esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Key Indicator**} & \multicolumn{6}{c}{**Simulation Models: Cumulative Improvements with the GUVEI-Model**} \\  & & **Initial** & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** & **Cluster 5** \\  & & **Situation** & & & & & & \\ \hline
9 & Implementation time (weeks) & 0 & 12 & 20 & 44 & 56 & 74 \\
10 & Labor productivity (tons/empl. \(\times\) day) & 21.1 & 26.2 & 29.7 & 47.5 & 51.3 & 61.5 \\
11 & \(\mathcal{O}\) WIP stock (Mio. tons) & 0.7 & 0.8 & 0.9 & 1.3 & 1.3 & 1.6 \\
12 & \(\mathcal{O}\) Production lead time (\# weeks) & 174.6 & 168.1 & 170.2 & 109.7 & 99.7 & 34.1 \\
13 & Cumulated service level (\%) & 79.6 & 85.6 & 87.2 & 95.8 & 96.2 & 96.8 \\
14 & \(\sum\) Sales (million euros) & 1051 & 1647 & 2069 & 3957 & 4300 & 4802 \\
15 & \(\sum\) Operational costs (million euros) & 2544 & 2369 & 2215 & 1868 & 1866 & 2231 \\
16 & \(\sum\) Profits (million euros) & \(-\)1493 & \(-\)722 & \(-\)146 & 2089 & 2434 & 2571 \\
17 & \(\sum\) Investment (million euros) & 0 & 100 & 150 & 750 & 950 & 1250 \\
18 & Return on investment (ROI) (\%) & - & 5.2 & 6.0 & 4.1 & 3.6 & 2.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._gaps for reaching the target levels can be derived. With an identified gap and related strategic decision-making, a strategic program can be established. After this step, a top-down approach for the definition of improvement project initiatives can be initiated and validated for greenfield future factories. However, brownfield companies also have ongoing improvement and Industry 4.0 related projects. Therefore, in a certain rolling period, it is necessary to assess how these running projects contribute to organizational targets and if they should be stopped, changed, postponed, or if they should continue their established planning process. For this kind of decision-making, it is key to assess if the strategic roadmap and the on-going projects match the needs of the organization in order to reach the organizational capabilities that enable a company to achieve its goals. In this context, the optimization model developed provides a framework for the identification of weak areas, the determination of suitable steps for their improvement and an evaluation scheme for project initiation. Managers can develop a digital twin model for the continuous monitoring of organizational maturity levels, enabling the assisted identification of functional gaps and deviations, as well as the automated simulation of alternatives for improvement. In this context, an important step when considering the project decision-making, as well as its successful implementation, and latter sustainable application is the human factor role. Finally, resources constraints, i.e., staff, time, money, etc., should be considered when developing the improvement project's final roadmap.
## 6 Performance Analysis
### Numerical Results
#### 6.4.4 Impact of Efficient Grouping on the Overall mMTC Transmission Delay  
The transmission delay is the average time for successful data packet reception. As for the energy consumption, Figure 8 shows the access delays as the traffic load increases exponentially as the collision percentage increases from 2% to 10%. Every time a device fails the RACH due to collisions, it enters the waiting period set by the back-off timer while new devices keep arriving. Consequently, collided devices in the back-off mode are joined by new devices, further increasing the congestion. As a result, more devices will be forced to enter the back-off mode, increasing the delay.  
Figure 9 shows the probability density function (PDF) of the latency for each method across a wide range of different grouping scenarios (30, 50, 60, 70, 90, 100/group). It is demonstrated that the proposed mechanism outperforms the other methods with reduced latency in terms of access delay. The area under the curve gives the probability of the variable falling within the interval of 58,000 ms to 63,000 ms, while other methods exceed 66,000 ms. The peak in the plot shows the high likelihood of our method's latency being around 60,000 ms. This is mainly because our method leverages the information related to the device's time constraint during the grouping, which results in fewer failed attempts and, hence, a lower access delay.  
Figure 8: Overall RACH-access delay per collision percentage.  
Figure 7: Total energy consumption per method.
## 2 Description of the Herringbone Retaining System (HRS)
### Structural Features  
Compared to conventional systems, the HRS has the following characteristics:  
1. Strong stiffness. Linking the vertical piles and batter piles to work as a whole strengthens the structural stiffness of the structure and controls the amount of excavation-induced soil movement.
2. Highly hyperstatic and reliable. All batter piles are embedded in the soil and independent of each other, so progressive collapse will not occur if a single component fails.
3. Its structure is compact. The batter piles do not occupy a lot of internal pit space which means that enough room is left to allow the use of large items of mechanical equipment.
4. High efficiency and low cost. Steel-pipe batter piles can pass through the building walls or foundation slabs with limited impact on the inner structure properties [16], when they occupy the space of the inner structure with large inclination. No demolition is needed or parts of the batter piles need be demolished after completing the inner structures construction. Thus, the construction period is effectively shortened and demolition costs are reduced [4].  
Figure 6: Basic configuration of the herringbone retaining system (HRS): (a) oblique view; (b) cross-sectional profile. (Note: (i) is the direct connection method; (ii) is the wedge plate method; (iii) is the concrete filling method.).
###### Abstract  
The aim of the paper was the implementation of low-cost smart sensors for the collection of bathymetric data in shallow water and the development of a 3D modelling methodology for the reconstruction of natural and artificial aquatic scenarios. To achieve the aim, a system called GNSS > Sonar > Phone System (G > S > P Sys) was implemented to synchronise sonar sensors (Deeper Smart Sonars CHIRP+ and Pro+ 2) with an external GNSS receiver (SimpleRTK2B) via smartphone. The bathymetric data collection performances of the G > S > P Sys and the Deeper Smart Sonars were studied through specific tests. Finally, a data-driven method based on a machine learning approach to mapping was developed for the 3D modelling of the bathymetric data produced by the G > S > P Sys. The developed 3D modelling method proved to be flexible, easily implementable and capable of producing models of natural surfaces and submerged artificial structures with centimetre accuracy and precision.  
smartphone; smart sensors; bathymetry; machine learning; 3D modelling 2023
###### Abstract  
In this paper, we propose a stacked dual-patch antenna with 3D printed thick quasi-air substrates and a cavity wall for wideband applications. To achieve the theoretical maximum bandwidth of the patch antenna, the quality factor of the system needs to be minimized. To achieve this, the area of the conductive radiator should be enlarged, while the permittivity of the substrate within the patch must be reduced close to 1. To realize a patch antenna with this maximum bandwidth, the stacked dual-patch configuration is employed to obtain an extended conductive radiator area. In addition, square-pipe resin frames manufactured using a 3D printing method are applied to the proposed antenna to implement a quasi-air substrate structure that has a low permittivity value close to 1. The proposed stacked dual-patch antenna with a quasi-air substrate has a broad bandwidth of 20.7%. The results demonstrate that by using the proposed antenna structure, broadband characteristics close to the fundamental bandwidth limit of the patch antenna can be achieved.  
Keywords:dual-patch antennas; broadband antennas; quasi-air substrates; 3D printing methods +
Footnote †: journal: Applied
# Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM
## 2 Proposed Facial Expression Recognition System Using the Mixture of CNN and SVM Classifier
### Preprocessing  
#### 2.2.1 Face Normalization  
To focus on the local facial motion for facial expression recognition, the global head motion is removed by face normalization. When the landmark points are provided in a database such as CK+ [43], we compute the left and right eye centers, and scale and align them such that the eye centers are at a fixed location in the normalized images. In a database with small or no head motion, the eye location is estimated from the first frame and applied to the given sequence with the same global transformation. In a real situation, the use of a face detector at the initial frame can provide the required information for face normalization. Figure 2 shows examples of face normalization from the CK+ database.  
Figure 1: Block diagram of facial expression recognition using mixture of CNN and SVM classifier.  
Figure 2: Face normalization and motion flow estimation. (**a**) Original input video sequences. (**b**) Normalized faces. (**c**) Estimated dense optical flow. Optical flow is color-coded by its direction and intensity as in [55].  
When a large head motion is encountered, a simple normalization with the initial frame does not eliminate the motion flow due to the global head motion. Recent research has resulted in an advanced landmark detection and localization algorithm [56; 57; 58], which can be used to remove large head motion artifacts in real situations.
# Systematic Review on Individualized Versus Standardized Parental Nutrition in Preterm Infants
## 4 Discussion
### Conclusion 1: The LoE of the Available Studies Comparing SPN vs. IPN Is Very Low (LoE1- and LoE 2-)
#### 4.3.1 Pharmaceutical IPN Individualization  
Consistently, the data reconfirmed the positive association between protein intake and growth [28]. Higher protein intake was associated with improved weight gain. Therefore, pharmaceutical IPN individualization improved weight gain in the randomized trial by [PERSON] et al. (11.8 \(\pm\) 5.2 vs. 4.9 \(\pm\) 8.0 g/kg/day, \(p\) < 0.02) [20] and in the observational trial by [PERSON] et al. (Figure 5). The latter study in a cohort of 140 low birth weight infants born between 2000 and 2007, showed significantly greater weight gain SDS during the 1 st week (\(p\) =0.036) and the 1 st month of life (\(p\) = 0.0004), and higher discharge weight SDS (\(p\) = 0.012) and OFC SDS (\(p\) = 0.006) in IPN.
## 5 Experimental Evaluations and Results
### Implementation Details  
All experiments are performed on Google Colaboratory with GPU runtime using 28 GB RAM and 68 GB hard disk. All the models are implemented using the Python 3.6, Keras deep learning library with TensorFlow [[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/)) (accessed date: 18 February 2021). Architectures of deep learning CNN models were downloaded from the internet and trained with hyper-parameters, such as \(32-64\) batch size, \(1\times 10^{-4}\), \(1\times 10^{-5}\) learning rate, 20-30 epochs, RMSProp or ADAM optimizer, binary cross-entropy loss, and  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Parasitized** & **Uninfected** \\ \hline Training & 8604 & 8766 \\ Testing & 4196 & 4076 \\ Validation & 979 & 952 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Partitioning of the test dataset into training, testing, and validation datasets before performing data augmentation.  
Figure 6: Sample images of parasitized (infected) and uninfected red blood cells from NIH Malaria dataset [54].  
0.3-0.5 dropout ratio for regularization. The models were initialized with random weights and trained on the training data using the tensorflow library.  
The cross-validation technique uses an independent dataset to evaluate the performance of the machine learning models on unseen data. To this end, the sample dataset is partitioned into \(k\) subsets of data and then training is performed on one subset of the data and validation on the other subset of the data. We evaluated the predictive models through five-fold cross-validation over 5 different test sets, with each fold having 2756 test samples and 24,802 training samples. The training data are randomly partitioned into 5 equal-sized subsets, one subset is used for validation testing and the remaining 4 subsets are used for training. The cross-validation process is then repeated 5 times for the proposed model, with each of the 5 subsets used exactly once as the validation data. The results of the validations were averaged to produce a single score.
## 4 Simulation Setup, Result Analysis, and Discussion
### Average Energy Consumption  
We determined the average energy consumption of the proposed SDAA protocol. We compared it with the other contending standard secure MAC protocols, including SEFSC, SDA, and EEST, based on the simulation result obtained in the network. The average energy consumption in the presence of a malicious node attacker (MNA)of the network is determined as the amount of energy consumed by the underwater sensor nodes (USN) during data transmissions. This is expressed as the average energy consumption of all the network USNs and the mobile nodes during the simulation. Figure 7 depicts the average energy consumption for all the approaches.  
The trend in the graph shows that more energy is consumed when the number of attackers increases from 1 to 18. Based upon this, the proposed SDAA protocol consumes 30 J, and the energy consumption of SEFSC, SDA, and EEST is 37, 42, and 44, respectively. Hence, the SDAA protocol consumes 7-14 J less energy than other competing approaches.  
Figure 6: Average dropped packets for SDAA and comparison with various attackers.  
Figure 7: Average energy consumption of SDAA and competing approaches with various attackers.
## 6 Conclusions  
In conclusion, our study demonstrates that FL within an edge-fog-cloud architecture offers a compelling lightweight alternative to centralized learning methodologies. While centralized learning exhibits superior performance metrics such as accuracy, precision, recall, and F1-score, with percentages reaching as high as 99.36%, FL within an edge-fog-cloud framework maintains commendable performance levels, with accuracy, precision, recall, and F1-score values recorded at 97.65%, 97.65%, 100%, and 98.81%, respectively, after five rounds of training. Furthermore, FL demonstrates a significant advantage in terms of training time, with an average training time of 155 s across edge devices compared to the much longer average of 4041.07 s for centralized learning. This advantage is attributed to FL's distributed learning paradigm, enabling parallel processing capabilities and faster convergence. Additionally, FL optimizes CPU and memory resources by distributing computational demands and data storage across edge devices, leading to efficient resource utilization and enhanced system scalability. Moreover, the implementation of FLwithin an edge-fog-cloud architecture addresses latency concerns by distributing resources closer to the data source or end-users, thereby reducing round-trip time and improving application responsiveness. This architecture ensures efficient resource utilization while mitigating the risk of data breaches and preserving user privacy through the integration of FL and blockchain technology. By adopting privacy-preserving techniques such as secure aggregation and differential privacy, our proposed system upholds the confidentiality and decentralization of sensitive data, while the utilization of blockchain technology enhances transparency and trustworthiness through a tamper-proof record of all transactions and interactions. In essence, our study showcases the effectiveness of FL within an edge-fog-cloud architecture as a lightweight, efficient, and trustworthy solution for CIDSs in IoT environments, poised to make significant contributions to the security and integrity of IoT systems.  
Conceptualization, A.A.W. and G.K.; methodology, A.A.W. and G.K.; software, A.A.W.; validation, A.A.W., G.K. and P.S.; formal analysis, A.A.W.; investigation, A.A.W.; resources, A.A.W.; data curation, A.A.W.; writing--original draft preparation, A.A.W.; writing--review and editing, G.K. and P.S.; visualization, A.A.W.; supervision, G.K.; project administration, A.A.W.; funding acquisition, G.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research used a public dataset download from [[https://www.unb.ca/cic/datasets/iotdataset-2023.html](https://www.unb.ca/cic/datasets/iotdataset-2023.html)]([https://www.unb.ca/cic/datasets/iotdataset-2023.html](https://www.unb.ca/cic/datasets/iotdataset-2023.html)) (accessed on 1 December 2023).  
The work was supported by the project Minigrants for doctoral students of the Wroclaw University of Science and Technology.  
The authors declare no conflicts of interest.
# Effect of Fiber Wave Structure in Printed Circuit Boards on Signal Transmission Characteristics
## 2 Materials and Methods  
### Materials and Equipment  
We focused on the fiber weave effect only for high-speed PCBs, so the research must be based on high-speed PCBs. Here, high-speed laminates and prepreg based on PPO (short for Polyphenylene Oxide) resins that were supplied by Panasonic were used to produce multilayer PCB.  
A vector network analyzer (VNA, E5071C) from Agilent was used to measure TDR (short for time domain reflectometry), the S-parameter, and phase difference. To ensure the accuracy of the measurements, the tested frequency bandwidth was 20 GHz, and the rise time was 22.3 ps.
# A Laser Irradiation Method for Controlling _Pieris rapae_ Larvae
## 5 Conclusions  
In the study, a physical control method of controlling _P. rapae_ larvae by laser irradiation is proposed. By testing the controlling effect of 450 nm laser on _P. rapae_ larvae and studying the influencing factors of lasers (laser power, irradiation area, laser opening time and irradiation position), the optimal combination of working parameters was obtained to improve the controlling performance of _P. rapae_ larvae.  
The main research conclusions were drawn:  
1. The optimal combination for the maximum comprehensive score was as follows: laser power, 7.5 W; irradiation area, 6.189 mm\({}^{2}\); laser opening time, 1.177 s; and irradiation position, middle abdomen.
2. The optimal combination identified based on the observations was used to verify the experiment. The results showed that the decrease in the antifeedant percentage of _P. rapae_ larvae after 24 h was 98.49%, whereas the 24 h mortality rate was 100%.
3. The combination of the experimental parameters was suitable for 1 st- to 5 th-instr _P. rapae_ larvae, and the mortality rate of the 5 th-instr larvae at 36 h also was 100%.  
The results indicate the capacity of laser irradiation to serve as a high-efficiency and noncontact physical control method against _P. rapae_. Although the high antifeedant percentage and mortality rate are good predictors of field success in laser, the control effect of laser irradiation against _P. rapae_ should be evaluated in the field. The approaches for this technology in processing plants or field remain to be studied further.  
Conceptualization, Y.L. and Y.X.; methodology, Y.L., J.L. and Y.X.; software, Y.L. and Z.H.; validation, Y.X.; investigation, Y.L. and Y.X.; resources, Y.X.; data curation, Y.L., J.L. and Y.X.; writing--original draft preparation, Y.L., Z.H. and Y.X.; writing--review and editing, Y.X., Z.Y. and X.H.; visualization, X.H.; supervision, Y.X.; funding acquisition, Y.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Surface of the Natural Science Foundation of Hunan Province of China, grant number \"2021 JJ30363\", and the Scientific Research Fund of the Hunan Provincial Education Department of China, grant number \"19A224\".  
Informed Consent Statement: Not applicable.  
**Data Availability Statement:** All data are presented in this article in the form of figures and tables.  
We gratefully acknowledge [PERSON], [PERSON] and anonymous referees for thoughtful review of this research as well as the assistance of [PERSON] with statistical analyses. We also thank the Yunyuan Scientific Research Base (Hunan Agricultural University, Changsha) for providing us with insects and broccoli leaves.  
The authors declare no conflict of interest.
# Security Scheme Based on Parameter Hiding Technic for Mobile Communication in a Secure Cyber World
## 3 Proposed Security Scheme
### Initial Attach with IMSI  
The first protocol is shown as Figure 1 and carried out after the Initial State after Radio Link Synchronization process is completed in the initial attach in the IMSI case. The first protocol is based on mutual authentication between UE and eNB MME and is designed to protect IMSI, which is leaked as plain texts in the ECM connection establishment process, and RNTI, which is leaked as plain texts in the EPS session establishment process.  
UE transmits a random number and the UE network capability generated for the attach request to MME. MME, which receives the attach request, generates random numbers and transmits them to UE, and UE and MME execute a series of calculation to transmit IMSI securely.  
UE and MME inputs transmitted and received random numbers and PLMN ID to the hash function, which has secret sharing according to MNC and generates 4 n bits F string. Generated F stings are divided into four sequences with each n bits. MME generates random number sequences that are used as challenge bits, and UE generates SSKUE' and calculates a lr sequence and exclusive.  
MME generates challenge bits C\(i\) using lri, adi, and c\(i\). Ci is composed of C\(i=ciFigure 1: Proposed protocol: Initial attach with International Mobile Subscriber Identity (IMSI).  
After the challenge response process, MME transmits \(\text{SSK}_{\text{UE}}\), which is drawn through s-box inverse function and transmits to eNB after hashing SSKUE. eNB transmits \(\text{SSK}_{\text{eNB}}\), which corresponds with \(\text{h}(\text{SSK}_{\text{UE}})\) to MME. MME verifies whether a correct secret value, S, is drawn by substituting \(\text{SSK}_{\text{MME}}\), \(\text{SSK}_{\text{UE}}\), and \(\text{SSK}_{\text{eNB}}\), which are corresponding with \(\text{SSK}_{\text{UE}}\) in \(Y=aX^{2}+bX+S\). In case verification of all SSK is successful, MME makes a hash calculation of unused \(\text{r}_{i}^{0}\) and subsequently transmits to eNB.  
UE and eNB execute the challenge response process using \(\text{h}(\text{r}_{i}^{0})\), and the random number, \(\text{RN}_{\text{UE}\_2}\), which is generated by UE, is transmitted to eNB.  
After the second challenge response process is completed, UE transmits encrypted IMSI to MME using unused ri1 as a key. MME obtains ISMI by decoding received encrypted messages through the same process with UE.  
After IMSI is securely transmitted, it executes down to the Access Stratum (AS) security setup process among the processes of ECM connection establishment, verification, NAS security setup, location update, and EPS session establishment. After AS security setup is completed, eNB transmits encrypted RNTI to MME using a secret key of the AS security setup in order to allocate RNTI to UE. eNB allocates received RNTI by encrypting received RNTI to \(\text{RN}_{\text{UE}\_2}\), which is saved in the ECM connection establishment process, and by transmitting to UE.