esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methodology
### Label-Free Quantitative Proteomics  
The comparative label-free quantitative proteomics of _E. coli_ cells expressing LgAC incubated in the dark and illuminated with BL was performed. The cells were lysed using lysis buffer (8.0 M urea in 25 mM ammonium bicarbonate) supplemented with protease inhibitors (Roche Applied Science, Penzberg, BY, Germany). The total protein in the lysate was quantitated using Brad-ford assay (Bio-Rad, Hercules, CA, USA). The sample was first reduced using 5 mM tris (2-carboxyethyl) phosphine and later alkylated using 50 mM iodoacetamide. The sample was then digested using trypsin (1:50 trypsin: lysate ratio) for 16 h at 37 \({}^{\circ}\)C. The digested sample was cleaned using a C18 silica cartridge (The Nest Group, Southborough, MA, USA) according to the manufacturer's protocol and dried using a speed vac. The dried pellet was resuspended in buffer A (5% acetonitrile, 0.1% formic acid) and analyzed on nano-LC/MSMS. Label-free sample was analyzed using an EASY-nLC system (Thermo Fisher Scientific, Waltham, MA, USA) coupled to an LTQ Orbitrap-Velos mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA) equipped with a nano-electrospray ion source as per the method described in [PERSON] et al. [23]. Four biological replicates were processed for each sample, and the RAW files were analyzed with MaxQuant (version. 1.4.1.2) against the _E. coli_ UniProt reference proteome database. Functional annotation and pathway validation of differentially expressed proteins (DEPs) were conducted using the KEGG pathway analysis ([[http://kobas.cbi.pku.edu.cn/anno_iden.php](http://kobas.cbi.pku.edu.cn/anno_iden.php)]([http://kobas.cbi.pku.edu.cn/anno_iden.php](http://kobas.cbi.pku.edu.cn/anno_iden.php))). Protein networking of selected DEPs were performed using String version 11 (Academic Consortium 2020; [24]) and the output was analyzed with Cytoscape 3.7.2 (San Diego, CA, USA) using the betweenness centrality algorithm [25]. A Venn diagram representing the distribution of proteins identified by label-free comparative quantitative proteomics was created using Venny 2.1 (Calle Darwin, MD, Spain) [26]. A heat map was constructed with Multi Experiment Viewer (MeV 4.9.0, Rockville, MD, USA) to show the comparative expression profile of differentially expressed proteins [27]. A pie diagram was created utilizing Past 3.11 (Natural History Museum, University of Oslo, Sars gt., Oslo) [28].
# A 100 Gb/s Quad-Lane SerDes Receiver with a PI-Based Quarter-Rate All-Digital CDR
## 2 Proposed Multi-Lane SerDes Receiver Architecture
### Proposed All-Digital PI-Based Quarter-Rate CDR Architecture  
Figure 4a shows the block diagram of the proposed all-digital 25 Gb/s PI-based quarter-rate CDR architecture. The CDR is composed of four data samplers, four edge samplers, four phase selectors (PS), four phase interpolators (PI), and a PI controller. The proposed CDR adopts a quarter-rate architecture to sufficiently widen the timing margin of the input sampler and to simplify the clock distribution network with much a lower sampling clock frequency. To this end, four data samplers (#1-#4) are arranged in parallel on the input stage, and the four data samplers are operated in synchronization with four Dclks (Dclk0-Dclk3) operating at quarter-rate (= 6.25 GHz), respectively. Additionally, 2x-oversampling technology is used to find the transition position of the input data stream. To this end, four edge samplers operating at quarter-rate are arranged parallel to the input. As shown in Figure 4b, when the CDR is locked, the four data clocks (Dclk0-Dclk3) are aligned to the middle of each output data (D\(<\)0\(>\)-\(-\)D\(<\)3\(>\)), respectively. For this operation, the PI controller generates the MA\(<\)1:0\(>\), MB\(<\)1:0\(>\), and PI\(<\)8:0\(>\) signals to control the PS and PI. The PS and PI generate the four Dclks (Dclk0-Dclk3) required for the four data samplers, and the adjacent Dclks are out of phase with  
Figure 3: Proposed 100 Gb/s quad-lane SerDes receiver architecture.  
each other by 90 degrees. The PS and PI also generate the four Eclks (Eclk0-Eclk3) required for the four edge samplers.  
By applying this 2x-oversampling quarter-rate CDR technology, demultiplexed 4-bit parallel data (D\(<\)3:0\(>\)) can be restored from the 25 Gb/s serial random input data. This CDR structure has the  
Figure 4: (**a**) Block diagram of the proposed all-digital PI-based quarter-rate CDR (**b**) and the CDR operation in locked state.  
advantage that the order of the recovered 4-bit parallel data (D\(<\)3:0\(>\)) is always constant. This means, for instance, that the data sampler #1 clocked with Dclk0 always outputs D\(<\)0\(>\) first, and the data sampler #2 clocked with Dclk1 always outputs D\(<\)1\(>\) first. This function is performed through the PI controller with the initial tracking mode.  
On the other hand, the order of the 4-bit parallel data output by the conventional quarter-rate CDR with 1:4 demultiplexing may not be constant. This means that in the conventional method, the data sampler #1 can output any data from D\(<\)0\(>\) to D\(<\)3\(>\) first. Thus, the conventional quarter-rate CDRs may require additional reordering circuits to set the order of the parallel data stream that is randomly output, which can have the disadvantage of increasing the latency and area overhead. In a packet protocol-based serial interface, the order of deserialized data can be arranged in a training mode with an additional preamble overhead of increased latency.
# Financial Optimization of a Solar-Driven Organic Rankine Cycle
## 1 Introduction
The previous literature review indicates the high interest of the ORCs driven by PTC. In this direction, this work examines a system with PTC, a sensible storage system and ORC operating with toluene. The system is examined with a developed dynamic model in the programming language FORTRAN. The thermodynamic analysis has been performed with a developed model in Engineering Elution Solver (EES) [19]. The system is investigated for the location of Athens in Greece for operation during the year. The novelty of this work is the optimization of these systems under different criteria and the comparison of the different optimum designs. Furthermore, there is an extra multi-objective optimization procedure that is able to determine the global optimum design of the solar-driven ORC. The difference of this work compared to previous studies is based on the detailed optimization analysis and on the use of the proper sunny days for every month in order to find representative results. The comparison of the different optimum designs is critical in order to know how these systems have to be designed in accordance with the evaluation criteria in any case. The emphasis is given in the optimization with different financial criteria, such as payback period, net present value, and the levelized cost of electricity. Moreover, energy efficiency is used as an optimization criterion in this work. The optimization variables are the collecting area and the storage tank volume, which are the main parameters for the solar field design. Lastly, it is also important to state that a parametric analysis is conducted before the optimization procedure, in order to make clear the way that every parameter influences the financial and energy indexes.
## 5 Discussion
### Towards Robust and Trustworthy Research  
Despite recent advances, several challenges are identified in the development and application of new imaging methods. Concerning data, key challenges include the limited availability of large, annotated datasets and variability in imaging protocols across institutions. Future development efforts should focus on building open multi-center datasets to enhance generalizability. At the same time, data privacy must be secured to ensure ethical compliance in all stages of research. Addressing this concern requires the adoption of strict data governance protocols for all parties involved in the research. Furthermore, fairness-oriented methods can help mitigate bias and good performance in underrepresented conditions such as rare kidney diseases [89; 90; 91]. In addition, privacy-preserving approaches, such as federated learning, can improve transparent but responsible development and application in medical research [92].  
In terms of study quality and reproducibility, researchers should consider following guidelines, such as \"Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD)\" [93] and the \"Checklist for Artificial Intelligence in Medical Imaging (CLAIM)\" [94], as presented in the following study [35]. In the future, novel and updated policies and guidelines can be expected at institutional, local, and global levels, and researchers in ML and AI models should stay informed about these developments to ensure compliance and methodological rigor. For example, updated versions of both CLAIM (2024 Update [95]) and TRIPOD (2024 AI Update [96]), offer revised recommendations for reporting clinical prediction models that use regression or machine learning methods. Furthermore, studies should carefully report preprocessing steps, data augmentation methods, hyperparameter tuning strategies and selected values, validation practices, and specific methods used. In particular, details about technologies should be reported, including programming languages, frameworks, and libraries used. For example, when considering technological solutions, researchers should evaluate specialized deep learning frameworks optimized for medical imaging applications. Most of the studies in this review either did not utilize or failed to report the use of such libraries. The only exceptions were [PERSON] et al. [35], who employed MONAI--a PyTorch-based framework--and [PERSON] et al. [69], who utilized NiftyNet, a TensorFlow-based platform. Finally, when possible, open-source code should be made available.  
Ultimately, the use of interpretable AI methods can improve trust, fairness, and explainability of the models currently used in medical research and application in clinical settings [97]. Those methods may also help address current limitations in dealing with SPECT and planar scintigraphy modalities. If the application of deep learning models yield limited success and negative results remain unreported, interpretable AI methods could help with the identification of underlying limitations, thereby guiding further model refinement and advancement.
# SALMA: A Novel Middlebox Infrastructure System Based on Integrated Subnets
## 2 Motivation
### Survey Details  
We conducted a survey with around 30 enterprise networks as participants. The goal of this survey was to demonstrate the challenges and limitations in existing deployments in today's networks. We collected the number of middleboxes, the average number of employees assigned to every middlebox, and the costs of different middlebox platforms. The studied enterprises are in various business sectors. They are Internet Service Providers (ISPs), telecommunications companies, academic institutions, and financial organizations. For security reasons, we were not allowed to reveal the identities of businesses that participated in the survey. Thus, we classified the survey results based on enterprise size (i.e., large, medium, and small). Our dataset included 4 small enterprises (fewer than 1000 hosts), 11 medium enterprises (between and 1000 and 10,000 hosts), and 15 large enterprises (10,000 to 100,000 hosts). Figure 1 shows the number of middleboxes deployed in the three classes of networks of the participating enterprises. The type of middlebox is abbreviated on the x-axis as follows: WO stands for WAN optimizer, PX stands for proxy, LB stands for load balancer, VS stands for VPN server, IDS stands for intrusion detection system, and FW stands for firewall.
###### Abstract  
We developed a health-monitoring methodology for high-temperature steam pipes that estimated the life prediction of creep-fatigue interaction by directly measuring the displacement of hot parts. Three different methods (boiler code, design stress, and operating stress) were used to estimate the stress of the high-temperature pipe system. As a theoretical approach, the German boiler standard code calculates the stress according to the pipe shape, while design stress, which is also called allowable stress, was determined by a function of the operating temperature. The operating stress was immediately calculated using the surrogate model, with maximum displacement measured using the 3D displacement measurement system. To achieve the surrogate model, the stress was estimated by the pipe-stress analysis under the given displacements, and the surface-response model was developed to relate the stress and displacement. We showed that those methods are efficient methods to predict the stress and are applicable in health-monitoring methodology. Finally, the creep life and the low-cycle fatigue life were investigated using the Larson-Miller parameter equation, as well as the Smith, Hirschberg, and Manson equations. Our proposed monitoring system can be used to predict the fatigue and creep life of high-temperature steam pipes in real time, and we believe that the system can be applied to actual maintenance in thermal power plants.  
main steam pipe; surrogate model; creep-fatigue interaction; life prediction
## 4 Research Challenges and Opportunities
### Oceanographic Sensors Protection  
In marine environments, there are over 4000 organisms related to fouling problems [75]. According to their sizes, organisms can be classified into micro-organisms (or so called biofilms, slimes, and micro-fouling) and macro-fouling [72]. Biofouling development on a sensor surface is subject to several chemical, physical and biological factors such as pH, dissolved oxygen, temperature, light, location depth, conductivity, organic material and hydrodynamic conditions. When oceanographic sensors are immersed in seawater, they are susceptible to biofouling problems which often lead to the long-term accuracy issues of marine environmental sensor measurements. Since the marine environment is aggressive and the seawater is corrosive, oceanographic sensors should take appropriate fouling protection measures.  
The biofouling protection for oceanographic sensors may be divided into three techniques according to their different actions: wipers mechanisms, copper corrosion mechanisms, and chlorine evolution mechanisms [72].  
1. _Wiper mechanisms:_ A biofouling protection system based on wipers is a purely mechanical method. It is an effective biofouling protection technique as long as the sensor head has a suitable shape for wiper cleaning and the wipers are in good condition.
2. _Copper corrosion mechanisms:_ A copper corrosion mechanism is an effective biofouling protection method to protect the sensitive sensor head, but the protection mechanism is not easy to apply to existing sensors and the cost is relatively high.
3. _Chlorine evolution mechanisms:_ A biofouling protection system based on a chlorine evolution mechanism uses bleach or chlorine generation by seawater electrolysis. Moreover, this protection mechanism is easily adapted to existing sensors and the cost is relatively low.  
Besides the abovementioned three biofouling protection techniques, there are some other interesting methods which promise effective results coming from research laboratories [76,77]. However, it is very difficult to implement these methods in the real sea environment.  
Biofouling protection for oceanographic _in-situ_ sensors is a very difficult problem. The ideal biofouling protection for oceanographic sensors should take into account six aspects: low cost, low power consumption, easy to install on existing sensors, no or low impact on measurement precision and the environment, long lifetime and robustness against aggressive conditions. Therefore, researchers and manufacturers should further study and explore the biofouling protection mechanisms for marine environmental sensors.
# KRT-FUAP: Key Regions Tuned via Flow Field for Facial Universal Adversarial Perturbation
## 3 Methodology
### Generation of UAPs  
We use the key regions mask M\({}_{\text{f}}\) obtained in the previous section and learnable noise n to generate the final universal adversarial perturbation v. To enhance the stealthiness of the perturbation, we set the intensity of the superimposed perturbation in the non-key regions to be half of the intensity in the key regions as a way to ensure that the effective noise is concentrated in the key regions. The final generated adversarial sample x\({}_{\text{adv}}\) is formulated as follows:  
\[\text{x}_{\text{adv}}=\text{x}+\text{v}=\text{x}+\text{Mask}\{\hat{\text{n}},\text{M}_{\text{f}}\}\text{,} \tag{7}\]  
where v denotes the generated universal adversarial perturbation, and \(\hat{\text{n}}\) is the learned noise. Mask\(\{\cdot\}\) denotes the function that generates the perturbation using the mask, we normalize the mask to be within the interval \([1/2,1  
where n and \(f_{\text{flow}}\) denote the learnable noise and flow field. \(\mathcal{L}_{\text{adv}}\) denotes the adversarial loss, \(\mathcal{L}_{\text{ste}}\) denotes the stealthiness loss, and \(\lambda\) controls the balance between them, and \(\xi\) is a parameter controlling the size of the perturbation. Adversarial examples are generated using the final learned noise h and flow field \(\hat{f}_{\text{flow}}\) to deceive the entire face recognition model. The fooling rate \(\delta\) denotes the probability of a successful attack using universal adversarial perturbations. The pipeline of KRT-FUAP is provided in Algorithm 1.  
```
0: Preprocessed training set X, random noise n, face recognition network \(\text{F}(\cdot)\), fooling rate \(\delta\), \(l_{\infty}\)-norm \(\zeta\) of perturbation, and decision threshold t
0: Universal adversarial perturbation v and learnable flow field \(f_{\text{flow}}\).
1: Initialize \((\text{n},f_{\text{flow}})\gets random\)
2: Obtain the original mask \(\text{M}_{\text{o}}\) from X
3:while fooling rate \(<\delta\)do
4:for\(x_{i}\) in Xdo
5: Spatial transformation: \(\text{M}_{\text{f}}\leftarrow\text{Flow}\{f_{\text{flow}},\text{M}_{\text{o}}\}\)
6: Obtain v \(\leftarrow\text{Mask}\{\text{n},M_{\text{f}}\}\)
7:if Similarity\(\{F(x_{i}+\text{v})\), \(F(x_{i})\}>\text{t}\)then
8:\((\Delta\text{n},\Delta\text{f}_{\text{flow}})\leftarrow\text{arg}\min_{(\text {n},f_{\text{flow}})}\left\|(\text{n},f_{\text{flow}})\right\|_{2}\)
9: s.t. Similarity\(\{F(x_{i}+\text{v})\), \(F(x_{i})\}\leqslant\text{t}\)
10: Update the noise: n \(\leftarrow\) n + \(\Delta\text{n}\)
11: Update the flow field: \(f_{\text{flow}}\gets f_{\text{flow}}\) + \(\Delta f_{\text{flow}}\)
12:endif
13: Clip v to maintain the \(l_{\infty}\) norm restriction
14:endfor
15:endwhile
16:return v
```  
**Algorithm 1** The Algorithm of KRT-FUAP
###### Abstract  
This paper is focused on the numerical evaluation of the equivalent damping ratio (EDR) given by a dissipative wood-based roof diaphragm in the seismic retrofitting of single-nave historical churches. In the design phase, the EDR could be a key parameter to select the optimal roof structure configuration, thereby obtaining the maximum energy dissipation. In this way, the roof structure works as a damper to facilitate a box behavior of the structure during the seismic response. The EDR measures the energy dissipated by the nonlinear behavior of the roof's steel connections and masonry walls during seismic events. In a preliminary retrofitting design phase, an initial implementation of the geometries of the walls and the chosen geometry for the roof is carried out by adopting an equivalent frame model (FEM) with inelastic rotational hinges for the nonlinear properties of the masonry walls and inelastic shear hinges for the nonlinear behavior of the roof's steel connections. Since, for historical churches, the transversal response under seismic events is the worst situation for the single-nave configuration, the earthquake is applied as transversal accelerations. In this way, the damped rocking of the perimeter walls due to the dissipative roof diaphragm can be described in terms of a hysteretic variable. By varying the value of the hysteretic variable, possible configurations of the roof diaphragm are tested in the design phase, considering the different shear deformation values of the inelastic hinges of the roof. Under these hypotheses, the EDR is evaluated by performing nonlinear Time History analyses based on the cyclic behavior of the inelastic hinges of the roof, the strain energy contribution due to the walls, and the lateral displacements of the structure. The EDR values obtained with the Time History method are compared with those obtained by applying the Capacity Spectrum Method by performing nonlinear static analyses, either for the coefficient method of FEMA 356 or the equivalent linearization technique of ATC-40. The EDR evaluations are performed by considering the following different hysteretic behaviors of the roof's steel connections: the skeleton curves with stiffness degradation and the trilinear model with strength and stiffness degradation. Finally, the variation in the EDR values as a function of the hysteretic variable is presented as well so to evaluate if the maximum EDR value corresponds to the optimal value of the hysteretic variable able to reduce the lateral displacements and to contain the shear forces acting on the roof and the facade under a safety limit.  
historical buildings; churches retrofitting; cross-lam timber structure; equivalent damping; seismic vulnerability; nave transversal response +
Footnote †: journal: Applied sciences  
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