esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis of Acrylamide  
The analytical method employed for acrylamide determination was based on the KMFDS method with minor modifications (KMFDS, 2019). The oil sample (1 g) was weighed into a 50 mL conical tube, and double-distilled water (9 mL) and acrylamide-d3 (ISTD, 1 mL, 200 ng/mL) were added to the tube. The extraction was conducted by shaking the tube at 250 rpm for 20 min. Then the sample was centrifuged at 26,912\(\times\)\(g\) for 5 min. The supernatant was filtered through a 0.45 \(\upmu\)m PVDF syringe filter. The sample purification was performed with two SPE columns in a series. An SPE column (Strata-X) was conditioned with methanol (3.5 mL) followed by water (3.5 mL). An aliquot of the sample (1.5 mL) was loaded onto the SPE cartridge, followed by water (0.5 mL). The eluent was discarded. Then, an additional 1.5 mL water was passed through the column, and the eluent was collected. Another SPE column (Bond Elut AccuCAT) was conditioned with 2.5 mL methanol and 2.5 mL water in a series. An aliquot (0.5 mL) of the eluate collected from the Strata-X SPE column was loaded on the preconditioned Bond Elut AccuCAT SPE column. Then, an additional 1 mL of the eluate from the Strata-X column was introduced onto the Bond Elut AccuCAT SPE column. The eluent was collected in a vial. High-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was carried out using a Shimadzu HPLC system coupled to a Shimadzu MS8040 MS/MS system (Shimadzu, Kyoto, Japan) in a positive electrospray ionization mode (5000 V). The mobile phase used was a solution consisting of 0.1% acetic acid in 0.5% methanol in water (MS grade). The multiple reaction monitoring (MRM) transitions for the quantitation and qualification of acrylamide were \(m/z\) 72 \(>\) 55 and \(m/z\) 72 \(>\) 27, respectively. The multiple reaction monitoring (MRM) transitions for the quantitation and qualification of acrylamide-d3 (ISTD) were \(m/z\) 75 \(>\) 58 and \(m/z\) 75 \(>\) 29, respectively. A C18 column (Kinetex polar C18 column, 150 mm \(\times\) 2.1 mm i.d., 2.6 \(\upmu\)m particle size, Phenomenex) was used for the separation of acrylamide. The quantitation was based on a standard calibration curve obtained from peak areas vs. concentrations of the authentic acrylamide.
## 1 Introduction
At the same time, observation with high spatial resolution is required [40], because many sugarcane fields are small (<2 ha). Therefore, both high-frequency and high-spatial-resolution data are required for classifying crop types and practices in agricultural land that includes sugarcane fields [39]. Recently, satellite constellations constructed by launching a large number of identical small satellites have attracted attention as systems that achieve both high temporal (every day) and high spatial resolution (3 m). Planet Labs, Inc. operates the largest satellite constellation, with Dove nano-satellites collecting multi-band (blue, green, red, and near-infrared) images of the entire land surface of the Earth every day; collection capacity is 200 million km\({}^{2}\)/day, based on a full constellation of approximately 130 satellites [41]. Owing to the huge number of images obtained, the opportunities for change detection and surface characterization are enormous, which encourages application of this approach to agricultural monitoring [42; 43; 44; 45; 46]. Understanding the crop growth pattern revealed by high-frequency and high-resolution observations may reveal the optimal observation dates for crop types and practices classification. The purpose of this study was to classify crop types and practices (i.e., sugarcane (ratoon), sugarcane (spring planting), sugarcane (summer planting in 2018), sugarcane (summer planting in 2019), pasture, purple yarn, pineapple and agricultural facility) on a subtropical island by using crop growth patterns estimated from satellite constellation data of high temporal and spatial resolution and to identify the most effective dates for undertaking the classification.
## 3 Experimental Process and Results
### Model Training  
The experiments were implemented in Python using PyCharm 2023.1.3 with Python 3.10.0, conducted under a Windows 11 environment. The hardware configuration included an Intel Core i9-14900K 3.20 GHz CPU (Intel, Santa Clara, CA, USA) and an NVIDIA GeForce RTX 3090 GPU (NVIDIA, Santa Clara, CA, USA). The implementation utilized PyTorch 1.13.1 with compatible CUDA 11.7.  
Training was performed over 200 epochs at decimation ratios of 0.1, 0.3, and 0.5, with a learning rate of 0.0001, determined as the optimal value via validation set tests across candidate values (including [PERSON]'s default 0.001). Evaluation metrics included SNR, PSNR, MRSE, and SSIM. To enhance the interpretability of these metrics in result assessment, Table 1, Explanation of evaluation metrics, provides detailed descriptions of the four evaluation metrics. To clearly demonstrate each model's performance under different noise conditions, observations were conducted at each epoch to visualize model performance dynamics.
## 6 Discussion
### Main Benefits of the Architecture  
Our research demonstrates that the suggested methodology serves as an effective approach for digitalization tools aimed at reducing the carbon footprints of vessels and enhancing their operational efficiency. Additionally, a significant advantage of the proposed framework lies in its ability to deliver a practical solution for onboard applications. Major challenges, such as data quality and the need for substantial computational power, are addressed through the distribution of tasks among self-contained agents, as outlined in this study. The primary innovations that facilitate this comprehensive solution are summarized below.  
Novel technologies for data collection and fusion. 1. Handling data nearer to their origin can resolve various challenges related to the data size, volume, and speed. Additionally, organizations can gain advantages in several areas: (a) enhanced business agility--the quick creation and implementation of fog applications, along with the provision of Mobility as a Service by equipment manufacturers; (b) improved cybersecurity--similar strategies can be applied to safeguard fog nodes as in other segments of the IT infrastructure; (c) better privacy management--sensitive information is processed locally rather than transmitted to the cloud; (d) reduced operational costs--local processing helps to conserve the network bandwidth.  
Artificial intelligence for predictive performance and maintenance management. The maritime industry faces challenges like vessel malfunctions, poor marine system performance, ship emissions, and rising operational costs due to system degradation. While extensive historical data from ship monitoring systems exist, they are often too complex to address the current environmental issues. Although planned maintenance systems (PMS) are widely used, only 2% of vessels adopt condition-based maintenance (CBM). Module 1 will concentrate on data collection, machine learning, and advanced modeling strategies.  
Automated real-time lifecycle management decision support. While several companies have initiated the provision of vessel performance and efficiency monitoring, these offerings remain proprietary and non-reusable. To date, the literature has only presented conceptual descriptions of relevant methodologies. The proposed solution stands out as the sole collaborative option, accessible to all participants in the supply chain, with a business model centered on advanced applications utilizing AI and ML techniques.
## 7 Experiment Design
### Systems
#### 7.2.2 Baseline System  
To evaluate the hypothesis Section 6. RAAVE is compared with a baseline system which in this case is a simple video player. Admittedly, the choice of using a simple video player as a baseline is an unusual one. Traditionally researchers compare their approach with an approach from the state of the art. However, it was not feasible for the current experiment. The proposed approach (RAAVE) automatically transforms video into a multimedia document i.e., RAAVE transforms video into something more than a video. State of the Art (SOTA) approaches that do that are Hypervideo based approaches (detailed in Section 2.1.1). As detailed in the SOTA review (Section 2), Hypervideo systems require human curation by utilizing specialized authoring environments and video players. Since RAAVE utilizes automatically extracted multimodal features, therefore, it is not feasible to have a direct comparison with Hypervideo systems due to this fundamental difference in the approaches. Moreover, while Hypervideo systems do represent multimodal information with video content, they utilize supplementary content from sources other than the source video, while the goal of the RAAVE approach is to utilize the content within the source video in novel ways to enhance the user experience thereby making a direct comparison not feasible due to the fundamental difference in the approaches.  
Another reason to choose a simple video player as a baseline goes to the main goal of the presented paper. As described in Sections 1 and 2 despite all the research; the current view on video content is essentially a continuous stream of images with or without an audio component. As RAAVE proposes to consider video content as a diverse content source by transforming it automatically, it is natural to compare the transformation based approach with the continuous stream of images with a parallel audio component i.e., a regular video player as a baseline which is also ubiquitous due its familiarity and common usage.  
Figure 3 shows the baseline system which users utilize to interact with the video. It contains a standard video player with pause/play button and a scrubber so that user can drag it across to watch a different portion of a video. Underneath the video player is information regarding the start and stop time of different segments of the video. It is provided to aid the user in performing the answer search task.
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 4 Simulation and Analysis
### Position Estimation Using the Proposed Method  
In this section, we demonstrate that the scatterer's position can be obtained through two frequency vectors. The simulation parameters are the same as Section 4.1. We first use one set \(\mathbf{\omega^{\prime}}\) to search for the position of the scatterer (3, 0), which is shown in Figure 2(a). Obviously, the scatterer (3, 0) cannot be distinguished in the polar plane. In Figure 2(b), the red point corresponds to the position of the scatterer (3, 0) estimated by using two frequency sets. Likewise, we use two frequency sets to estimate the position of six scatterers via (26), as shown in Figure 4. It is clear that the coordinates of all scatterers are correctly estimated.
## 6 Results and Analysis
### Communication Overhead  
To appraise the message exchanging overhead of the presented protocol, the data that is transmitted between the participating parties and the TS during the identification and session key generation phase need to be considered. It is identified that the secret key of size 160-bit using ECC can yield equal security to a 1024-bit RSA secret key. For the evaluation phase, we believe that the resultant one-way hash function, participant's identities, timestamp, and nonce number are 160 bits in length. The end party sends three parameters which are \(\{XA,\ PA,\ T1\}\) to the TS for authentication, and their lengths are \((160\times 3)\ =\ 480\) bits. On the other side, in the party verification phase, the TS sends an encrypted packet \(CP_{A}\) using a 160-bit ECC key to the end party for session key establishment and its length is \((160\times 2)\ =\ 320\) bits. On the basis of these computations in the proposed SAPS protocol, Patient (A) and Doctor (B) have the same communication cost during their end user confirmation and secure key generation processes. On the other hand, existing ECC- and RSA-based schemes have greater communication overheads, as depicted in Figure 5.  
TPKE [15] is based on RSA which requires 1344 bits for the authentication method and 1344 bits for server to end party communication. Similarly, A-TMIS [16] consumes the same communication cost as TPKE for the whole procedure. In the login phase of [PERSON] et al.'s protocol for EAKA [40], the server requires 640 bits of communication cost, and the server to the user requires 480-bits. AAS [41] requires 640 bits and 480 bits communication from the user to server and the server to user, respectively. By the above analysis, the proposed scheme has less communication cost by ensuring user anonymity.
# Evaluation of Mixed Traffic Flow Efficiency and Safety on Hard-Shoulder-Running Freeways
## 3 Results
### The Traffic Efficiency of HSR with CAVs  
As shown in Figure 4a,c,e, without HSR, the traffic flow efficiency does not exhibit an immediate enhancement as the penetration rate increases. Instead, a notable improvement in the overall traffic flow efficiency is observed only when the penetration rate attains a specific threshold. The thresholds are 0.5, 0.3, and 0.1, corresponding to the free-flow, saturated flow, and congested flow conditions, respectively. After implementing the HSR as shown in Figure 4b,d,f, the overall traffic efficiency of the study area exhibits a discernible increase with the augmentation of the penetration rate of CAVs, regardless of the prevailing traffic flow conditions (including the free-flow, saturated flow, and congested flow scenarios). As shown in Figure 4b,d, when the hard shoulder is open for traffic in both free-flow and saturated flow conditions, we find that at the same penetration rate of CAVs, the higher the speed limit set for the hard shoulder, the more efficient the traffic flow becomes. In contrast, under congested traffic conditions, an interesting observation emerges. Within a penetration rate range of 0.4 to 0.7, the traffic efficiency achieved with a speed limit of 120 km/h does not necessarily surpass that achieved with a speed limit of 80 km/h. This finding underscores the complexity of traffic dynamics in congested scenarios and highlights the need for nuanced traffic management strategies that consider varying conditions.  
Figure 4: The values of traffic efficiency metrics at different penetration rates of CAVs in different scenarios of HSR. The black line represents the values of traffic efficiency without HSR. The red, yellow, and green lines represent the traffic efficiency when the maximum speed limits on the hard  
shoulder are set at 120 km/h, 80 km/h, and 60 km/h, respectively, during the opening of the hard shoulder. (**a,b**) show the traffic efficiency at different penetration rates of CAVs in the free flow; (**c,d**) represent the traffic efficiency at different penetration rates of CAVs in the saturated flow; and (**e,f**) show the traffic efficiency at different penetration rates of CAVs in the congested flow.
## 3 Results and Discussion  
For each 10 \(\upmu\)s simulation time delivered through a local Backward Euler solver with a sampling period of 10 ps, the MATLAB simulation runs for approximately 1 min. The results are recorded with the virtual spectrum analyzer on the communication central frequency peaks using the maximum and minimum hold trace option. This method delivers the maximum and minimum received signal strength (RSS) for the simulated GFSK signal. On the experimental side, 100 consecutive frames transmitted at 0 dBm are measured through their RSSI, BER and PER. It needs to be noted that for the four designed  
Figure 5: Experimental test setup for 868 MHz and 2.4 GHz communication over the DC bus composed of four Li-ion cells organized in parallel (**A**) and in series (**B**).  
experimental setups, the BER and PER were zero, therefore the only transmission metrics were provided by the RSSI.  
To illustrate the RSS and RSSI for different coupling capacitances alongside the graph's markers showing the average value, the error bars are used to illustrate the RSS or RSSI highest recorded value (the bar's upper end) and lowest peak (the bar's lower end).  
Consequently, the error bars show experimental data intervals' minimum and maximum ends [70], although referring to them as the min-max range bars [71; 72] would be more clear and therefore it will be used in describing this study's results. The dashed line shows a possible linear relationship between the individual measurements and it was plotted to outline the value's association with a communication frequency or the other.
## 2 Inter-DCI Link Modeling and OP Estimation
### MCF Modeling and ICXT Generation
The parameter \(\xi_{m}\) can vary between \(0\) and \(1\)[13]. The overall ICXT field results from the sum of the ICXT fields corresponding to each one of the interfering cores at the output of the MCF. Each ICXT field induced by core \(m\) that interferes in the signal at the output of the core \(n\) in the two polarization directions, \(c_{XT,m,x}(t)\) and \(c_{XT,m,y}(t)\), is given by [13]  
\[c_{XT,m,x}(t)=c_{m,x}(t)*\mathcal{F}^{-1}[F_{xx}(\omega)]+c_{m,y }(t)*\mathcal{F}^{-1}[F_{yx}(\omega)]\] \[c_{XT,m,y}(t)=c_{m,x}(t)*\mathcal{F}^{-1}[F_{xy}(\omega)]+c_{m,y }(t)*\mathcal{F}^{-1}[F_{yy}(\omega)] \tag{4}\]  
where \(*\) stands for convolution and \(\mathcal{F}^{-1}\) stands for the inverse Fourier transform. The DP-DCM has been developed to keep the complexity and time of simulation of ICXT at acceptable levels. In such a model, the evolution of the ICXT impact on the system performance is evaluated in time fractions with a much shorter duration than the ICXT decorrelation time [20]. Those time fractions are separated by time intervals longer than the decorrelation time of ICXT. This means that, from time fraction to time fraction, the ICXT is uncorrelated and, within each time fraction, the ICXT is totally correlated. For this reason, the ICXT in each time fraction corresponds to an independent set of RPSs, which we call MCF realization [20]. Therefore, the different MCF realizations are obtainedby generating randomly different sets of \(N_{p}\) RPSs from each interfering core. In each iteration of the Monte Carlo (MC) simulator, one MCF realization is generated, and the symbols of the PAM4 signal transmitted in each interfering core are randomly generated. For equal powers at the input of the CUT and each interfering core and identical loss, the ratio between the mean ICXT power per interfering core and the mean power of the signal, at the output of the interfered core \(n\), called the ICXT level per interfering core, is given by \(X_{c}=N_{p}|K_{nm}|^{2}\)[13].
# Erosion Resistance Properties of Iron-Carbon Composite Plating to Molten Lead-Free Solder
###### Abstract  
The use of Sn-3 mass%Ag-0.5 mass%Cu lead-free solder (SAC305) has become common. Since SAC305 has a higher content of tin than conventional tin-lead eutectic solder, erosion of the Fe plating layer used in the solder iron tip and the point soldering machine nozzle frequently occurs. In this study, to prolong the life of the Fe plating layer, the applicability of composite plating in which a carbon-type filler is compounded with Fe was studied. Graphite and a multi-walled carbon nanotube (MWCNT) were used as filler materials in the composite plating layer. For both Fe-graphite and Fe-MWCNT composite plating layers, solderability testing and erosion-resistance testing were carried out. In the solderability test, although the spread rates of SAC305 to both Fe-graphite and Fe-MWCNT plating layers slightly decreased compared to the Fe plating layer, SAC305 solder was not repelled against both plating layers. In the erosion-resistance test, the Fe-MWCNT composite plating layer performed the best with the least erosion depth. The erosion depth of the Fe-graphite composite plating layer and the Fe plating layer were 10 and 100 times larger than that of the Fe-MWCNT composite plating layer, respectively. It was confirmed that the diffusion of Fe into molten SAC305 could be greatly reduced due to the composing carbon filler in Fe.  
erosion resistance; lead-free solder; solder iron tip; composite plating Article
## 1 Introduction
The instability of microbial phytases to the acidic pH (pH 2-3) of poultry stomachs in the presence of pepsin is an apparent disadvantage of such technologies. Moreover, the optimum pH values for the phytase classes PhyA (pH 2.2 and pH 5.2) and PhyC (pH 5.5) do not coincide with the pH of the poultry gastrointestinal tract where phytate hydrolysis occurs, namely the duodenal pH (6.4-6.6), the small intestine pH (6.5-7.0), and the colon pH (6.5-7.5) [18]. Consequently, the residual phosphorus in the faeces, even after the phytase action, ranges within 40% to 60% of the initial amount in the feed. A significant increase in the phytase efficacy can be achieved if there are used either phytases with an optimum pH ranging within 6.0-7.5 or encapsulated phytases in the micro-containers that are stable in the poultry stomach but are dissolved in the duodenum. In this study, we tried to confirm the data on a higher efficacy of the encapsulated OPP compared to the conventional secreted phytase that [PERSON] et al. reported in their paper [19] using a food-producing animal model (broiler chickens). This is of significance, as the results of the experiment using a mouse model where the encapsulated phytase improved the pivotal zoo technical features, namely absolute body weight throughout the experiment and feed conversion ratio (FCR), cannot be transferred to a food-producing animal model [19]. The recommended dosage of the encapsulated OPP for broiler feeding decreased from 165 FYT/kg to 30 FYT/kg due to the experiment data using a chicken model ([PERSON]'s unpublished data). This is important, as the phytase activity in the dried cell biomass mixed with residual yeast growth substrate is two to three orders lower than in the highly concentrated conventional phytase preparation produced by drying a cell free culture medium. Moreover, we assayed the residual phosphorus in broilers' faeces and the content of some other macro and microelements, namely Mg, Ca, K, Zn, and Cu. The reduction of the residual elements in the faeces was considered as independent evidence of the phytase's ability to suppress the anti-nutritional effect of the grain phytate described before by [PERSON] et al. [20]. The influence of the encapsulated and conventional phytases on the broilers' body weight at the end of the 42-day feeding experiment was considered a cumulative effect of the accelerated phosphorus release from the phytate. This made the phosphorus accessible for the bird organism, suppressing the antinutritional effect of the phytate caused by hindering the absorption of Mg, Ca, K, Zn, Cu, and some other mineral and organic nutrients from the chime [21]. The additive containing encapsulated phytase was produced by using chicken intestine (slaughter house waste) and the fat melted from it as a substrate. The biomass of the non-recombinant (parental) strain _Y. lipolytica POIf_ was obtained under conditions equivalent to the OPP-producing recombinant strain and used in the diet for a negative control group with regard to equilibrating the nutritional effect of the yeast biomass and residues of the substrate in all groups involved in the experiment.