esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Protein Expression/Purification  
The HRas-CRaf-RBD_CRD complex was crystallized using the HRasR97C mutant. The HRasR97C construct containing residues 1-166 (henceforth referred to as HRas) was generated by PCR site-directed mutagenesis from the wild-type HRas construct similarly truncated. HRas protein was expressed, purified, and GDP was exchanged for the non-hydrolyzable GTP analog GppNHp, as previously described [39]. The GB1_Raf-RBD-CRD (52-184) construct, as previously published [7; 40], was extended utilizing PCR site-directed mutagenesis to generate the construct of CRaf-containing residues 52-187 used for the experiments described in this paper. Cys 184 coordinates a Zn\({}^{2+}\) ion and truncation at this position led to a disordered CRD in our previously published structure [7]. CRaf protein was transformed into _Escherichia coli_ BL21(DE3) cells and grown at 37 \({}^{\circ}\)C at 220 rpm in the presence of 50 mg/L ampicillin. Expression was induced in the presence of 20 uM ZnCl\({}_{2}\) at an OD\({}_{600}\) of 0.6-0.8 with 0.5 mM IPTG. The temperature was lowered to 32 \({}^{\circ}\)C and cells were harvested after 5 h by centrifugation.  
Cells were resuspended in 50 mM Tris-HCl pH 7.4, 500 mM NaCl, 1 mM BME, 5% \(v/v\) glycerol, and 20 uM ZnCl\({}_{2}\) in the presence of 1 mg/mL leupeptin, 1 mg/mL pepstin, and 12 mg bezamidine. Cells were lysed by sonication and the insoluble fraction was separated by centrifugation at 13,500 rpm, 4 \({}^{\circ}\)C for 30 min. The supernatant was syringe filtered through a 0.45 um membrane before performing Ni NTA affinity chromatography (HisTrap HP, GE Lifesciences). CRaf protein was eluted with 200 mM imidazole. Fractions were pooled in the presence of 1 molar equivalent HRas and concentrated to 2 mL. Protein was dialyzed overnight into cleavage buffer (20 mM Tris-HCl pH 8.1, 100 mM NaCl, 1 mM DTT, 25 mM CaCl\({}_{2}\), 5 mM MgCl\({}_{2}\), 20 uM ZnCl\({}_{2}\), 5% \(v/v\) glycerol) at 4 \({}^{\circ}\)C. Ten units of thrombin were added per mg of CRaf_52-187 and incubated for 18 h at room temperature. The cleaved complex was further purified by size exclusion chromatography (16/60 Sephacryl S100, GE Lifesciences).
## 2 Materials and Methods
### Compression Tests  
The material behaviour was also analysed through a series of compressive tests with quasi-static loads. As far as the specimen preparation, test loading application, and data analysis go, the EN ISO 844:2014 and ASTM D 1621 standards were followed.  
Compression tests were performed on cubic specimens having a size of 50 \(\times\) 50 \(\times\) 50 mm\({}^{3}\). Testing loads were applied on specimens by an electro-mechanical universal testing machine (INSTRON 5566, Norwood, MA, USA) digitally controlled by BLUEHILL 2 software. More specifically, the tests were carried out under displacement control, with longitudinal strain rates \(\dot{\epsilon}\) equal to 0.1, 2.0 and 10 min\({}^{-1}\). To get data on foam hysteretic behaviour, specimens were compressed until a nominal strain of 80% was reached and then unloaded with the same strain rate amplitude of the loading phase. During each test, the value of the applied load and the specimen height reductions were acquired by the machine control software to obtain the foam engineering stress-strain curve.  
Some compressive stress-strain curves are reported in Figure 2, which provides information on the foam mechanical performance. The observed mechanical behaviour is consistent with that of flexible foam. Specifically, an initial linear region was evident and suggested a stiff mechanical response at the onset. This region was followed by a portion of the curve with a lower stiffness. A final stiff region of the stress-strain curve was observed as it is usually reported for flexible foams. The central region of the curve did not show a plateau (a zero-slope region) and was characterized by a lower stiffness compared with the other two regions of the graph. A compressive modulus of elasticity (\(E\)) of 0.4136 MPa was calculated from the slope of the initial linear region (\(\epsilon<0.02\)) of the stress-strain curve. Furthermore, the results of Figure 2 reveal a stiffening effect when the strain rate is increased, which represents a typical aspect of the viscoelastic behaviour of the material.  
Figure 2: Loading—unloading compressive stress–strain curves of ethylene-vinyl acetate (EVA) specimens at different strain rates (\(\dot{\epsilon}_{1}\) = 0.1 min\({}^{-1}\), \(\dot{\epsilon}_{1}\) = 2.0 min\({}^{-1}\), \(\dot{\epsilon}_{1}\) = 10 min\({}^{-1}\)).
# A Data-Driven Game Theoretic Strategy for Developers in Software Crowdsourcing: A Case Study
## 3 Research Design
### The Competition is Weak  
Before discussing the reward strategy, we carry out reasonable assumptions and describe the question.  
**Assumption 1**.: _When the competition was weak, developers with a strong crowdsourcing ability can get rewarded. At the same time, crowdsourcing tasks were relatively abundant during this period (in fact, crowdsourcing tasks are added dynamically)._  
**Question 1**.: _When the number of crowdsourcing tasks is \(n\), completion of crowdsourcing task \(i\) consumes the time \(t_{i}\), and its reward is \(v_{i}\), and the duration of the contest season is \(T\), then how would one choose the best crowdsourcing task during a certain period if the worker wants to get the most reward?_  
_Formal description:_ Given \(T>0,t_{i}>0,v_{i}>0,1\leq i\leq n\), we need to find \(n\) vectors, in which \(x_{i}\in\{0,1\}\), satisfying the condition \(\sum_{i=1}^{n}t_{i}x_{i}<T\) to make the \(\sum_{i=1}^{n}v_{i}x_{i}\) the largest, namely:  
\[max\sum_{i=1}^{n}v_{i}x_{i} \tag{7}\]\[s.t\left\{\begin{array}{c}\sum\limits_{i=1}^{n}t_{i}x_{i}<T\\ x_{i}\in\{0,1\}\,1\leq i\leq n\end{array}\right. \tag{8}\]  
From the question description we can see that in weak competition the strategy of reward maximization for workers is the 0-1 knapsack problem. Dynamic programming can be used to figure out the equation, and the process of which includes two statuses:  
Status 1: There remains time after the \(i-1\) tasks have been chosen, in which case the maximized income can be acquired without choosing the \(i\)th task.  
Status 2: There remains time \(T-t_{i}\) after choosing the task \(i-1\), in which case choosing the \(i\)th task can help him get the maximum reward. The state transition equation for this problem is:  
\[f(i,T)=max\{f(i-1,T),f(i-1,T-t_{i})+v_{i}\} \tag{9}\]  
The Algorithm of Reward Maximization  
Input: The cost of tasks, \(T_{t}=\{t_{1},t_{2},\cdots,t_{i}\}\); the prize of tasks, \(V=\{v_{1},v_{2},\cdots,v_{i}\}\); total time: \(T\)  
1. n\(\leftarrow\) length[\(V\)]  
2. **for**\(j\gets 0\) to \(T\)  
3. **do**\(m[0,j]\gets 0\)  
4. **for**\(i\gets 1\) to \(n\)  
5. **do**\(m[i,0]\gets 0\)  
6. **for**\(j\gets 1\) to T  
7. **do**\(m[i,j]\gets m[i-1,j]\)  
8. **if**\(t_{i}\leq j\)  
9. **then**\(m[i,j]\gets v_{i}+\text{m}[i-1,j-t_{i}]\)  
10. **return**\(m\).  
11. \(n\leftarrow\) length[\(T_{i}\)]  
13. \(j\gets T\)  
15. **for**\(i\leftarrow\text{n to 1}\)  
16. **do**\(m[i,j]=m[i-1,j]\)  
17. **then**\(x[i]\gets 0\)  
19. **else**\(x[i]\gets 1\)  
20. \(j\gets j-t_{i}\)  
21. return \(x\)  
Output: \(x=\{x_{1},x_{2},\cdots,x_{i}\}\), \(x\in\{0,1\}\), made \(\sum_{i=1}^{n}t_{i}x_{i}<T\) and \(\sum_{i=1}^{n}v_{i}x_{i}\) is the maximum.  
In this algorithm, the time complexity of line two to line three is \(O(n)\). Two nested for-loops within line four to line 10 repeats \(n\) times in the outer layer and \(T\) times in the inner layer, of which the time complexity is \(O(Tn)\). The time complexity of the rest of the lines is \(O(n)\), obviously.
## 2 UAV Network Communication Model and Architecture
### Decentralized Communication Architecture  
Decentralized architecture is an important trend in modern UAV systems because of its scalability, flexibility, and high reliability. This architecture is essentially a control model in which there is no single control center [28]. Instead, UAVs operate independently, or rely on automatic coordination with other UAVs in the network. In this system, based on information from sensors, communication among UAVs, or algorithms, each UAV can make decisions without direct guidance from a central server or UAV leader [29].  
Since UAVs operate at high speeds and their missions can cover large areas, the connection status of UAVs changes frequently. Therefore, with its high flexibility and  
Figure 3: Schematic diagram of centralized communication architecture.  
adaptability, decentralized architectures are increasingly proving their role in UAV swarm applications. The \"Single-Group Swarm Ad hoc Network\" is a model that is very suitable for the UAV swarm problem. UAVs in the group self-organize and coordinate with each other without fixed network infrastructure or central servers. This helps the whole group operate as a unified entity [30].
# Digital Twin Conceptual Model within the Context of Internet of Things
## 6 Data Analytics and Visualization Layer (DAVL)  
Once the data is available at the VSL, the DAVL can access the data repositories to mine the data and report results to management. The aim of data mining is to assess the condition of the physical object or system and predict possible failures and maintenance requirements for the foreseeable future.  
The digital twin of a system, product, or process can be used efficiently to monitor and remotely manage the physical item using data analytics and business intelligence tools and technologies. It allows programming maintenance schedules, load balancing, and predicting failures and disruptions. Using the digital twin model, design specifications, and BOM data, we can construct the raw data available in the VSL into a knowledge base that reflects machine health and operational performance. This will transform the digital twin into a data-driven digital twin model in which the operational parameters of machine sensors or machine components have to be rectified or adapted continuously in the operation stage of the manufacturing process. Additionally, using advanced machine learning algorithms and data analytics, we can integrate real-time streaming sensor data with other operational inputs to create an operational data-driven digital twin. This operational digital twin will provide a more holistic and dynamic virtual representation of the whole manufacturing system, end-to-end processes, and operations. Thus, these analytics are important to be combined with a digital twin in order to reduce system downtime, improve production efficiency, and perform predictive quality maintenance.  
The data analytics sublayer involves two main tasks: (1) Perform relatively complex computations on the sensors' big data that could not have been achieved through edge processing and (2) employ machine learning or deep learning algorithms to predict the condition of the physical object or system in the foreseeable future. First, based on real-time sensors' data, historic data, BOM, and design specifications, a classifier can be built utilizing supervised machine learning or deep learning algorithms. These techniques are further elaborated as follows.
## 6 Results and Discussion
### Practical Applicability of the Proposed CNN-LSTM Algorithm  
A key practical consideration is the capability for real-time application; thus, the method's suitability in this context was evaluated. An analysis was conducted to determine whether the proposed EEG signal denoising method is appropriate for real-time applications, particularly beneficial for brain-computer interface systems. All computations and software implementations were carried out using Python (version 3.9.18) and the TensorFlow library (version 2.10.1). The experiments were performed on a computer equipped with 32 GB of RAM, a Gigabyte GeForce GTX 1660 Ti OC graphics card with 6 GB GDDR6 memory, and an Intel Core i7-9750H CPU running at 2.60 GHz under Windows 10 (64-bit). Results showed that denoising four EEG channels, each with a duration of one second, using the proposed CNN-LSTM-based model took an average inference time of 78 ms. However, the method was developed primarily for the offline processing of entire EEG sessions, leveraging high-performance computing resources. Therefore, no specific optimization for low-power or embedded deployment was performed, as the focus was on maximizing artifact reconstruction quality rather than minimizing computational complexity.  
Our research confirms the effectiveness of the proposed method for electrodes O1, O2, and Oz, enabling its use in BCI systems. During the study, we also recorded signals from the Cz channel, which does not contain SSVEP signals. Visual analysis demonstrated that artifacts were effectively removed from this channel as well. Crucially, the signal remained unchanged in regions without artifacts, thus preserving the original EEG structure. We are therefore confident that our method can also be effectively applied to denoising signals from other EEG channels. This capability would extend its practical application to other  
Figure 15: EEG signals after cleaning with the CNN-LSTM model for subject 24.  
EEG signal analysis domains, including epilepsy diagnosis, evoked potential analysis, and various clinical and research applications.
###### Abstract  
This study evaluated the effects of exercise interventions on sleep quality and weight loss through a systematic literature review and meta-analysis. A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and the Cochrane Library for publications up to December 2022. Only randomized controlled trials (RCTs) were included in the analysis. The Risk of Bias was assessed using the Cochrane Risk of Bias 2 (ROB 2) tool, and disagreements were resolved by consensus. Data synthesis and meta-analysis were performed using Comprehensive Meta-Analysis Version 4 (CMA version 4) software, with outcomes expressed as pooled effect sizes, odds ratios (ORs), and 95% confidence intervals (CIs). Seven RCTs encompassing 908 participants were analyzed. The meta-analysis indicated a significant improvement in sleep outcomes (OR = 3.93, 95% CI [2.04, 7.56], \(p<0.001\)). The combined aerobic and resistance exercise regimen showed the most substantial effects on sleep quality. Additionally, four of the seven RCTs included in the weight loss analysis indicated a significant improvement in weight loss (OR = 2.58, 95% CI [1.79, 3.71], \(p<0.001\)). Exercise interventions have a strong potential for improving sleep quality and weight loss in adults with obesity. Future studies should focus on developing optimized targeted strategies for sleep enhancement.  
obesity; exercise; RCTs; sleep quality; weight loss +
Footnote †: journal: Applied sciences  
1
# Practical Guide to Ultrasound Beam Forming: Beam Pattern and Image Reconstruction Analysis
## 6 Comparison among Different Beam-Forming Options  
A general comparison between the different techniques discussed thus far can be drawn. Figure 4 illustrates the different driving schemes for MLA, MLT, plane wave, diverging wave, and synthetic aperture beam forming. The transmit beams are represented in orange, and different shades of orange are used to highlight the multiple beams for MLT, and separate the beam profiles for plane and diverging wave beam forming, respectively. For MLA, the receive beams are also shown in shades of blue. The duration of the transmit phase is also emphasized for MLT. Table 1 summarizes the peculiarities of each modality. A comparison is made with standard line-by-line beam forming. A plus or minus sign means that the performance with respect to that specific imaging feature (one for eachcolumn) is improved or reduced, respectively. MLA and MLT beam forming is essentially trading spatial resolution for data acquisition rate, and can be generally applied to any array aperture. In the case of MLT especially, where focused beams are used in transmission, penetration depth is not lost compared to standard beam forming. Plane and diverging wave beam forming focus instead on achieving a very high data acquisition rate. Consequently, spatial resolution and penetration depth are affected. Moreover, these approaches substantially require a large aperture size. This is also true for synthetic aperture beam forming, where an increase in the number of transmitting elements leads to improved performance. The strength of synthetic aperture beam forming is certainly on the attainable spatial resolution, which is achieved at the expense of penetration depth and the data acquisition rate.  
As described in the previous sections, for every technique, these pros and cons can be mitigated by specific implementations. However, the rule that what one gains regarding particular feature implies a loss in performance with respect to the other features generally applies.
## 2 Materials and Methods
### Remote Supervised Telerehabilitation Session  
The methodology for HTR has been detailed in earlier publications [14; 16; 17]. In summary, all patients were provided with a specialized remote device designed for telem monitored and telesupervised exercise training--a telerehabilitation kit. This kit included a device for ECG monitoring (data from three precordial leads, which were transmitted to the monitoring center via a mobile phone network), a blood pressure measuring, and a weighing scale.  
The HTR protocol was divided into two phases: an initial phase, conducted at an outpatient center over five days, and a home-based phase lasting twelve weeks. The initial phase focused on conducting baseline clinical assessments, optimizing treatments, providing education, creating individualized exercise training plans, conducting five monitored educational training sessions, and performing psychological evaluations. The home-based phase comprised two main components for each session. First, the training consent procedure required patients to engage in a telephone consultation with a nurse. During this call, patients provided information about their current condition, symptoms, and medications and sent resting ECG and blood pressure data (if measurement was possible). Additionally, patients used a web- and mobile-based application (Remedizer) to store clinical data. The following data were recorded daily: blood pressure, heart rate, body temperature, fluid intake and output, LVAD pump speed, flow, pulsatility index, power, and International Normalized Ratio (INR). If no contraindications were identified, the nurse granted clearance for the patient to begin their training session. The second component involved the training session itself. Each patient's training program was pre-set in the telemonitoring system, specifying exercise duration, rest periods, and timing for ECG recordings. The device guided patients through their sessions with audio prompts, ensuring proper execution of the exercise plan. ECG recordings were automatically synchronized with the peak exercise period. After completing the session, patients transmitted the recorded ECG data to the monitoring center using the mobile phone network. These recordings were stored electronically and reviewed by physicians, who made final decisions based on the analysis. Telephone communication was also utilized to offer psychological support. To enhance safety during exercise, patients were encouraged to have a companion, usually a family member, present during training. These companions were trained in basic first aidprocedures by the medical team in the monitoring center and were instructed to call for medical assistance if needed while providing initial aid until professional help arrived.
# Antivirus Evasion Methods in Modern Operating Systems
## 3 Antivirus Detection Mechanisms
### Heuristic Detection  
[PERSON] in [20] defined heuristics as a set of rules applied to a program to determine if it contains a virus or not. [PERSON], [PERSON], [PERSON], and [PERSON] described heuristic-based detection as utilizing machine learning and data mining techniques to learn a program's intentions [18]. The learning model detects suspicious features found in new and modified viruses. Heuristic analysis is classified as static or dynamic. Static analysis compares the possible behavior of the decompiled code with the heuristics database, and dynamic analysis runs a program in an isolated environment to observe its operation [21]. The combination of static and dynamic heuristics allows for the assessment of new and modified threats without exposing the client's device. However, as [PERSON] and [PERSON] noted, it is impossible to create an algorithm that can definitively distinguish between a virus and an uninfected program with 100% accuracy. Heuristic-based detection also has its limitations. Static analysis may return a false positive, as it can find similarity between safe files and the heuristics database. Dynamic analysis may fail to detect a virus that requires certain conditions to be met before being launched. To address these limitations, combining heuristics with signature-based detection can eliminate these drawbacks.
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Cloudlet Computing
#### 2.2.1 Face Recognition  
[PERSON] et al. [31] used a mobile-Cloudlet-cloud architecture to develop a face recognition system. They demonstrated that the power of Cloudlet servers can reduce the overall response time of face detection compared to prior work.
## 5 New Trends and Future Research Directions in Enterprise Networking
### Hybrid and Multi-Cloud Networking  
Several enterprise firms are seeking ways to integrate services from multiple cloud providers in order to achieve flexibility, resilience, and performance optimization [37; 72]. This is apparent in the shift towards multi-cloud and hybrid networking. Hybrid networking promotes data sovereignty and low-latency access to essential applications by combining cloud services with on-premises infrastructure [57]. Networking solutions are developing as businesses implement multi-cloud strategies to offer smooth connectivity and control across various data centers and cloud providers. This will minimize latency and improve data security. Cloud computing, which integrates traditional technologies, enables computer functionalities to be accessed and managed remotely [92].  
Therefore, cloud computing in hybrid, multi, public, or private connectivity modes permit computations and data processing to take place at data centers. Data centers possess significant amounts of data and computational capacity to transmit between different locations [100]. Consequently, clients only pay for storage, data transfer, or processing resources utilized at any instant time. This allows organizations to focus on other business  
Figure 8: Network automation structure.  
goals without any concern about maintaining the computer hardware and infrastructure. Generally, as illustrated in Figure 9, cloud computing technologies can be deployed in the form of infrastructure-as-a-service (IaaS) [101], software-as-a-service (SaaS) [102], and platform-as-a-service (PaaS) [93] mode. These deployments use the respective infrastructure, software, and platform from the cloud to provide services in the form of rent to end users.