esa-sceva/satcom-chunk-collection huggingface dataset:
# Invisible Shield: Unveiling an Efficient Watermarking Solution for Medical Imaging Security
## 3 Proposed System
\(I_{\text{host}}\) represent the host image with dimensions M*N. Convert Text to Watermark Bits: Convert the text watermark into a sequence of bits using an encoding scheme. Let Text_Watermark be the input text to be converted into bits. Watermark_Bits = Text_To_Bits(Text_Watermark) Step 3: Frequency Domain Curvelet Transform (FDCuT) Input: Host (medical) image Action: Apply FDCuT to the host image to obtain \(I_{LF}\), \(I_{MF}\), and \(I_{HF}\) sub-bands. \(I_{LF,JMF}\),\(I_{HF}\) = FDCuT(\(I_{\text{host}}\)) Step 4: Block-based Processing and Watermark Embedding Input: High-frequency (HF) sub-band, text watermark bits Action: Block-based processing: * Divide the HF sub-band into non-overlapped blocks. * Apply these blocks' transformation methods (e.g., Discrete Cosine Transform--DCT). \(I_{\text{HF\_block}}\) = Convert_to_Blocks(\(I_{\text{HF}}\),PQ) Watermark embedding: * Embed the text watermark bits into selected coefficients within the transformed blocks. \(I_{\text{Watermarked\_Coefficients}}\) = Embed_Text_Watermark(\(I_{\text{HF\_Blocks}}\),Watermark_Bits) Step 5: Encryption using RSA Input: Watermarked coefficients Action: Encrypt the watermarked coefficients using the RSA encryption algorithm. Encrypted_Coefficients = RSA_Encrypt([Waternarked_Coefficients, Public_Key)
```  
**Algorithm 1** SecureMediMark algorithm. ```
Step 1:Decryption using RSA Input: Encrypted watermarked coefficients Action: Decrypt the encrypted coefficients using the RSA decryption method with the private key. Decrypted_Coefficients = RSA_Decrypt(Encrypted_Coefficients,Private_Key) Step 2: Extract Watermark from Watermarked Coefficients Input: Decrypted coefficients Action: * Perform inverse operations on the decrypted coefficients to extract the watermark bits embedded in the transformed blocks. * Convert the watermark bits into the original text watermark using the appropriate decoding scheme. * Watermark_Bits = Extract_Watermark_Bits(Decrypted_Coefficients)Text_Watermark = Convert_Bits_to_Text(Watermark_Bits)Text_Watermark = Convert_Bits_to_Text(Watermark_Bits) Step 3: Reverse Block-based Processing to Reconstruct HF Sub-band Input: Extracted watermark and decrypted coefficients Action: Apply the inverse of the transformations used during watermark embedding (e.g., inverse DCT, reverse block-based processing) to reconstruct the original HF sub-band. \(I_{\text{HF\_Reconstructed\_Blocks}}\) = Inverse_DCT(\(I_{\text{Watermarked\_Coefficients}}\)) \(I_{\text{HF\_Reconstructed\_Bits}}\) \(I_{\text{HF\_Reconstructed\_Blocks}}\)
```  
**Algorithm 2** MediRestore decryption protocol.
## 4 Challenges and Limitations of Nanosensor Deployment  
The deployment of nanosensors in bridge maintenance faces certain challenges and limitations that need to be addressed for successful implementation. These challenges primarily relate to signal processing and data interpretation, calibration and sensor reliability, as well as environmental factors and durability. Let us explore these challenges in more detail:
## 4 Co-Citation Analysis
### Duration of Each Mode  
The time-line form of the co-cited network is shown in Figure 4, which is just another manifestation of co-citation network and is more helpful for us to judge research hotspots and duration of each mode.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cluster ID**} & \multicolumn{2}{c}{**Cited Reference**} & \multicolumn{2}{c}{**Citing Reference**} \\ \cline{2-6}  & **Cites1** ** & **Reference** & **Year** & **Coverage2** & **Reference** & **Year** \\ \hline \multirow{3}{*}{\#0 _Mobile bank usage_} & 13 & [28] & 2009 & 17 & [32] & 2015 \\  & 13 & [65] & 2010 & 12 & [36] & 2011 \\  & 12 & [18] & 2009 & 11 & [37] & 2015 \\  & 31 & [29] & 2009 & 20 & [33  
The dot on the horizontal line indicates a document in the cluster in which it belongs and the connection between the documents indicates their co-citation relationship. On the right are the clusters which the reference belongs to and the corresponding timeline coordinates represent the year of publication. Because we set that literature can only appear in the network if it is cited more than a certain number of times, some clusters which have not lasted until now, do not mean that there is no latest literature but their influence is less. Therefore, when the clusters have not lasted until now, there are two possibilities here. One possibility is that relevant research is no longer a research hotspot and the latest research is ignored, which is shown in Figure 4 as clusters #0, #2 and #4. Another possibility is that the relevant researches have just started and there are fewer networks formed, which is shown in Figure 4 as clusters #3, #7 and #10. The clusters that last until now are #1 and #4.  
We have discussed the research directions that each cluster represents in Section 4.1. Therefore, we can divide the modes of Internet finance into three categories. The first category consists of Internet banking and mobile bank and their related researches are no longer the current hot spot. The second category consists of crowdfunding and P2P lending and their works are the latest mainstream. The third category includes modes like big data finance and digital currency, which are just started. There may be other patterns but fail to show up because they are not related enough with other studies.
## 4 Prototyping and Experiments  
A prototype has been manufactured to allow experimental validation of the analyses given above (Figure 15). The prototype is composed of various components including a stator top cap, a stator, a spring, the windings to produce an alternating magnetic field, the mover yoke, and a magnet to produce a constant magnetic field. The stator of a single-phase PM prototype is attached to a rectangular tank in a stainless steel lid. The spring that connects the stator and the mover is embedded between the lid and motor yoke. It has the function of strutting to guarantee that the air-gap length is 2 mm. In addition, the spring should have enough stiffness to ensure that when current of stator windings is zero, the stator does leave its initial position (x = 0, y = 0). When the mover is fixed, the stator can repeat a reciprocating motion under the action of the electromagnetic force. The schematic diagram of experimental device is shown in Figure 16.  
The initial position of the stator is measured using a position sensor under the condition that the current of stator windings is zero. When a DC voltage is applied to the windings, the stator is forced to move leftward by the electromagnetic force accordingly. Meanwhile, the stator windings are also subjected to a rightward spring force. The spring force will decline if the stator is subjected to another rightward force generated by a rotating bolt and it will decrease to zero when the stator returns to its initial position. Then the output voltage can be measured by the pressure sensor while the current can be recorded according to the DC power supply. Thus, the electromagnetic force to which stator windings are subjected at the initial position can be calculated by converting the output voltage into the corresponding force. By changing the applied DC voltage and repeating the aforementioned steps, a figure can be drawn that depicts the changes in electromagnetic force with current.  
The predicted thrust force-current characteristics (Figure 17) are compared and measured. As shown, there was strong agreement between the FE predicted, and results were measured. When the figure of ampere-turn was between 0 and 2500 A, there was a linear relationship between the thrust force and the ampere-turn. However, when the figure of ampere-turn was over 2500 A, the two factors showed a nonlinear relationship because of the magnetic saturation in the actuator. The increase in  
Figure 16: Schematic diagram of experimental device.  
Figure 15: Prototype LOA.  
the temperature of the prototype winding was only 43 \({}^{\circ}\)C, which indicated that the quasi-Halbach magnetized LOA had superior overload performance.
# A Systematic Review of the Role of BIM in Building Sustainability Assessment Methods
## 2 Building Sustainability Assessment Methods
### Leed  
The first version of the Leadership in Energy and Environmental Design (LEED), developed by the United States Green Building Council (USGBC), dates from 1998. The aim was to provide building owners and operators with a concise framework to identify and implement green building solutions. It is mostly used in the United States of America, and it is recognised in more than 30 countries [9].  
LEED is a point-based system, with a balance between known effective practices and emerging concepts, following six major categories: Sustainable Sites, Water Efficiency, Energy and Atmosphere, Materials and Resources, Indoor Environmental Quality and Innovation in Design. Using existing validated technologies, LEED assesses the environmental performance of buildings from an overall point of view during their lifecycle. The number of points that the project earns determines the certification level. In addition to credits, some sections of LEED include prerequisites that also must be satisfied, even though they do not count towards the building's total points [30].  
Nowadays, LEED has several rating systems, in four main areas: Building Design and Construction, Operations and Maintenance, Interior Design and Construction and Neighborhood Development. These systems cover different types of buildings, from residential, hospitals, retail, schools and warehouses, among others [32].  
BIM applications on LEED are usually initiatives from researchers, private organisations or designers. Several authors have developed their specific applications for LEED, according to their needs. In 2014, the USGBC released some applications for LEED automation: Autodesk apps for LEED, COMNET Energy Modeling Portal, Greengrade LEED Management Software, Green Wizard, IES Tap for LEED, Tracker Plus LEED and Trane [17].
# Hybrid Representation of Sensor Data for the Classification of Driving Behaviour
## 3 Hybrid Representation Approach
### RNN-Based Time-Series Representation  
As an alternative to CNNs, RNNs, such as LSTMs or GRUs, can also be employed for the classification of time slices of raw acceleration data. Taking into account that RNNs have been formulated to address time-series, they constitute a natural choice for time slice classification. As was the case with CNNs (see Section 3.1), the absolute classification accuracy is not a primary concern at this stage and the resulting time-series encoding represents the frequency of occurrence of driving patterns. Both LSTMs and GRUs are configured with 1 layer of 128 neurons, followed by a dense layer of 2 neurons with softmax activation function. This shallow network architecture has been tested, taking into account to avoid overfitting. Sparse categorical cross-entropy is employed as a loss function and Adam is employed for optimization. Preliminary versions of these components have been presented in [13; 17].
# Proteomic Evaluation of Insecticidal Action of Phosphine on Green Peach Aphids, _Myzus persicae_
###### Abstract  
Phosphine (PH3) fumigation is one of the best alternatives to methyl bromide for regulating insect pests during storage and shipping. Recently, this treatment has been applied to other agricultural insect pests, including _Myzus persicae_. To understand the mode of PH3 toxicity in _M. persicae_, proteomic analyses using liquid-chromotography tandem mass spectrometry were conducted to measure comparative protein expression levels between PH3-treated samples and controls. PH3 treatment increased NADH dehydrogenase (ubiquinone) flavoprotein 1, mitochondrial-like (complex I) expression, but decreased complex II and ATPases in the mitochondrial electron transport chain (ETC) of _M. persicae_. Glucosidase and antimicrobial proteins such as lysozyme showed enhanced expression in PH3-treated _M. persicae_. Some regulatory proteins related to apoptosis were more abundant in PH3-treated _M. persicae_. Biochemical activities of acetylcholine esterase (AChE) and cytochrome c oxidase (COX) were also measured because these enzymes are known to be targeted by PH3. Only COX activity decreased in relation to increasing PH3 concentrations in adult _M. persicae_. The expression of six selected genes were determined in relation to PH3 treatments. No two-fold changes in the expression of the six genes was observed. Thus, PH3 toxicity caused disruption in the ETC and glucose supply, as well as dis-regulation of apoptosis in _M. persicae_.  
Keywords:phosphine; proteomic analysis; complex I; electron transport chain; glucosidase +
Footnote †: journal: Applied Physics Letters B
## 4 Discussion
### Different Trends of GPP and LAI  
[PERSON] et al. (2017) [PERSON] et al. (2017) defined vegetation productivity as a combination of net assimilation rate, LAI, and photosynthesis duration. That is to say, apart from the LAI, net assimilation rate and photosynthesis duration are also decisive for GPP.  
Firstly, the net assimilation rate is related with the vegetation type as well as vegetation age. There is a great spatial difference of vegetation type across the whole NEC as Figure 1b and [PERSON] et al.'s study [PERSON] et al. (2017) display, so that the relationship between GPP and LAI has great spatial heterogeneity. In addition, the vegetation age is different with time went by which the correlation between GPP and LAI is also influenced. Second, combined with the correlation analysis between GPP and meteorological factors (Figure 5), we found that the CBM is a radiation-dominant region. so, the decreasing trend in solar radiation (or sunshine duration) (Figure 4c) may offset the effect of increasing LAI\({}_{\text{GS}}\) (Figure 6b), leading to non-significant variation in GPP\({}_{\text{GS}}\) over the 30 years.  
Actually, LAI and GPP describes vegetation growth from two different aspects. LAI is defined as one half of the total green leaf area per unit horizontal ground surface area ([PERSON] et al., 2017), which aims to quantify the amount of leaf area in an ecosystem ([PERSON] et al., 2017), so in the NEC, the increasing LAI\({}_{\text{GS}}\) reflects that the leaf quantity and vegetation coverage of the canopy in the NEC increases, while the small variation in GPP\({}_{\text{GS}}\) indicates that vegetation's contribution to regional carbon balance has not changed during the three decades of interaction with its environment across the NEC.  
In summary, although LAI\({}_{\text{GS}}\) indicates a greening trend, ecological productivity may not have corresponding variation. GPP\({}_{\text{GS}}\) may be a more suitable parameter to reflect variation in vegetation growth and vegetation's impacts on carbon cycle ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016).
## 2 Sparse Reconstruction
### Matrix Case  
The sparse reconstruction problem for vectors is extend to that for matrices [30]. For the matrix \(X\in\mathcal{R}^{n\times m}\), row and column norms are defined by  
\[\|X\|_{r_{1}}=\sum_{i=1}^{n}\max_{1\leq j\leq m}|x_{ij}|,\ \ \|X\|_{c_{1}}=\sum_{j=1}^{m}\max_{1\leq i\leq n}|x_{ij}|,\]  
where \(x_{ij}\) is the \((i,j)\)-th element. Consider the following problem:  
\[\min_{X\in\mathcal{R}^{n\times m}}\|X\|_{c_{1}}\ \ \text{s.t.}\ \ AX=B, \tag{6}\]  
where \(A\in\mathcal{R}^{q\times n}\) and \(B\in\mathcal{R}^{q\times m}\). If this problem is feasible, then there exists a solution such that a few columns are nonzero [30]. In a similar way, we can consider the problem of minimizing \(\|X\|_{r_{1}}\) subject to \(AX=B\). Hereafter, it is stated that the matrix \(X\) is row (column) sparse if only a few rows (columns) of \(X\) are nonzero.  
Also, for row and column norms, we can consider the block-sparse reconstruction problem. In the case of the row norm, suppose that the matrix \(X\) is decomposed to \(p\) submatrices, i.e.,  
\[X=\begin{bmatrix}X_{(1)}^{r}\\ X_{(2)}^{r}\\ \vdots\\ X_{(p)}^{r}\end{bmatrix},\ \ X_{(i)}^{r}\in\mathcal{R}^{n_{i}\times m},\ \ \sum_{i}n_{i}=n.\]Then, a convex relaxation of the block-sparse reconstruction problem for matrices is given by the following problem:  
\[\min_{X\in\mathcal{R}^{n\times m}}\sum_{i=1}^{p}w_{(i)}\|X_{(i)}^{r}\|_{r_{1}}\ \ \text{s.t.}\ \ AX=B, \tag{7}\]  
where \(w_{(i)}\geq 0\) is a given weight. In the case of the column norm, suppose that the matrix \(X\) is decomposed to \(p\) submatrices, i.e.,  
\[X=\begin{bmatrix}X_{(1)}^{c}&X_{(2)}^{c}&\cdots&X_{(p)}^{c}\end{bmatrix},\ \ X_{(i)}^{c}\in\mathcal{R}^{n\times m_{i}},\ \ \sum_{i}m_{i}=m.\]  
In this case, a convex relaxation of the block-sparse reconstruction problem is given by the following problem:  
\[\min_{X\in\mathcal{R}^{n\times m}}\sum_{i=1}^{p}w_{(i)}\|X_{(i)}^{c}\|_{c_{1} }\ \ \text{s.t.}\ \ AX=B. \tag{8}\]
## 3 Results and Discussion
### Transformer Noise Separation Test in a Substation  
#### 3.3.1 Test Setup  
On the basis of point source simulation and experimental validation, we carried out a noise separation test for three transformers in a substation. The three transformers have the same model, with dimensions approximately 5 m \(\times\) 5 m \(\times\) 2.5 m, voltage of 110 kV, and power rating of 50,000 kVA. The reasonableness of the separation results is proved by analyzing the noise line spectrum and A-weighting SPL obtained from the separation sound pressure results of three transformers at different test points. The test is divided into two test conditions according to the horizontal distance between the microphone line array and transformers. The schematic diagrams of Condition 1 and Condition 2 are given, respectively, in Figures 9 and 10, where \(d_{1}\) is the distance between the microphone line array and three transformers (2 m in Condition 1, 10 m in Condition 2); \(d_{2}\) is the distance between the microphone line array and test points (8 m in Condition 1, 0 m in Condition 2); the \(x\)-coordinates of the seven test points are the same as those of No. 3, 5, 7, 10, 12, 14, and 17 microphones in the line array; \(\Delta\) is the array element spacing; and \(l_{1}\) and \(l_{2}\) are the distances between the three transformers, which are 15.25 m and 15.72 m, respectively.  
The main equipment used in the substation test is shown in Table 4. The above equipment is arranged and connected as shown in Figures 9 and 10 where the line array of 18 microphones is realized by panning a line array of 9 microphones. Figure 11 shows the test scene in the substation. In order to obtain high enough resolution to meet the transformer's main working frequency, the analysis bandwidth of the B&K PULSE acquisition system is set to 3200 Hz, and the number of frequency lines is set to 3200 (i.e., the frequency resolution of the resulting sound pressure spectrum is 1 Hz). The microphones' line array measures sound pressure amplitude, phase and sound pressure gradient amplitude, and phase. Seven microphones measure the field noise at seven test points, and the intrinsic noise pressure of the three transformers is obtained by the vibro-acoustic coherent analysis separation technique (more details in Ref. [8]).
## 1 Introduction  
With the development of electronic products, the information industry and industrial manufacturing, the demands for energy have sharply increased in recent years. As a result, many energy conversion and storage devices, such as fuel cells, lithium batteries and supercapacitors, have emerged [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. Generally, carbon materials such as carbon nanotubes, carbon fibers and graphene, owing to their large specific surface area, good conductivity and high stability, have been widely utilized as the electrodes or as the current collectors to further load other active materials [11; 12; 13; 14; 15].  
As is well known, the diameter of ultra-long fibers prepared using electrospinning technology can be expediently controlled from a few tens of nanometers to several dozens of micrometers [16; 17; 18]. Moreover, during the electrospinningning process, various materials (metal nanoparticles, metal oxide or metallic salts) could be conveniently added into the spinning precursors to fabricate the composite materials for diverse applications [19; 20; 21; 22; 23; 24; 25]. Therefore, it is advantageous to construct carbon nanofiberswith uniform diameter, excellent conductivity and self-supporting flexibility using the electrospinning technology, which can be utilized as the electrodes or current collectors for energy storage devices. For example, [PERSON] et al. reported that lignin was blended with polyacrylonitrile (PAN) in different ratios and fabricated into carbon nanofiber electrodes by electrospinning followed by thermal stabilization, carbonization and subsequent activation by CO\({}_{2}\) of the carbonized mats. The coin cell supercapacitors employing these electrodes exhibit 128 F/g specific capacitance, 59 Wh/kg energy density and a 15 kW/kg power density when operated at 3.5 V using an ionic liquid electrolyte [26]. [PERSON] et al. fabricated freestanding and flexible MnO-decorated carbon nanofiber (CNF) composites as lithium-ion battery anode materials with an initial capacity of 1131 mAh/g and a retention capacity of 923 mAh/g after 90 charge-discharge cycles under a current rate of 123 mA/g [1].  
In order to apply the spun carbon nanofibers in the flexible energy storage devices with high performances, it is significant to further improve the mechanical, electrical and electrochemical properties of the carbon nanofibers. In this work, we aim to fabricate the spun carbon nanofiber mats followed by thermal stabilization, carbonization, toughening modification and activation to achieve the characteristics of high conductivity, good flexibility and large specific capacitance. The effects of the spinning polymer concentration, thermal stabilization and carbonization temperatures, addition of MWCNTs, as well as acid activation on the performances of the carbon nanofiber mats were carefully investigated.
# Proposal of an Adaptive Fault Tolerance Mechanism to Tolerate Intermittent Faults in RAM
## 4 Assessment of the Adaptive Fault Tolerance Mechanism
### Comparison with Other Proposals
Others adapt to voltage scaling [41; 47], to the overall number of failed cells (counted by the OS) [48], or to a calculated bit error rate stored in an internal table called \"reliability map\" [50]; finally, in [42] when the CLC-S decoding detects that the number of errors exceeds its error correction capabilities, it launches the second decoding in CLC-E mode. Thus, as we have mentioned throughout the paper, this is, to our knowledge, the first work where a low-level hardware-implemented AFT mechanism is proposed to adapt to intermittent faults with no redundancy increment.