esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 5 Evaluation
### Evaluation of Buffer Hardware
#### 5.2.1 Resource Evaluation  
Table 3 lists the resource consumption of LUT, REG, and BRAM for different buffer schemes. Note that the results of D-SWIM and SWIM-2 pass schemes are compared, and the ratio values of D-SWIM to SWIM-2 pass are listed in the parenthesis of the D-SWIM column. In Configuration 1, D-SWIM consumes 44.9% and 34.7% more LUT and REG than that in SWIM. However, the BRAM consumption in D-SWIM is less--this is because \(N_{LB}\) is 4 in SWIM as 4 lines compose a BRAM-partition period, but this issue does not exist in D-SWIM. When \(N_{blk}\) is set to 16, as per Configuration 2, \(N_{LB}\) of SWIM increases to 8, which consumes more logic and BRAMs than D-SWIM. Although the 2-pass optimization halves the \(N_{LB}\), the BRAM cost in SWIM is 16, which is 1.8 times that of 9 in D-SWIM. SWIM costs more BRAMs of specific widths to compose the LB, whereas D-SWIM sets all BRAM ports to the maximum width, which fully utilizes the bandwidth and reduces the resource. Note that the D-SWIM buffer accommodates arbitrary image width, and configurations use different \(N_{line}\) values but identical \(H\) and \(N_{blk}\) (e.g., Configuration 2 and Configuration 3) share the same hardware via dynamic programming. In Configurations 5-6 where \(H\) is 5, D-SWIM's consumptions of both logic and BRAM are less than SWIM. D-SWIM saves 13.1% LUT, 31.4% REG, and 55.9% BRAM compared with SWIM-2 pass in Configuration 6. With Configuration 6, the \(N_{LB}\) of SWIM is 8, which costs more logic on the line-selection multiplexer.  
We also investigate the impact of parameters on the resource consumption in the D-SWIM scheme. Comparing the results in Configurations 1-4, we note that \(N_{blk}\) greatly affects the logic and BRAM resources. This is because a larger \(N_{blk}\) requires more complex multiplexers for pixel-block manipulation. Note that \(H\) also affects the hardware (comparing Configuration 3 to Configuration 5) because a larger \(H\) costs more LUTs for the line-selecting multiplexer and more REGs on the temporary pixel-storage.
## 4 Test Results and Analysis
### Stiffness Degradation  
The secant stiffness \(\mathbf{K_{i}}\) is used to describe the stiffness degradation of the frame as shown in Figure 14, and the formula for calculating \(\mathbf{K_{i}}\) can be found in Chinese standard JGJ 101-96 [26] as  
\[\mathbf{K_{i}}=(|+\mathbf{P_{i}}|+|-\mathbf{P_{i}}|)/(|+\Delta_{i}|+|-\Delta_{i }|) \tag{1}\]  
where \(\mathbf{P_{i}}\) and \(\Delta_{i}\) are the maximum values of the load and the corresponding displacement under the \(i\)th cycle, respectively.  
It can be seen from Figure 14 that the secant stiffness decreases with the increase in displacement, and the stiffness degradation changes from fast to slow during the whole test. The main reason is that  
Figure 14: Stiffness degeneration.  
Figure 12: An energy dissipation capacity calculation diagram.  
Figure 13: The energy dissipation capacity.  
before the yielding of the frame, there are many cracks on the beam, which makes the reduction rate of the bearing capacity less than the growth rate of the displacement, and the stiffness degradation is more rapid. However, when the plastic hinge appears at the beam end and column end, the good deformation capacity of the frame causes the bearing capacity to decrease slowly. At this point, the reduction rate of the bearing capacity is less than the growth rate of the displacement, and the stiffness degradation is relatively slow. At the same time, it can be seen from the figure that the stiffness degradation of the frame has obvious regularity. The RACFCST frame is distributed in the form of a quadratic function, and the mathematical expression is as follows:  
\[y=ax^{2}+bx+c \tag{2}\]  
where \(y\) is \(\textbf{{K}}_{\text{i}}/\textbf{{K}}_{\text{e}}\); \(\textbf{{x}}\) is \(\Delta/\Delta_{\text{i}}\); \(\textbf{{K}}_{\text{e}}\) is the elastic stiffness, which is calculated by the initial load divided the displacement corresponding to the initial load in the load-control stage; and \(\textbf{{a}}\), \(\textbf{{b}}\) and \(\textbf{{c}}\) are the regression factors, which are equal to 0.0328, \(-\)0.3267 and 0.971, respectively.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 2 Background Motivation and State of the Art
### Combining Watermarking with Cryptographic Primitives  
Watermarking and cryptographic techniques were deployed to ensure a high level of security of medical images transmissions. In [18], the watermark generator together with an encryption algorithm was proposed to ensure the content-confidentiality of the image. In particular, this system merges an Integer Wavelet Transform (IWT)-(Least Significant Bit) LSB watermarking and an encryption  
Figure 1: Two state of the art proposals of secure medical image transmission systems.  
algorithm using a random permutation and a chaotic keystream-based key generator. Electronic patient record (EPR) and context information are extracted and used as watermarks to be embedded into the original images. Although the proposed encryption algorithm has advantages to secure medical images, it still has disadvantages such as once the image is decrypted, it is no longer protected and it becomes difficult to verify its origin and its integrity [19]. In [20], a robust and secure watermarking approach was proposed for telehealth applications. This approach combines three watermarking techniques of the transform domain: Digital Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Singular Value Decomposition (SVD). The patient record/identity is embedded in the original medical image. A chaotic encryption algorithm relying on two-dimensional logistic maps was applied on a watermarked image in order to improve patient data confidentiality. In [21], two watermarking algorithms dedicated to medical images in the transform domain were proposed. In the first watermarking algorithm, a digital watermark and EPR are embedded in the Region of Interest (ROI) and Region of Non-Interest (RONI). In the second one, ROI is kept unmodified for telediagnosis reasons and RONI is deployed to hide the digital watermark and EPR. In [22], a medical image watermarking algorithm based on the wavelet was proposed. In the suggested technique, the cover medical image is decomposed into ROI and RONI regions and three different watermarks are embedded into the RONI part using DWT. In [23], a system combining encryption and watermarking in the spatial domain was presented. The encryption relies on the Advanced Encryption Standard (AES) in a Cipher Block Chaining (CBC) mode. Integrity and authenticity factors were checked by the authors. The performance evaluation of the system showed that the Peak Signal to Noise Ratio (PSNR) obtained without attacks was around 49 dB. The experimental result showed an acceptable quality and sufficient capacity for embedding.  
It is noted that all previous proposals do not deploy physical marking for the watermark. Such a technique is utilized to mark physically a product for future reference such as origin, authenticity, etc. [24]. This flaw or weak point leaves the medical image device/generator without any proof of the ownership.
## 4 Experiment Result and Discussion
### Verification of the Leakage Prediction  
To verify the leakage value predicted by the supply airflow derived in this study, the leakage calculated using the pressure difference measured by the pressure sensor was compared to the value predicted by the changing supply airflow during automatic operation of the air conditioning system. The automatic operation data is the same as the data in Figures 12 and 15 and shows the leakage airflow over time. The comparison revealed that the leakage airflow calculated by the pressure difference measured by the  
Figure 14: Relationship between differences in supply and return airflow rates and leakage airflow rates.  
Figure 13: Relationship between pressure difference and leakage airflow rate.  
pressure sensor and the leakage airflow predicted using the airflow of the supply and return are proportional, and the pressure difference in the data is negative, so infiltration was determined to occur at the airflow rate shown in Figure 15. The leakage airflow derived from the relationship observed in this study was calculated differently depending on the air conditioning situation, which changes in real time, whereas the leakage airflow calculated using the leakage function was measured as the same pressure difference even if the air handler operation situation is different because the pressure difference sensor installed in the test bed shows the measured value as an integer. When analyzing the relative error of the leakage prediction value and the measured value, the results were almost the same with a minimum of 0.00% and a maximum of 30.6%, but the average error rate for 5 h was 8.5%, which is similar to approximately 200 CMH in terms of airflow. The cvRMSE of the predicted and measured values of the leakage volume was 11.05%, which is lower than the limits specified by the ASHRAE (\(\leq\)30%) or IPMVP (\(\leq\)20%) guidelines. These results confirm the suitability of the leakage prediction model based on the use of the relationship between the air supply and exhaust airflow and the pressure difference of air handlers for predicting leakage in operating buildings.
Future Internet_**2010**, \(2\), 60-73; doi:10.3390/fi2010060  
_future internet_  
**ISSN 1999-5903**  
www.mdpi.com/journal/futureinternet  
_Article_  
Dynamic QoS Evaluation of Multimedia Contents in Wireless Networks by \"Double-Boomerang\" Watermarking  
[PERSON] *, [PERSON] and [PERSON]  
Digital Signal Processing, Multimedia, and Optical Communications Laboratory, Department of Applied Electronics, University of ROMA TRE, via della Vasca Navale 84, 00146 Rome, Italy;  
E-Mails: [EMAIL_ADDRESS] (A.C.); [EMAIL_ADDRESS] (G.G.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-06-5733-7004; Fax: +39-06-5733-7026.  
_Received: 4 January 2010; in revised form: 2 February 2010 / Accepted: 2 March 2010 / Published: 8 March 2010_  
**Abstract:** This work presents a cooperative network-aware processing of multimedia content for dynamic quality of service management in wireless IP networks. Our technique can be also used for quality control in UMTS environments, exploiting the tracing watermarking recently introduced in literature. In this work, we use the transmitted video-sequences to monitor the QoS in a videoconference call. The video-sequence of every active user travels on the communication link, one time as video (transparent mode), one time as watermark (hidden mode) describing a boomerang trajectory. The results obtained through our simulation trials confirm the validity of such approach. In fact, the advantages of distributing the management process are (i) an easier and more precise localization of the cause of QoS problems, (ii) a better knowledge of local situations, (iii) a lower complexity for a single QoS agent and (iv) an increase in possible actions.  
**Keywords:** Quality of Service (QoS); wireless networks; 3G multimedia communications; network-aware processing; watermarking
## 1 Introduction
They verified that the spatial representativeness of the MODIS daily albedo product approximately varied from 606 m to 843 m. [PERSON] and [PERSON] [40] estimated the point spread function (PSF) of nominal 250 m MODIS gridded surface reflectance products, and discovered that the spatial representativeness varied from 344 m to 835 m along the rows, and between 292 m and 523 m along the columns. Their work helps users understand the spatial properties of the satellite product, but their work was based only on a single area, and a general adaptable result that is based on different kinds of land surfaces is needed. The MODIS BRDF/albedo product was derived with a semi-empirical, kernel-driven BRDF model. Data for 16-day, multi-angular, cloud-free, atmosphere-corrected surface reflectance was compiled to apply the retrieval procedure. To better characterize the rapid change of the land surface,the daily albedo product was retrieved using the same semi-empirical algorithm, but with the single day of interest emphasized by being weighted more heavily [39; 42] in the 16-day composite period. The spatial representativeness and accuracy of the newly released daily albedo product (MCD43A3, V006) have not been tested for different types of land cover for a long time series. The high-resolution (30-80 m) satellite albedo product is essential in understanding the climatic consequences of land cover change and medium-to-fine scale applications [43]. It is also a key bridge to the assessment of coarse-resolution products. [PERSON] et al. [44] developed a method to estimate both snow-covered and snow-free albedo from the Chinese environment and disaster monitoring and forecasting small satellite constellation (HJ) satellite data. [PERSON], et al. [15] then derived 30 m albedo from Landsat 7 and Landsat 8 using a similar algorithm. [PERSON] et al. [45] evaluated 30 m albedo product estimated from Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) at Surface Radiation (SURFRAD), AmeriFlux, Baseline Surface Radiation Network (BSRN), and Greenland Climate Network (GC-Net) sites, with results indicating that the direct estimation approach can generate reliable albedo estimates with accuracy of 0.022 to 0.034 in terms of the root mean square error (RMSE). The derivation of global land surface albedo product using Landsat sensors makes it possible to better understand energy transfer between the land surface and the atmosphere at global and regional scales. Furthermore, it makes the assessment of the coarse-resolution albedo product possible, as well as scale transformations for different land cover types and landscapes.
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 2 Classification of DLC Coatings
### Properties
#### 2.4.4 Growth Rates in PECVD  
The precursor molecule for a-C: H development is, for the most part, picked as to the film growth rate. The growth rate is discovered to be firmly connected to the ionization capability of the atom [30]. Unsaturated atoms with low ionization possibilities; for example, acetylene, return a large number of high development rates compared to methane [65]. Acetylene is likewise preferred by several researchers, since it has the most reduced H content (alongside benzene) of the standard precursor molecule, and hence the H substance of the subsequent a-C: H film likewise will, in general, be lower [66]. As modulus of elasticity is identified with the part of C-C bonding, it makes the H content inclined to drop to increase the modulus, and accordingly the hardness, for a given sp\({}^{3}\) content. Subsequently, one should keep a high atomic energy of state 500 eV for each C to decrease pressure. Particle energy for each C ion demands 500 eV for methane, but 1000 eV for acetylene. An irregular mix stores a-C: H from methane at high atomic energy (600-1200 eV) [67, 68, 69, 70, 71, 72].  
In this way, the atomic energy is well over the ideal estimation of 100 eV compared to the highest sp\({}^{3}\) content. Hence, the C-bonding is partly graphitic. Yet, the high ion energy also leads to the implantation of a large part of the H atoms of the incident methane, with the goal that the film has an uncommonly higher H content for a-C: H films of sp\({}^{2}\) content [73,74].
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 7 Simulation Results
### Analysis of the Effectiveness of the Greedy-Based DRL Algorithm  
In this subsection, we validate the effectiveness of the greedy-based DRL algorithm using two different dependency structures with identical task sizes and quantities. One dependency structure primarily consists of two parallel structures, while the other primarily consists of three parallel structures, and the depth of the dependency tasks in the two parallel structures is greater than that in the three parallel structures.  
Figure 7a reflects the usage of computing resources under different numbers of edge servers. As shown in the figure, when the number of ESs is fixed, the dependency structures may vary, but the amount of computing resources consumed remains the same. This is because, regardless of the dependency structure in this case, the number of sub-tasks processed by the proxy ES in the optimal scenario is the same, the number of further distributed sub-tasks is the same, and the computing capability of each non-proxy ES is the same. Therefore, the amount of computing resources consumed is the same. Figure 7b shows the usage of communication resources in different situations. When the number of edge servers is two, regardless of the main parallel structure of the dependent task, the proxy ES distributes five sub-tasks externally; as such, the communication resources used in this case are the same. However, as the number of edge servers increases, the number of tasks assigned to each edge server under different dependent task structures varies, thus resulting in different usages of communication resources. Furthermore, the changes in total resources as shown in Figures 7a,b indicate that the more edge servers that are motivated to participate in the computation, the less resources each edge server contributes to, on average.  
Figure 8 presents the impact of dependency structures and the number of ESs on the execution time of dependent tasks. When dependent tasks are fully executed locally, the dependency structure has no impact on the task, and thus its local execution time remains constant. For tasks primarily based on two parallel dependency structures, the execution time with three ESs is slightly less than with two edge servers. This is due to the waiting time for sub-tasks in the dependent tasks being greater than the communication time between edge servers. When the number of ESs is four, since the computing resources of each non-proxy ES are the same compared to when there are three ESs, the offloading optimal decision remains the same regardless of the parallel structure. However, as each edge server's average resource contribution decreases and less resources are allocated to each task, the execution time increases. But, overall, the execution time of the dependent task under the incentive mechanism is lesser than the total local execution time.  
Figure 7: Resource usage graph. (**a**) Computing resource usage graph and (**b**) communication resource usage graph.
# A Survey of Handwritten Character Recognition with MNIST and EMNIST
## 2 MNIST Database  
### Acquisition and Data Format  
The MNIST (Mixed National Institute of Standards and Technology) database was introduced by [PERSON] et al. [2] in 1998. Since then, this dataset has been widely used as a testbed for different machine learning and pattern recognition proposals.  
The MNIST database contains a total of 70,000 instances, from which 60,000 are for training and the remainder are for testing. The database comprises two different sources: NIST's Special Database 1 (collected among high-school students) and NIST's Special Database 3 (retrieved from Census Bureau employees). The training and test set were chosen so that a same writer would not be involved in both sets. The training set contains samples from more than 250 writers.  
Original images were submitted to preprocessing. This procedure first involved normalizing images to fit in a 20 \(\times\) 20 pixel box while preserving the aspect ratio. Then, an anti-aliasing filter was applied, and as a result black and white images were effectively transformed into grayscale. Finally, blank padding was introduced to fit the images in a larger 28 \(\times\) 28 pixel box, so that the center of mass of the digit matched its center. An example of a sample corresponding to the digit '7' can be found in Figure 0(a). A larger set of instances belonging to the training set is displayed in Figure 0(b).  
By looking at this figure, we can see that some digits can be easily confused. Depending on how they are written, it can be difficult to elucidate if a certain digit is a '4' or a '9', to say an example. Some cases of digits difficult to recognize, even for humans, can be seen in Figure 0(b) (see the ninth '2' or the ninth '7').
## 3 Results
### Gene Ontology Enrichment Analysis of Differentially Expressed Genes  
GO enrichment analysis was performed to broadly classify all of the DEGs into corresponding biological process, molecular function, and cellular component categories. GO terms in each category were sorted from lowest to highest by corrected \(p\)-values. To understand the distinct response of the two genotypes to the NT and AT treatments, we analyzed the enriched GO terms of the comparison groups  
Figure 3: (**a**) A Venn diagram illustrating overlaps among differentially expressed genes (DEGs) in different comparison groups, i.e., CKvsNT, CKvsAT, and NTvsAT, in 15 and 814; (**b**) a bar chart showing up- and downregulated DEGs in the six comparison groups. The red column shows upregulated DEGs, and the green column shows downregulated DEGs. CK: control.  
CKvsNT (15 and 814) (Figure S1) and CKvsAT (15 and 814) (Figure S2). There were more upregulated than downregulated DEGs in enriched GO terms for CKvsNT (15 and 814); more downregulated than upregulated DEGs were detected among enriched GO terms for CKvsAT (15 and 814). Moreover, the numbers of DEGs for CKvsNT (15 and 814) were much higher than those for CKvsAT (15 and 814). Furthermore, the number of downregulated DEGs among enriched GO terms was also greater than the number of upregulated DEGs for NTvsAT (15 and 814) (Figure 4). In addition, highly enriched GO terms for NTvsAT DEGs according to the biological process category are involved in translation, RNA methylation, and macromolecule methylation; those according to the molecular function category included structural constituent of ribosome, structural molecule activity, and rRNA binding predominated, those and according to the cellular component category included ribosome, cytosolic ribosome and ribosomal subunit. These results indicate distinctive differences in the transcript levels of certain genes between the NT and AT treatments.  
Figure 4: Enriched Gene Ontology (GO) terms for NTvsAT (15 and 814). The results are summarized under three top-level ontologies: biological process, molecular function, and cellular component. The \(x\)-axis represents the functional category of the DEGs. The \(y\)-axis indicates the number of annotated genes expressed in a given subcategory. The downregulated DEGs are represented by green, and the upregulated DEGs are represented by red.
## 1 Introduction
It should be emphasized that the work reported in the literature does not include any methodology of optimally sizing and dispatching a battery system for the purpose of reducing energy cost at large facilities. The present paper introduces a first attempt on attaining the optimal size and dispatch profile of a behind-the-meter battery which yields maximum profit for a large commercial or industrial facility. The technique has been tailored to fit the IESO market regulations in Ontario, but could be extended to other jurisdictions whose rules allow a certain level of energy cost control via distributed energy resources. Similar to the case in Ontario, it is anticipated that other markets will have an ROI level which encourages investments by facility owners, utilities, and independent energy investors.