored structures deviate from remaining members of the group by rotation of their respective signature (SIG) domain. The proximal (P) site bound nucleotide weakly, while the distal (D) site bound nucleotide tightly; this finding could be correlated to the subtle difference in amino acids surrounding the two types of nucleotide binding sites. In the absence of DNA, the weak proximal site hydrolyzes ATP rapidly, which in turn stimulates turnover at the tight distal site to give rise to a P\({}_{\text{2 ATP}}\)-D\({}_{\text{2 ATP}}\) species. Interestingly, provision of native or damaged DNA provokes divergent responses: a P\({}_{\text{2 ATP}}\)-D\({}_{\text{2 ATP}}\) species accumulates when native DNA is detected, in contrast, damaged DNA leads UvrA to populate a P\({}_{\text{2 ATP}}\)-D\({}_{\text{empty}}\) or P\({}_{\text{2 ATP}}\)-D\({}_{\text{ATP}}\) states [46]. This remarkable repertoire of varied responses likely set the stage for transitioning towards to the damage specific stages of NER. Insights into roles played by the ATPase activities of UvrA have also emerged from thoughtful experiments on NER in live _E. coli_[38]. This study showed that UvrA requires ATP binding at both proximal and distal sites to load UvrB. Closer analysis revealed that ablation of ATP binding at the proximal site (Walker A, K37A) leaves the capacity of UvrA to locate lesions relatively untouched in comparison to wild-type. Further, impeding ATP hydrolysis by the proximal site (Walker B, E514A) results in a species that binds tightly to DNA regardless of the presence of the damage; this mutant was also deficient in loading UvrB onto damaged DNA. Disruption of the distal site (Walker A: K646A) revealed a crucial role for the distal site in finding the lesion. Impairing the capacity of the distal site to hydrolyze nucleotide turned UvrA into a tight DNA binding protein, similar to what was measured with the proximal ATP hydrolytic mutant. In contrast to the proximal site, disruption of ATP hydrolysis at the distal site left the UvrB-loading capacity of UvrA unscathed. Further, the authors infer that UvrB loading requires a functional proximal site but only the capacity of the distal site to bind ATP.
## 4 Thermal Management of Heat Transfer in 5G Networks Technology
For example, Figure 11 illustrates an enhanced liquid-cooled base transceiver station (BTS) developed by [PERSON] et al., 2020 [90], compared to an old one with a traditional heat sink. Some authors, like [PERSON] et al., 2019 [91], have examined the theoretical concept of enhancing the performance of communication systems through the application of cryogenic cooling to their RF front-ends. This technique has garnered limited attention in the context of wireless communications. The primary theoretical analysis results obtained by [PERSON] et al., 2019 [91], are very promising and suggest a significant increase in channel capacity, which could lead to improved spectral efficiency, extended range, or reduced power output from mobile stations. This technique has wide potential applications in base stations for machine-type communication (MTC) and Internet of Things (IoT) equipment. _Thermal Management: Future Directions and Challenges_  
The increasing demands in power generation and heat release from 5G base station equipment and electronic devices require further research and development efforts. This is to propose new optimal designs of enhanced thermal management and more efficient heat transfer in circuit boards, components cabinets, and amplifier devices. Future solutions can be summarized by the following guidelines in computational science and design process:  
* Enhanced designs of future generations of antennas and electronic chips and components for reduced energy consumption. * Enhanced designs of heat sinks and coolers that employ innovative new technologies, such as non-traditional particle-fluid-based techniques [92; 93; 94], unconventional heat sinks design [95], innovative thermo-fluid topology designs [96; 97; 98; 99], and shape optimization methods [100; 101; 102]. Figure 11: (a) Air-cooled base transceiver station (BTS) unit with a heat sink; (b) the same BTS unit with a liquid cooling modification [90].