esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Testing of an Intelligent Control System for Maize Picking Harvest
## 2 Materials and Methods
### Software Algorithm
#### 2.2.4 Header Height Control  
When the piston of the header lifting the hydraulic cylinder is in different positions, the relative positions of the header components change, and the height and inclination angle of the pulling rollers also change. During the maize picking process, the piston action of the hydraulic cylinder on the header changes the height and angle of the maize picking, which affects the harvesting process. The movements of the header and bridge part were analyzed to elucidate the relationship between the height and inclination angle of the pulling rollers during the maize picking process.  
Figure 7 shows the movement analysis diagram for the header. The header and bridge are simplified as the quadrilateral ABCD. As the hydraulic cylinder piston extends or retracts, the angle \(\alpha_{\text{D}}\) changes. The geometric relations in the graph can be used to obtain the header height \(h\).  
For the triangle ABD,  
\[\alpha_{\text{D}} = arc\cos\frac{{l_{AD}}^{2}+{l_{BD}}^{2}-({l_{AB}}+{y_{L}})^{2}}{2{l _{AD}}{l_{BD}}} \tag{5}\] \[h = h_{\text{D}}-{l_{\text{CD}}}\cos(\alpha_{D}+\alpha_{AD}+\alpha_{ CD}) \tag{6}\]  
where \(y_{L}\) is the piston displacement of the hydraulic cylinder, \(h\) is the header height (i.e., the vertical distance between point C and the ground), and \(h_{\text{D}}\) is the vertical distance from the bridge hinge point (i.e., distance from point D to the ground).  
A PID controller is a control loop feedback mechanism commonly used in various control systems [32; 33]. This is a simple and highly reliable algorithm, and it can reduce the response time. A PID controller was applied to minimize the error in the header height and improve the dynamic response of the hydraulic system as well as the resulting maize picking quality. The header height controller receives the target height from the intelligent controller and the actual height from the ultrasonic sensors. It then calculates the deviation \(h_{e}\) between the target and actual header heights. A linear combination of the proportional error, integral error, and differential error is applied as the control inputs to adjust the height error to approach 0.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{4}{c}{\(n_{e}\)} \\ \hline \(n_{ec}\) & \(\Delta K_{p}\)\(\Delta K_{d}\)\(\Delta K_{i}\) & & & \\ \hline  & **NL** & **NS** & **ZE** & **PS** & **PL** \\ \hline NL & PL/NL/PS & PL/NLZE & PL/NL/ZE & PS/NL/ZE & ZE/ZE/PS \\ NS & PL/NL/NL & PS/NS/NL & PS/NS/NS & ZE/ZE/ZE & NL/PS/PS \\ ZE & PS/NS/NL & PS/NS/NL & ZE/ZE/NS & NS/PS/ZE & NL/PS/PS \\ PS & PS/NS/NL & ZE/ZE/NS & NS/PS/NS & NS/PS/ZE & NL/PL/PS \\ PL & ZE/ZE/PS & NS/PS/ZE & NS/PS/ZE & NL/PL/ZE & NL/PL/PS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy rules of \(\Delta K_{p}\), \(\Delta K_{d}\), and \(\Delta K_{i}\).  
Figure 7: Movement analysis diagram of header.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 4 Incremental Frequent Itemset Mining with Map-Reduce
### The Algorithms
#### 4.2.1 General Scheme
Step 8 (Join's reducer) read an itemset and all its database marks, and determined the further processing required for each itemset. It is composed of three cases:  
1. In case that it is locally frequent in \(\Delta\) and old \(\mathcal{D}\), then it is globally frequent so it may be outputted immediately (step 9). 2. If it is locally frequent only in \(\Delta\), then we need to count it in old \(\mathcal{D}\) (steps 10-11 by using the additional count MR job). 3. If it is locally frequent in old \(\mathcal{D}\), we need to count it in \(\Delta\) (steps 12-13 by using the same MR job as in steps 10-11 with other input). All three outputs (9, 11 and 13) are collected in step 14, and represent together \(\text{FI}(\mathcal{D}^{+},minSup)\). The proposed scheme contains at least three different kinds of MR jobs:  
1. Find _deltaFI_ by using any suitable MR algorithm (may have more than one job inside). 2. Join MR job. Any Join algorithm may be in use. The Mapper output is just a copy of the input (Identity function); the Reducer should have three output files/directories (instead of just one) for each case. Count itemsets inside the database. The same MR count algorithm may be used for both old \(\mathcal{D}\) and \(\Delta\). Counts in both DBs could be executed in parallel on the same MR cluster. There is (at most) one pass over the old \(\mathcal{D}\) for counting--Steps 10-13 use the same algorithm for counting. The mapper reads a list of itemsets for counting and counts them in its data split. The reducer summarizes each itemset, and leaves only \"globally\" frequent itemsets. There is no requirement for any additional input for general FIM (e.g., previous knowledge, _PK_), but any advanced algorithm may use any additional acquired knowledge from mining old \(\mathcal{D}\) as an input to the incremental algorithm.
# Passive Power Line Communication Filter Design and Benchmarking Using Scattering Parameters
## 5 PLC Filter Design, Simulation, and Evaluation
### PLC Simulation Using the S-Parameters Simulator  
S\({}_{11}\) parameters for each component were measured using a VNA and the obtained Touchstone files [65] are used in simulating the filter in the RFSim99 S-Parameters simulator. RFSim99 is a linear S-parameter-based circuit simulator which solves matrix equations for two-port devices.  
Figure 15 is the graphical representation of a two-port network [66; 67]:  
* \(a_{1}\) and \(a_{2}\) are the incident waves for Port1 and Port2;
* \(b_{1}\) and \(b_{2}\) are the reflected waves for Port1 and Port2;
* \(S_{21}\) is the forward transmission coefficient (from port 1 to port 2) and \(S_{12}\) is the reverse transmission coefficient (from port 2 to port 1);
* \(S_{11}\) is the input reflection coefficient and \(S_{22}\) is the output reflection coefficient.  
Below is the matrix algebraic representation of two port S-parameters [66; 67] which is also used by the simulator:  
\[S_{11}=\frac{b_{1}}{a_{1}} \tag{16}\]  
\[S_{12}=\frac{b_{1}}{a_{2}} \tag{17}\]  
\[S_{21}=\frac{b_{2}}{a_{1}} \tag{18}\]  
\[S_{22}=\frac{b_{2}}{a_{2}} \tag{19}\]  
\[\begin{pmatrix}b_{1}\\ b_{2}\end{pmatrix}=\begin{pmatrix}S_{11}&S_{12}\\ S_{21}&S_{22}\end{pmatrix}\times\begin{pmatrix}a_{1}\\ a_{2}\end{pmatrix} \tag{20}\]  
For the measurements, PLC filter online insertion loss test setup version 2, shown in Figure 7, was utilized. The actual implementation of the test setup is depicted in Figure 16.  
Figure 14: Designed PLC filter.  
Figure 15: Generalized two-port network.  
Figure 17 presents the results of the simulated and measured insertion loss for the proposed filter.  
Measurements and graphical representation were performed with the following settings and conventions:  
* Measurement equipment reference impedance: \(Z_{0}=50\)\(\Omega\);
* The measured S21 [dB] parameter is considered to be the insertion loss of the filter [68];
* Due to the dynamic range and noise floor of the VNA, measurements can be performed starting low at \(-130\) dB [69]. Although the simulated S21 magnitude values are as low as \(-300\) dB, all magnitude values \(\leq-140\) dB were scaled to \(-140\) dB in order to facilitate graphical representation.
# Efficient Implementation of PRESENT and GIFT on Quantum Computers
## 3 Proposed Method
### Quantum Circuit for GIFT Block Cipher
#### 3.2.4 Constant XOR of GIFT Block Cipher  
The round constant \(C\) in Table 5 and the single bit are XORed to block \(B\). Since the constant \(C\) for each round is already set, we performed \(X\) gates on \(b_{23},b_{19},b_{15},b_{11},b_{7}\), and \(b_{3}\) only for positions where bit of \(C\) is one. When the round constant \(C\) is 3 in round 2, \(c_{0}\) and \(c_{1}\) are 1. Therefore, the \(X\) gate (\(b_{3}\)) and \(X\) gate (\(b_{7}\)) are performed. For a single bit, an \(X\) gate is always performed on \(b_{n-1}\). In this way, no qubits are used for Constant XOR. Moreover, CNOT gates are not used; only \(X\) gates are used. The implementation of the GIFT-\(n/128\) constant XOR quantum circuit is described in Algorithm 9  
```
1:\(b_{n-1},b_{23},b_{19},b_{15},b_{11},b_{7},b_{3}\) of \(n\)-bit block \(B\).
2:\(b_{n-1},b_{23},b_{19},b_{15},b_{11},b_{7},b_{3}\) of \(n\)-bit block B after Constant XOR.
3:\(b_{23},b_{19},b_{15},b_{11},b_{7},b_{3}\leftarrow\)\(X\) (\(b_{23},b_{19},b_{15},b_{11},b_{7},b_{3}\)) according to round constant \(C\) (\(c_{5},c_{4},c_{3},c_{2},c_{1},c_{0}\))
4:\(b_{n-1}\gets X\) (\(b_{n-1}\))
5:return\(b_{n-1},b_{23},b_{19},b_{15},b_{11},b_{7},b_{3}\)
```  
**Algorithm 9** Quantum circuits for constant XOR of GIFT-\(n/128\) block cipher
# Railway Bridge Condition Monitoring Using Numerically Calculated Responses from Batches of Trains
## 5 Bridge Health Monitoring  
When trains are traveling on track only (off-bridge), their data can be used to monitor AP, which is a valuable measure of track condition. In addition, as was shown above, off-bridge data can be used to determine local train dynamic properties. When a train crosses a bridge, it is subject to another AP, this time made up of a combination of the track profile, track deformation, and elements of bridge deflection. Because the AP includes bridge deflections, it contains information about the bridge response to applied load and ultimately about the bridge condition.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 6 Performance Analysis
### Performance of the ARM Cortex-M for Asymmetric Cryptography
#### 6.1.1 Execution Time  
Execution time represents the actual time consumed in encryption, decryption and key generation with ECDSA, which implemented using a HP pavilion laptop with a Core(TM) i7-7700 HQ CPU @ 2.80 GHz, 16 GB RAM, 64-bit. We recorded 10 execution times for ECDSA for each of the processors (M0, M3, M4, and M7) and computed the mean value for each. The lowest execution times were obtained for the M4 and M7 processors, while the highest was obtained for M0 and M3 as shown in Table 1, from Figure 5 we can easily see that the optimal choices for executing the ECDSA were the M4 and M7 processors, due to their high performance. We selected the M4 processor for our architecture in order to strike a balance between the two factors of execution time and power consumption.
## 5 Security Analysis
### Signature Anonymity  
One of the main objectives of the scheme is to ensure the anonymity of signatures by blinding the message \(M\) before signing. Blinding involves multiplying the original message \(M\) by a random factor \(r\), so that the signature \(\sigma^{\prime}\) is generated on a blinded message \(M^{\prime}=M\cdot r\ mod\ n\). The factor \(r\) is chosen by the recipient and is known only to him, which makes it impossible for the signer to deduce \(M\) from \(M^{\prime}\).  
Blinding guarantees anonymity through two fundamental properties:  
1. Mathematical independence between \(M\) and \(M^{\prime}\): If \(r\) is random and uniformly distributed in the domain \(\mathbb{Z}_{\mu}^{*}\), then \(M^{\prime}\) is uniformly distributed in the same domain, regardless of the value of \(M\). This ensures that \(M\) cannot be deduced by the signer.
2. Reversibility of blinding: Only the recipient can remove the blinding by applying \(r^{-1}\), obtaining the final signature \(\sigma=\sigma^{\prime}\cdot r^{-1}\ mod\ n\). Since \(r\) is secret, only the recipient can perform this operation.  
The non-forgeability of the signature is ensured by using cryptographic keys based on difficult mathematical problems, such as the discrete logarithm problem or the factorization of large numbers (depending on the algorithm used, RSA or ECC). The final signature \(\sigma\) is validated using the public key \(K_{pub}\), by checking the equation:  
\[\sigma^{e}\ mod\ n=M.\]  
This equation is valid only if \(sigma\) was generated with the private key \(K_{priv}\). Otherwise, the probability that an attacker will generate a valid signature is practically equal to the inverse of the theoretical attack resistance of the model used, since this would require solving a cryptographic problem consistent with the implemented vector system.
## 3 VM Consolidation Model
### Virtual Machine Placement  
This study targets a virtualized environment that assigns PM resources to a large number of VMs and considers CPU, memory, and network as the resources. The CPU is the most influential factor for CM consolidation since it is the resource that has the largest impact on performance and energy consumption of the PM. Consequently, most of the research on VM placement consider the CPU. Memory is also considered here since it is the resource affecting the performance of the PM and is a major factor in VM migration. Swap memory in the storage drive is used when the PM does not have spare memory, which slows down, causing decrease in the QoS and failures. Network bandwidth is also an element to consider since its DC network environment is remote, but it is only considered as a resource affecting VM migration and not a resource that is assigned, like the CPU and memory.  
The constraint for VM placement is defined as follows:  
\[\sum_{i=1}^{P}x_{ij}=1\qquad\forall j,t \tag{1}\]  
Equation (1) represents that the sum of every VM assigned to PMs is 1 and also indicates that only one VM can be assigned to a PM. \(x_{ij}\) is a binary variable representing whether the \(j\)th VM is assigned to \(i\)th PM (if it is assigned, 1; otherwise, 0).  
\[\sum_{j=1}^{V}c_{j}\cdot x_{ij}\leq C_{i}\cdot thr\qquad\forall i \tag{2}\]  
\[\sum_{j=1}^{V}m_{j}\cdot x_{ij}\leq M_{i}\cdot thr\qquad\forall i \tag{3}\]Equations (2) and (3) are the constraints for the PM capacity, and the sum of the total resources (CPU and memory) of VMs assigned to \(i\)th PM cannot exceed the total amount of resources for the \(i\)th PM. In addition, the spare resources of the PM to prepare for a sudden increase in resource demand is reflected in the total amount of resources for the \(i\)th PM as a threshold.
## 3 Results and Discussion
### Mesogen Orientation of PAz Films
#### 3.1.2 PAz Film Sandwiched between Two Glass Substrates  
The PAz film was sandwiched between two glass substrates as described in Section 2.2. Figure 2 shows the 2D GI-SAXS and 1D profiles of this sample at room temperature (a) and at 90 \({}^{\circ}\)C (b). In contrast to the results for the film contacting with air (Section 3.1.1), the lamellar structure was more randomly oriented and rather preferentially in the in-plane direction. At room temperature, another spot at 55\({}^{\circ}\) from the normal was also observed for the (001) scattering. These facts indicate that the lamellar plane was preferentially oriented vertically with a tilted structure, reflecting the smectic C phase (\(d\) = 2.7 nm). At 90 \({}^{\circ}\)C, on the other hand, the scattering tilt structure disappeared, and scatterings showing a periodicity at \(d\) = 3.4 nm were observed both in the in-plane and out-of-plane directions. This shows that the LC structure was changed to a smectic A phase at the higher temperature. In this way, the free (air) surface and the solid substrate gave the homeotropic and planar anchoring for the Az mesogens, respectively, which is consistent with the theoretical considerations based on the excluded volume effect [31; 32; 34].  
Figure 1: GI-SAXS data of PAz films with thicknesses of 100 nm (**a**) and 400 nm (**b**). In each part, left and right figures display 2D GI-SAXS patterns and 1D intensity profiles taken from the 2D data, respectively. In the left profiles, red and black data show the out-of-plane and in-plane directions, respectively.  
Figure 2: GI-SAXS data of PAz film sandwiched between two glass substrates at room temperature (**a**) and at 90 \({}^{\circ}\)C (**b**). In each part, left and right figures display 2D GI-SAXS patterns and 1D intensity profiles taken from the 2D data, respectively. In the left profiles, red and black data show the out-of-plane and in-plane directions, respectively.
## 3 Methods  
Section 3 is divided into three key parts: The first part discusses the CNN techniques examined, specifically VGG and ResNet. The second part centers on delineating the used cases for evaluating CNN's performance in handling partially or low-quality fingerprint images. In the third part, we discuss the DCAI approaches implemented to enhance our findings' performance. The input of our method is a single fingerprint image, and the output a gender classification. This study employed supervised learning and CNN methods for model training--in particular, networks such as VGG [24] and ResNet [25], which have maintained their significance within the realm of deep learning for image classification. The architectures emphasize depth through the recurrent use of small receptive field convolutions and pooling layers, simplifying implementation and comprehension. ResNet [25] introduces shortcut connections known as residual blocks; however, VGG's [24] adaptability remains a significant advantage. By incorporating pre-trained weights into both models, they can both be seamlessly integrated into diverse networks and deep learning tasks, emphasizing their versatility. VGG and ResNet should be carefully considered in the context of smaller datasets. The less complex architecture and shallower depth of pre-trained VGG models may be advantageous in reducing overfitting, which is particularly beneficial for smaller databases. In contrast, ResNet's depth enables it to learn intricate patterns. VGG and ResNet's selection for smaller datasets hinges on factors such as computational resources, data complexity, and project-specific requirements. In the broader context of image classification, the choice between VGG and ResNet is emblematic of the ongoing evolution of deep learning, with each framework offering distinct advantages based on dataset characteristics and computational constraints. Our approach involves fine-tuning CNN architectures tailored to the specific image classification objectives. To train our CNN models, we employed standard training techniques [13], including data augmentation, normalization, a method for class imbalance [26], stochastic AdaGrad optimization, and others. With a learning rate of 0.0001 and a batch size of 32, the models were trained using the default parameters. The process of choosing the specific CNN architecture (i.e., VGG or ResNet) was selected based on experimentation and validation, ensuring optimal performance in classifying gender fingerprint images. Given the imbalanced dataset, evaluating the predictive model's performance posed challenges, as traditional accuracy measures may have been misleading as they can mirror the inherent class distribution. Therefore, we opted for the F-score as our evaluation metric, which
## 5 Discussion
### Economic and Operational Implications  
The economic implications of our approach extend beyond simple resource optimization. While the initial implementation costs are higher than traditional methods, the long-term benefits are substantial. The 15% reduction in required fixtures translates to approximately SGD 280,000 in construction cost savings for a typical office building of similar size to our study site. Additionally, the annual maintenance savings of SGD 42,000 demonstrate the ongoing financial benefits of more accurate facility planning.  
The space optimization achieved through more accurate fixture prediction has broader implications for building design. The recovery of approximately 45 square meters of floor space represents not only direct cost savings but also opportunities for alternative space utilization. Several building managers in our study reported repurposing saved space for additional meeting rooms or collaborative areas, adding value beyond the direct savings in sanitary facility costs.
# New Insights in Potato Leaf Freezing by Infrared Thermography
## Materials and Methods
### Detection of Freezing Exotherms by IDTA  
For more detailed analysis of the spatio-temporal freezing behavior of leaves of _S. tuberosum_, infrared differential thermal analysis (IDTA; [10; 11; 12]) was applied. In contrast to DTA, IDTA allows for the determination of the location of ice nucleation, ice propagation but also supercooling. A digital infrared camera (ThermoCAM S60, FLIR Systems, Danderyd, Sweden) was used for thermal recording. The differential sequences were obtained by subtraction of images. Subtraction and analysis was conducted with the software ThermoCAM Researcher Pro (version2.10, FLIR Systems, Danderyd, Sweden). Experiments were repeated three times with single leaves.
## 6 Conclusions  
This paper studied the cutting temperature of micro-textured tools when cutting GH4169 in spray cooling. The research was conducted in finite element simulations and experiments. The influence of micro-textures with different morphologies and size parameters on the cutting temperature was revealed. The optimized morphology and size parameters of micro-textures were obtained. The following conclusions were drawn:  
1. Compared with non-textured tools, the use of micro-textured tools in cutting reduced both the average cutting temperature and the temperature at the tool nose. This results from two factors. On the one hand, the micro-textured tools increase the curl rate of the unit chip, which leads to an earlier separation of the chip from the rake face of the tool, thus reducing the contact area of the friction pair and lowering the cutting temperature. On the other hand, in the area where the chips are in close contact with the rake face, the micro-texture forms a vacuum contact, and it also means the existence of the vacuum contact becomes an important factor in cutting temperature reduction;
2. Furthermore, the morphology of micro-textures has an effect on temperature reduction. Among the five designed morphologies, the comprehensive cooling performance of T3 (linear groove parallel to the tool nose) is significantly superior to the other morphologies. Compared with the non-textured tools, the temperature reduction in T3 is 23%, and those of T1, T2, T4, and T5 are 17%, 9%, 21%, and 15%, respectively;
3. In addition, the size parameters of micro-textures have an effect on temperature reduction as well. Among the 14 combinations of dimensional parameters designed  
Figure 13: Temperature of tool nose with different size parameters of micro-groove-parallel textured tools.  
for T3, the best combination with the lowest cutting temperature is as follows: \(A\) is 140 \(\upmu\)m, \(B\) is 120 \(\upmu\)m, C1 is 50 \(\upmu\)m, and C2 is 300 \(\upmu\)m;
* Experiments were conducted in the same working conditions as the simulation. Since the experimental results conformed to those of the simulation analysis, it can verify the accuracy and reliability of the simulation model.  
Methodology, [PERSON] ([PERSON]); software, [PERSON] ([PERSON]); validation, [PERSON]; writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Science Foundation of China (Grant No. 516/5144).  
Not applicable.  
The authors declare no conflict of interest.