esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Vision System for Counting Small Metal Parts in Electro-Deposition Industry
## Materials and Methods
### Light Settings
#### 2.3.1 Frontal Light with Black Uniform Background
Figure 7: (**a**) Item B after morphological image closing plus image erosion; (**b**) an example of wires loop after morphological closing followed by image erosion; (**c**) clusters merged after dilation. The image is intentionally left in grayscale to show actual original items. However, this solution may also lead to some unwanted side effects that make this alternative unsuitable. In fact, in Figure 7b, it can be noted that, in some cases, the wires formed closed loops in the image. Such loops may result in being completely closed by a morphological closing filtering. If sufficiently large, they can be easily mistaken for items in the counting phase. In addition, if a couple of items are sufficiently close, the filter may cause the fusion of the relative clusters into one (see Figure 7c). Between the two alternatives proposed above, the better performance proved to be the first one (i.e., the morphological opening-based solution). Starting from the morphologically opened image (see Figure 5c), the connected regions representing the actual items needed to be discriminated from the ones representing small wire portions and/or item fragments. To this aim, an elective method could be to perform area-based discrimination, carried out by imposing an appropriate area threshold. Since the item dimensions are widely variable and unknown a priori, a fixed area threshold value cannot be based on the item dimension itself. On the contrary, the wire dimension is constant. Accordingly, it is possible to define a fixed area threshold under which clusters are considered too small to be an item, thus must be ignored. Considering that the wire thickness is approximately \(1\) mm--corresponding to 7 pixels in the image (based on the shooting setup described in the previous section), a limit dimension was set at 4 mm\({}^{2}\)--corresponding to 200 pixels. In the example shown in Figure 8a, this method allowed appropriate discarding of small clusters. However, in several other situations, such as the one depicted in Figure 8b, this criterion led to misclassification. This was mainly due to the heavy image cluster fragmentation induced by the acquisition setup and the subsequent image filtering. Other than the simple criterion described above, other more complex techniques have been tested in order to cluster pixel regions, namely k-means clustering and Support Vector Machine (SVM) [17; 18]. The results, not detailed in the present paper, show that this misclassification still occurs.
# Enhancing Chatter Stability for Milling Thin-Walled Blades by Designing Non-Uniform Allowance
## 2 Description of the Methodology
### Experimental Verification of Thin-Walled Blade Milling  
In order to simulate actual blade operating conditions, the base of a single blade is clamped in a flat nose pliers along the length direction, similar to a mortise and tenon structure. Place the blades in a free state. The stability state of cutting is directly reflected through the acceleration spectrum during the cutting process. The vibration testing principle in milling is depicted as in Figure 7. The ECON-AVANT-MI-7008 data acquisition and analyzer from Hangzhou Yiheng Technology Co. (Hangzhou, China) are utilized, alongside the Dytran3032A acceleration sensor for accuracy and reliability of measurement and data analysis. To monitor vibration amplitude during milling, the acceleration sensor was affixed to the tip position of the blade and acquired the dynamic acceleration signal in real-time via zoning. Prior to measurement, it is essential to calibrate the phase characteristics of the measurement channel for force and vibration acceleration signals to avoid any phase differences which could lead to measurement errors. Roughness was measured using a TR100 Roughness Meter manufactured by Beijing Times Yuanfeng Technology Co. (Beijing, China) Surface roughness was evaluated using Ra as the index, with testing conducted in the feeding direction. Each experiment was conducted in groups of five, with the average value taken as the final result. The experimental test site is illustrated in Figure 8.  
Figure 8: _Cont._  
Figure 7: Schematic diagram of milling vibration test.  
To ensure the proposed method's reliability in the finishing stage, a traditional uniform allowance distribution structure design was shown as A and B in Figure 7(a), alongside the non-uniform allowance structure design shown as C and Din Figure 7(a), with a guarantee of the same amount of removal volume. The vibration acceleration spectrum acquired during cutting is shown in Figure 7(b). The experiments were conducted using a VMP-850A four-axis vertical machining center equipped with a BT40 spindle and toolholder, a carbide ball end milling cutter with a diameter of 8 mm and a tool overhang of \(L=30\) mm. The axial depth of cut was \(a_{p}=0.2\) mm, and the cutting speed was \(f=1100\) mm/min with a milling mode of down milling and a blade material of TC4 titanium alloy. The blade basin milling process is shown in Figure 7(c).The blade suction milling process is shown in Figure 7(d).The cutting tool was cooled with emulsion.
## 2 Materials and Methods
### PCR Amplification, Cloning, and Sequencing  
The sequence of the ASeV isolate (GenBank accession number (acc. no.) OU466880\(-\)84) was used for designing the primer sets targeting near full-length segments of RNA2 to RNA5, including the ORF2 to ORF5 and flanking untranslated regions. The PCR products for RNA2-5 covered almost the full length of the corresponding genomic RNAs: RNA2 (2225 out of 2243 nt in the reference isolate, acc. no. OU466881), RNA3 (1473 out of 1483 nt  
Figure 1: Symptoms observed in collected _Frazinus_ spp. leaf samples. **a**: Leaf deformation and mottling in _F. ornus_ (Hamburg, Germany); **b**: shoestring symptoms in _F. excelsior_, characterized by elongated and narrowed leaves (Melzower forest, Germany); **c**: chlorotic line pattern and leaf deformation in _F. excelsior_ (Sigtuna, Sweden); **d**: leaf deformation and line pattern in F. excelsior (Oberbozen, Italy); **e**: chlorotic line pattern on _F. excelsior_ leaves (Basel, Switzerland); **f**: chlorosis and leaf deformation in _F. excelsior_ (Bromma, Sweden). The presence of ASeV has been confirmed by RT-PCR.  
in acc. no. OU466882), RNA4 (1510 out of 1518 nt in acc. no. OU466883), and RNA5 (1330 out of 1333 nt in the reference isolate, acc. no. OU466884). Primer sets targeting two regions of ORF1 on the RNA1--hereafter be referred to as the ORF1 N-proximal region (antigenome positions 840-1875 nt, acc. no. OU466880) and the ORF1 C-proximal region (antigenome positions 4362-5470 nt, acc. no. OU466880)--were designed using the sequences of ASaV isolates (acc. nos. OU466880 and OU46687) (Table 1). PCR amplification was performed using Phusion DNA Polymerase (2 U/ uL) (Thermo Fisher ScientificTM, Waltham, MA, USA), a high-fidelity enzyme with proofreading \(3^{\prime}\to 5^{\prime}\) exonuclease activity, in a 25 uL reaction following the manufacturer's instructions. The RT-PCR results are presented in Supplementary Figures S1-S6. Amplified RT-PCR products were ligated into the pJET1.2 blunt-end cloning vector (CloneJET PCR Cloning Kit, Thermo Fisher ScientificTM, Waltham, MA, USA) and transferred into _Escherichia coli_ XL1-Blue MRF competent cells (Stratagene; Thermo Fisher ScientificTM, Waltham, MA, USA). Colony PCR using pJET1.2F and pJET1.2R primers (provided by the kit) was applied to select the recombinant plasmids. Among the screened colonies harboring the recombinant plasmid with the expected insert, at least two and a maximum of six independent colonies were subjected to plasmid extraction using the NucleoSpin Plasmid EasyPure kit (Machery Nagel, Duren, Germany) and were sent for Sanger sequencing to Macrogen Europe (Amsterdam, The Netherlands) using vector-specific primers (pJET1.2F and pJET1.2R) and gene-specific primers to cover the gaps (Table 1).
# Quantum Implementation of AIM: Aiming for Low-Depth
## 1 Introduction
#### Contribution  
In short, this work makes the following contributions.  
1. Quantum Circuit Implementation of AIM. We present the implementation of a quantum circuit for the variants of AIM (-I, -III, and -V). This marks the first quantum implementation of AIM, which is the symmetric key primitive of AIMer (a candidate algorithm in the KPQC and NIST PQC competitions).
2. Low-Depth Implementation. Our implementation of the quantum circuits for AIM focuses on low Toffoli depth and full depth. To minimize depth while allowing for a reasonable number of qubits, we gather multiple contributions, including a low-depth quantum circuit for the multiplication, optimized quantum circuits for inner operations (Mer and Linearlayer) of AIM, and the reuse of ancilla qubits (through reverse operation).
3. Post-quantum Security Evaluation of AIM. We evaluate the post-quantum security of AIM by estimating the cost of [PERSON]'s key search based on the implemented quantum circuits of AIM-I, -III, and -V. For this security evaluation, we compare the estimated cost of [PERSON]'s key search for AES, as estimated by NIST [10; 11], with the findings (the authors of [13] reported/analyzed the issue of underestimated cost in [14], the result in [11]) from recent related work [13].
## 2 Preliminaries
### Diffie-Hellman Secret Exchange Protocol  
In the formula that the protocol depends on, \((a*G)b=(b*G)*a\), where \(a\) and \(b\) are constants. The sender and the receiver negotiate the parameters of the elliptic curve and the base point \(G\). These parameters are all public.  
The sender generates a random key pair (public key: \(K_{a}=k_{a}G\), private key: \(k_{a}\)). The receiver generates a random key pair (public key: \(K_{b}=k_{b}G\), private key: \(k_{b}\)). The sender multiplies the recipient's public key by his own private key to obtain the shared key \(k_{s}=(k_{b}G)k_{a}\). Similarly, the recipient multiplies the sender's public key by his own private key to obtain the shared key \(k_{s}=(k_{a}G)k_{b}\). According to the formula mentioned earlier, \((a*G)b=(b*G)*a\), the shared keys obtained by the sender and the receiver are equal. In this way, the two parties complete the key exchange through the protocol.
# Storytelling with Image Data: A Systematic Review and Comparative Analysis of Methods and Tools
## 3 Task 1. Building the Foundation for Organizing and Curating the Raw Image Data
### Curation: Image Preparation Using Cleaning and Preprocessing
#### 3.1.2 Image Enhancement  
Rather than correcting faults, enhancements are utilized to optimize for certain feature-measuring methodologies. Sharpening and color balance are two well-known image enhancement techniques. The followings are some broad types of image-enhancing techniques and their possible advantages for feature description.  
* **Enhancements to Illumination**[18; 19]. Lighting and contrast enhancement difficulties are inextricably linked when it comes to low-light picture enhancement. Several works have attempted to build frameworks for how lighting and noise in low-light images are viewed jointly, resulting in noise control and low-light improvement findings. Convolutional neural networks (CNNs) can simulate the usage patterns of illuminance and picture noise accordingly and utilize them as restrictions to aid the joint learning process. High-quality pictures require high-quality thin slices, should be clean, free of imperfections, and consistent in thickness from center to edge to minimize artificial intervention contrast--the narrower the segment, the smaller the path variance and the lesser the intervention color.
* **Pyramids of Scale-Space.** [PERSON] [20] developed defining the diffusion pyramid to investigate lighting qualities over a range of scales precisely. They reasoned that by aggregating the nonlinear rate of diffusion from wide to fine scales via max pooling, the dazzling characteristic of lighting, even in the dark environment, would be well shown from both domestic and global perspectives. Additionally, [PERSON] et al. [21] suggested a scale-aware deep CNN for deraining that has a multiscale features extraction branch that is coupled to a scale-space invariant focus branch.
* **Enhancements to the Blur.** Motion blur is a typical occurrence due to the exposure time taken by camera sensors, throughout which scenes are collected at distinct time stamps and averaged (blurred) into a signal [137]. So, a slow shutter speed and change in momentum of the camera or subject might create motion blur. It can also occur as a result of a UAV (unmanned aerial vehicle) platform's extreme vibrations, which might impair fracture identification [22]. The noise of blurring produced by complementary beam removal imaging could be reduced with a compressed blind deconvolution and denoising technique [23].  
Preprocessing and denoising are crucial procedures in image storytelling since they enhance the quality of the used images. It involves various techniques, such as image denoising, color correction, and contrast adjustment, which are used to improve the overall appearance and readability of the image. Denoising an image by reducing undesired noise can, for example, diminish the visual impact of the image for a storytelling purpose.
###### Abstract  
The relevant value standards for reserved deformation of tunnels with different types of loess soil are not yet perfect. Through mathematical statistics, literature research, and other methods, 148 monitoring sections of loess tunnels with different soil types were investigated. Pearson, Kendall, and Spearman correlation coefficients were used to analyze the influence of surrounding rock grade, moisture content, and burial depth on the deformation law of tunnels, providing reference for the value of reserved deformation for loess tunnels. The research results indicate that: (1) The correlation between soil type and convergence around the tunnel is strong, followed by excavation span, and the correlation between moisture content and burial depth is weak. Therefore, the design of reserved deformation should focus on considering the soil type and excavation span. (2) For loess tunnel sections with class IV surrounding rock, the deformation allowance for cohesive loess and silty loess tunnel sections can be set within 15 cm, and for sandy loess, it can be set within 15-20 cm. (3) For the loess tunnel section with V-grade surrounding rock, the deformation margin can be determined as follows: viscous loess within 15-20 cm, silty loess within 15 cm, and sandy loess within 35-47.5 cm. (4) The moisture content is mainly distributed between 11% and 23%. It is worth noting that in sandy loess sections, the influence of moisture content on tunnel arch settlement is more significant compared to cohesive loess and silty loess sections. (5) The settlement of the arch crown is affected by changes in coverage depth and has discreteness. When the deformation margin is set to 16 cm, the assurance rate can reach 84.6%. Finally, the rationality of the proposed reserved deformation amount was verified through engineering practice. The research results can provide reference for related similar projects.  
loess tunnel; deformation allowance; crown settlement; surrounding convergence; statistical analysis; correlation coefficient Article
## 3 Related Work
### NBLA Open-Loop Power Control  
In the uplink scenario, the NB-IoT network normally only supports an open-link power control as stated in [49]. The reason for this open-loop power control exclusivity is mainly motivated by limited energy and processing capacity of most NB-IoT nodes such as sensor nodes which run on batteries most of the time. How this open loop power control is implemented within the NB-IoT nodes is that, based on the MCS and RU information alone. The NB-IoT node works out an estimate of the required power necessary to achieve an uplink transmission. This means that the Base Station (BS) (eNB in the case of LTE) does not send any form of power control command (information) to the NB-IoT before the uplink transmission.  
According to the authors in [49; 50], the transmit power (\(P_{NPUSCH,c}(i)\)) required by a NB-IoT node within a Narrowband Physical Uplink Shared channel (NPUSCH) during an uplink session within a given uplink slot \(i\) for serving a cell \(c\), given that the number of repetitions of the allocated NPUSCH RUs is less than 2 can be modelled as  
\[P_{NPUSCH,c}(i)=min\{P_{CMAX,c}(i),10\times log_{10}\times(M_{NPUSCH,c}(i))+P_{ O_{NPUSCH,c}}(j)+\alpha_{c}(j)PL_{c}\} \tag{23}\]  
where  
* \(P_{CMAX,c}(i)\) is the configured NB-IoT node uplink transmit power in slot \(i\) for serving cell \(c\),
* The possible values for \(M_{NPUSCH,c}(i)\) are \(\{\frac{1}{4},1,3,6\) or \(12\}\) as defined in [50],
* \(P_{O_{NPUSCH,c}}(j)\) is a parameter composed of the sum of two components from the higher layers within the NPUSCH data re-transmission channel model,
* \(PL_{c}\) is the downlink path loss estimate calculated in the UE for serving cell \(c\), and
* \(\alpha_{c}(j)\) is a coefficient configured by higher layers based on the estimated total loss over the link  
Should the number of repetitions of the allocated NPUSCH RUs be higher or equal to 2, then, the \(P_{NPUSCH,c}(i)\) can be simply modelled as  
\[P_{NPUSCH,c}(i)=P_{CMAX,c}(i) \tag{24}\]  
In summary, it is important to note that none of the existing approaches (neither traditional repetition schemes nor the NBLA) consider the channel conditions in the selection of the repetition number for the traditional repetition schemes as well as dynamically minimizing this value based on the improvement of the channel conditions. At the same time, the existing NBLA scheme does not dynamically vary the Modulation Coding Scheme number based on the channel conditions. This is where the contribution of this study comes in.
## 2 Materials and Methods
### Mathematical Model Development
#### 3.3.1 Diagnostic of Model Adequacy  
In general, the model must be validated to ensure that it provides an accurate approximation of the actual (experimental) values. Investigating and optimising parameters  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**\(T\) (\({}^{\circ}\)C)**} & \multicolumn{2}{c}{**pH**} & \multicolumn{2}{c}{**\(c\) (mmol L\({}^{-1}\))**} \\ \cline{2-7}  & **F Value** & \(p\)**-Value** & **F Value** & \(p\)**-Value** & **F Value** & \(p\)**-Value** \\ \hline MODEL & 42.66 & \(<\)0.0001 & 5.88 & 0.0053 & 48.88 & \(<\)0.0001 \\ \hline A–pH & 1.67 & 0.2247 & 29.16 & 0.0003 & 13.36 & 0.0044 \\ \hline B–\(t\) (min) & 118.28 & \(<\)0.0001 & 2.46 & 0.1481 & 195.89 & \(<\)0.0001 \\ \hline C–\(i\) (A cm\({}^{-2}\)) & 95.57 & \(<\)0.0001 & 1.23 & 0.3342 & 51.02 & \(<\)0.0001 \\ \hline AB & 0.9867 & 0.3440 & 4.87 & 0.0518 & 0.7386 & 0.4103 \\ \hline AC & 1.87 & 0.2042 & 1.48 & 0.2744 & 15.06 & 0.0010 \\ \hline BC & 9.30 & 0.0052 & 1.42 & 0.2855 & 11.33 & 0.0027 \\ \hline R\({}^{2}\) & 0.9746 & & 0.8412 & & 0.9778 \\ \hline Adj-R\({}^{2}\) & 0.9518 & & 0.6982 & & 0.9578 \\ \hline Pre-R\({}^{2}\) & 0.8639 & & \(-\)0.1692 & & 0.8070 \\ \hline C.V. (\%) & 6.07 & & 6.86 & & 6.32 \\ \hline Std. Dev. & 2.07 & & 0.5491 & & 0.3187 \\ \hline Mean & 34.08 & & 8.01 & \(-\)5.04 \\ \hline Adeq. Pre. & 19.3520 & & 8.1390 & 18.9734 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis of variance (ANOVA) for all measured responses.  
without assessing the model's fit could lead to misleading and unreliable results. Therefore, diagnostic plots such as the normalised plot and the parity plot between predicted and actual values are essential for verifying the model's fit and analysing the relationship between actual and predicted values. The left column of Figure 8 shows a graph comparing the observed (actual) response values with the predicted response values.  
This comparison helps identify observations that the model poorly predicts. Ideally, the data points should be evenly distributed around the 45-degree line. The right-hand column of Figure 8 displays the normal probability plot of the residuals, which indicates whether the residuals follow a normal distribution. If the residuals are normally distributed, the points should align along a straight line, although a moderate amount of scatter is expected even for normal data. No formal statistical tests are required for this diagnosis; visual inspection of the plot is sufficient. Externally standardised residuals provide the most reliable metric for this analysis.  
Figure 8: Observed response values versus predicted response values are on the left, and the normal plot versus residuals are on the right.
## 4 Privacy and Security Challenges
### Security Challenges  
From a security perspective, it is a challenge to preserve trusted client participation. We focus on two main types of poisoning attacks that could happen in this environment to force the model to misclassify, which can be explained as follows [28]:  
Figure 4: Taxonomy of federated learning, Blockchain, and IoT.  
Data poisoning (S1), or poisoning attacks: when the adversary adds specific noise to the dataset or alters its labels, better known as label-flipping attacks.
2. Model poisoning (S2): similar to data poisoning, model poisoning is when the adversary tries to actively alter the model updates to change the model decision outcome.
## 2 Related Work and State-Of-The-Art Technologies
### Parallelization and Synchronization
Another dataflow processing option implemented by the framework would be that the coordinator can also join multiple incoming dataflows into one output result dataflow by somehow combining dataflow elements with the same index. As demonstrated in Figure 5, the coordinator would obtain, in this case, two dataflows \(L_{flow}(i)\) and \(P_{flow}(i)\) in parallel. Each element of \(L_{flow}(i)\) has to be combined with its corresponding element in \(P_{flow}(i)\). Whenever elements arrive in the queues belonging to the dataflows, the coordinator will check if all corresponding elements already exist. Then they will be transferred together to a task instance that calculates a result which will be placed in the output queue. If not all pairing input elements are available, the arriving elements will be stored until a complete tuple is available. In conclusion, the coordinator can perform quite different strategies to distribute input data to the task instances and to collect and manage the corresponding results without causing data corruption or loss. The above strategies for coordination and dataflow processing were shown as example strategies, since they are used by the concrete example workflow for benchmarking the parallel execution performance of the framework as discussed further below. Many more dataflow coordination operations are possible and new ones could be easily implemented in future versions of the framework.
## 3 Performance Analysis of TDOA Method and Beamforming Method Based on the Simulated Signals
### Simulation of AE Source  
In this section, the commercial software Abaqus/Explicit is used to obtain the typical signals of the AE sources. In the simulation, the element type is C3D8R; the element size is 1 mm; the time increment step is set to be \(1\times 10^{-8}\) s; the sampling interval is \(2\times 10^{-7}\) s; and the total simulation time is \(3\times 10^{-4}\) s. The quadrupole force is used to simulate AE source. As shown in Figure 5a, the concentrated forces with 1 N magnitude are loaded on the four nodes, respectively. The time history of these forces, as triangular pulse form, is shown in Figure 5b. Here, the S0 wave data are selected for localization.