esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology & Case Study
### The Low-Cost & Portable Monitoring System
#### 2.1.1 Wireless Data Transmit Method  
Figure 2 shows a sensor unit of the wireless device installed into a landslide site. One unit consists of a MEMS tilt sensor, and is compatible with different sensors. A sensor unit can work automatically with a microcomputer powered by solar cells or independent batteries. The 434 MHz ISM band radio modems have 127 pre-programmed channels, an alternative 1/10 mW power source, and they fit the European Radio and Telecommunications Equipment (R&TTE) Directive and the European Conformity (CE) Marking. They are integrated in the proposed sensor unit, therefore the sensor unit is capable of creating a series of wireless data transmit systems, from simple control systems to wide network systems. The sensor units collect real-time data every 10 min in rainy season and every 1 h in the dry season. The data can be transferred to a gateway unit placed next to the monitoring slope, by adopting energy-saving radio modules. The gateway unit receives data from all the sensor units installed, and transfers data to a server through a mobile network. The distance for data communication is more than 300 m in field circumstances. Therefore, monitoring data can be acquired anywhere and anytime via the internet. The data is arranged in time, and any abnormal variation of the parameters in the slope can be observed as a precaution of possible failure when the thresholds are identified.
## 1 Introduction
Additionally, DETR can handle objects of different sizes and quantities and can directly output object feature vectors, which can be used for subsequent tasks such as target tracking. To address its limitations in feature extraction, we combine it with Swin Transformer [11] and BiFormer Attention [12]. Based on the above considerations, this paper proposes an RT-DETR-Tomato target detection algorithm to improve the efficiency and speed of RE-DETR in detecting tomatoes. In tomato detection, the objects often lack feature expression due to issues such as occlusion, small target size, and few effective pixels [13; 14; 15]. Consequently, some tomatoes may go undetected in practical scenarios. Based on the aforementioned analysis, we innovatively propose the RT-DETR-Tomato model for tomato target detection to achieve high efficiency and reliability in natural environment tomato target detection. The RT-DETR-Tomato detection model is designed for tomato detection under complex environmental conditions, with the main innovations as follows to improve detection performance:  
1. By replacing the original ResNet50 backbone of the RE-DETR model with the Swin Transformer, the model's detection accuracy was improved. 2. By integrating the BiFormer Vision Transformer with dual-level routing attention, the small object feature extraction capability was enhanced, leading to improved model performance and high computational efficiency. The remainder of this paper is structured as follows: Section 2 focuses on related work in tomato target detection. Section 3 describes the model framework and implementation details. Section 4 verifies the effectiveness of the algorithm through experiments. Section 5 provides a summary.
## 5 Results and Discussion
### Validation Result
#### 5.3.2 Analysis of the Accuracy  
The angle measurement of the supporting block plane and the bottom of the track slab measured by two detection methods are compared. The measured value of the contact measurement is 1.321\({}^{\circ}\), and the value measured by the method in this paper is 1.342\({}^{\circ}\). The difference between the two measurements is 0.021\({}^{\circ}\). The measurement results prove that the detection method in this paper is accurate. As shown in Table 6.
## 2 Literature Review
### Data and Parameters Applied in Suspended Sediment Load (SSL) Prediction  
The information applied here to forecast SSL in the Atrek basin contains monthly SSL, monthly discharge, and precipitation during 1997-2017. The data were obtained from the Iran water resource management organization. Seven groups of input data were applied for improved ANFIS and MFNN models. A lag time of 3 to 9 months was considered for SSL prediction. The best inputs were obtained by principal component analysis (PCA). In the case of hydrological predictions, accurate knowledge of effective predictors is important for decision makers [28]. PCA is an identification method that applies orthogonal transformation to turn a collection of measurements of conceivably associated parameters into a collection of values of linearly unassociated parameters named principal components. However, there are min (m\({}^{-1}\), z) components when m observations and z variables are defined for data. When a component has the largest variance, the considered component is the most effective component. PCA is widely applied to determine the effective inputs for estimation of hydrological variables. Therefore, the proposed structures for the model inputs-outputs pattern were obtained from PCA, as shown in Table 1. The proposed models have been developed using Matlab 9.5 software version R2018b (MathWorks, USA) with a system 17 processor, SSD, and 64 GB RAM. On the other hand, the PCA computations have been carried out using SPSS 2018 software version 25.0.0.0 (IBM Corporation, USA). All the information and the data used in this study have been collected from water resource management authorities in Iran.
# Quality Improvement of Green Saba Banana Flour Steamed Cake
## 3 Results and Discussion
### Batter Analysis  
The viscosity and specific gravity of the cake batters were measured. Figure 1 shows that the batter prepared using 100% CSBF reported the lowest viscosity and the highest specific gravity (\(p<0.05\)) among all tested samples. Gluten in the standard batter (100% WF) provided the desirable, unique rheological properties of the batter by conferring water absorption capacity, cohesivity, viscosity and elasticity [9]. Higher viscosity in the WF batter was attributed to the gluten development [41] after hydration by water and mixing. In the absence of gluten, the control exhibited high fluidity; little air could be incorporated in the batter during mixing, which led to a high batter density. The addition of SPI and Ovalette (OOP10, OOP15, O35P0 and O7P0) significantly increased the batter viscosity and improved the air retention of the batter (\(p<0.05\)). SPI was reported to have higher water-adsorption capacity as compared to wheat protein [42] and resulted in an increment of the gluten-free batter viscosity as reported earlier [43]. Compared to the batter formulations containing single additive (OOP10 and OOP15 vs. O35P0 and O7P0), the combination of two additives would further reduce the specific gravity of the batters (Figure 1). Since the introduction of SPI and Ovalette to GSBF was based on an addition basis, the use of 10% and 15% of SPI elevated the total solid content of the formulations and thus their viscosities. It is noteworthy that the presence of Ovalette in the formulation was essential to lower the batter density to a more desirable value, closing to that of the standard (100% WF). Ovalette appeared to enhance the aeration and help stabilize the foams in the batter. Ovalette is a commercial emulsifier consisting of a mixture of \(\alpha\)-tending emulsifiers (mono- and diglycerides and polyglycerol esters of fatty acids) that is commonly used in cake production. Blends of monoglycerides and polyglycerol esters of fatty acids bring about synergistic effect in enhancing the formation of firm and stable gels via attractive integrations and forming \"bridges\" between air bubbles in foams that lead to more solid-like foams [16; 44] to effectively retain the entrapped air. Blends of polyglycerol fatty acid esters and monoglycerides are known to improve sponge cake aeration and stability with less mixing time and improved foam and emulsion stability [16; 45].  
Figure 1: Viscosity (cP) and specific gravity (mL/g) for different formulations of cake batter. Histogram with different alphabet (a–h) indicates significant difference (\(p<0.05\)).
## 3 Methodology
### Preprocessing Raw Data
#### 3.1.2 Unwanted Frequency Removal  
Frequencies below 0.5 Hz and above 49 Hz were removed using a zero-phase finite impulse response band-pass filter (FIR-BPF). This step was designed to eliminate baseline 50 Hz noise and low-frequency components. The filter employed a Hamming window  
Figure 1: Diagram of the 10–20 international electrode placement system, showing standardized electrode locations on the scalp for EEG recording to ensure accurate and reproducible EEG data collection.  
design with a heuristic order, and the transition bandwidth was automatically estimated using EEGLAB [29].
# User-Information-Aware D2D Multicast File Distribution Mechanism
## 4 User's Demands Awareness
### Video Request Probability
#### 4.1.2 Video Popularity  
Tracking the video viewing rankings of YouTube websites in different regions it was found that the popularity of videos in specific regions follows the Zipf distribution, although the popularity in different regions is different [29]. Let \(F\) denotes the set of videos requested by the users in the cell over the past period of time \(T\), \(|F|\) is the number of videos, so the popularity of video \(f_{l}\) ranked \(l\) can be expressed as:  
\[P_{l}^{pop}=\frac{l^{-\gamma}}{\sum_{c=1}^{|F|}c^{-\gamma}},1\leq l\leq|F| \tag{6}\]  
where, \(\gamma\) is the Zipf distribution index in the region, \(P_{l}^{pop}\) is the popularity of video \(f_{l}\).  
The probability that a user requests a certain video is mainly determined by the user's interest in the video and the popularity of the video. Let's assume that the video \(f_{l}\) requested by the service requester is classified the category \(k\). The \(P_{pi,l}^{interest}\) denotes the degree of interest of potential requester \(u_{pi}\) in video \(f_{l}\), and then the degree of interest \(P_{pi,l}^{interest}\) can be calculated according to Equation (5). The probability that potential requester will request video \(f_{l}\) later can be expressed as:  
\[P_{pi,l}^{request}=\lambda P_{l}^{pop}+(1-\lambda)P_{pi,l}^{interest}=\lambda \frac{l^{-\gamma}}{\sum_{c=1}^{|F|}c^{-\gamma}}+(1-\lambda)\Bigg{[}\delta \frac{x_{pi,k}}{X_{pi}}+(1-\delta)\frac{\sum_{j=1}^{x_{pi,k}}t_{pi,k}^{j}}{t_ {pi}}\Bigg{]} \tag{7}\]  
where \(\lambda\) is the weight coefficient, \(k\) is the category number of video \(f_{l}\), and \(P_{pi,l}^{interest}\) \(\in\) [0,1], where the above equation must satisfy the constraint conditions \(t_{pi,k}^{j}>t_{th}\) and \(1\leq l\leq|F|\).
## 2 Materials and Methods
### Gas Chromatography-Mass Spectrometry (GC-MS)  
The volatile compounds were further evaluated by adopting GC-MS (7890B GC System, 5977A MSD). Each sample (1 \(\upmu\)L each) was employed for analysis on a DB-FFAP capillary column (60 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m, Agilent Technologies, Santa Clara, CA, USA). Helium (99.999%) was used as a carrier gas at a constant flow rate of 2.0 mL/min, and the inlet temperature was 250 \({}^{\circ}\)C. The oven temperature was maintained at 40 \({}^{\circ}\)C initially and was held for 3 min, ramped to 150 \({}^{\circ}\)C at a rate of 2 \({}^{\circ}\)C/min and was held for 2 min, and then finally was raised to 230 \({}^{\circ}\)C at a rate of 7 \({}^{\circ}\)C/min and held was for 8 min. The temperature of the transfer line was 250 \({}^{\circ}\)C, and that of the MS ion source was set at 230 \({}^{\circ}\)C. The ionization energy of the electron impact mass spectra was 70 eV, and the acquisitions were over an \(m/z\) scan range of 35-450 amu. The injection was performed in splitless mode.  
Three parallel experiments were conducted for each sample, and the volatile compounds were identified by comparing their mass spectra in the NIST (2020) baijiu flavor compounds database developed by our group and their RI from the DB-FFAP column with those of the authentic standards. The retention indices of the compounds were calculated using alkanes (C8-C40). The n-alkanes of C8-C40 were configured in the n-hexane solution, which was selected to take into account the protection of the column. The solution concentration was 20 \(\upmu\)g/L. The same heating procedure as the sample was selected. The injection mode was splitless, and the injection volume was 1 \(\upmu\)L. After the RI were calculated according to the formula, they were compared with the values in the literature so as to realize the further characterization of the compounds.
# Asynchronous Quantum-Resistant Blockchain for Secure Intelligence Sharing
## 5 Experiments Result
### Storage Space Cost  
AQRB leverages SPHINCS\({}^{+}\) as the algorithm to construct the blockchain and selects SPHINCS\({+}\)-128f as the parameter with the same robustness as ECDSA (P-256). According to the information block and verification block content in Figure 10, the data size of Cyber information is 136,704 bits, \(SPBH\) and \(PVBH\) are 256 bits, \(height\) and \(timestamp\) are both 32 bits, \(proof\) is 137,312 bits, and the total number of \(Sharing\) and verification blocks depends on the number of information maintenance companies \(M\). Therefore, the storage space is estimated based on the three conditions: supply and demand balance, oversupply, and undersupply. With the block establishment condition being twice that of the \(S\) reputation value, it requires 2-4, 5-7, and 1 company maintenance for each condition discussed.  
In the context of supply and demand balance, when two to four companies take charge of maintaining data, \(Sharing\) includes the \(Sig_{SSK_{i}}(req_{i})\), \(rep_{i}\), and \(PK_{i}\). If the data are maintained by two enterprises, \(Sharing\) size is 273,984 bits and the two verification blocks are 274,624 bits. In the case that three enterprises are involved in the provision, \(Sharing\) size is 410,976 bits and the verification block is 411,936 bits. When four companies are involved in the maintenance, \(Sharing\) size is 547,968 bits and the verification block is 549,248 bits. Therefore, while two to four companies join the data play, it takes 685,632 to 1,234,240 bits for the information block. In comparison, in cases of oversupply and undersupply, the data sizes are 1,508,552 to 2,057,152 and 411,328, respectively.  
Figure 11 illustrates the storage costs of sharing block and verification block under different numbers of companies participating in the data maintenance. As the number of companies increases, the storage space required for both sharing block and verification block increases, impacting the total storage space consumption. Figure 12 illustrates the advantage of AQRB in terms of storage cost compared to popular blockchain like Bitcoin and Ethereum. As of now, the record shows 46 million users, with an average block size of 1.5 MB for Bitcoin. Ethereum has 378,000 users with an average block size of 72,000 bits. In contrast, AQRB requires the storage of only those nodes that are pertinent to its operation, thereby achieving significant data storage efficiency.  
Figure 11: Storage cost for a different number of companies.  
Figure 10: Block storage space cost.
## 3 Results and Discussion  
### Effect of Blending on the Basic Chemical Characteristics of WSs  
The results of one-way ANOVA for the basic chemical composition of WSs from the natural blending and the blending operation in each ageing technology are shown in Table 1. None of the studied parameters were significantly influenced by the blending technology; that is, the natural blending imparted chemical characteristics similar to those resulting from the blending operation, regardless of the ageing technology. Therefore, the novel blending technology did not affect the WS matrix, which is a positive achievement since the aroma, taste and mouthfeel perception of the alcoholic beverages depend on their ethanol content (expressed by the alcoholic strength), acidity and total dry extract [3; 24].  
Comparing the WSs from the alternative and traditional technologies, slightly higher alcoholic strength was found in the former (76.31-76.83 vs. 75.82-76.40% \(v/v\)), reflecting lower WS evaporation from tanks than barrels, thus confirming our previous results [19; 20], which was not influenced by the kind of blending performed.  
In addition, it is worth mentioning that the total dry extract was slightly higher in the WSs from the alternative technology (2.42-2.50 vs. 1.47-1.53 g/L), regardless of the kind of blending, reflecting a better equilibrium extraction/oxidation of wood compounds and therefore contributing to enhance the WS quality [45]. Besides phenolic and furanic compounds (addressed in the next subsection), higher fixed acidity (0.28-0.34 vs. 0.21-0.24 g acetic acid/L AE) observed in the WSs aged by the alternative technology, as well as the presumably higher levels of sugars released from the wood (derived from hemicelluloses and cellulose [46]), may have contributed to this outcome.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & \multirow{2}{*}{\(p\)} & \multicolumn{6}{c}{**Alternative Technology**} & \multicolumn{6}{c}{**Traditional Technology**} \\ \cline{2-13}  & & \multicolumn{3}{c}{**Natural Blending**} & \multicolumn{3}{c}{**Blending Operation**} & \multirow{2}{*}{\(p\)} & \multicolumn{3
## 7 A Future Supply Chain Scenario  
In the future, car manufacturers will not make cars, they will only manage processes. In the hypothetical DellAuto's supply chain, chassis parts are made in Shanghai, then assembled by ChassisMax in Mexico City, and then shipped to EnginesR'US in Stuttgart. Now suppose there is a new opportunity: Tasmanian-based AssemNow can do the next twenty chassis cheaper and faster. What is required to revise DellAuto's supply chain on the fly?  
First, there must be a check that the ChassisMax contractual penalty for canceling the current order is less than the savings of using AssemNow. If this is so, then the following notifications need to be made to:  
* ChassisMax and AssemNow canceling and placing order.
* DellAuto's Finance Department saying that the deposit with Bank of Mexico is no longer required.
* DellAuto's Treasurer saying that the scheduled payment to ChassisMax should be canceled, and replaced by a penalty payment.
* DellAuto's Order Department saying that a PO should be issued to AssemNow.  
EnginesRUs should not be notified because message exchange with the shipping service shows that the chassis will arrive in Stuttgart on the original date. A coordination system would note only effects of the changes, as they propagate through the dependencies, and not make useless notifications just because something changed.  
The effects of such functionality could be world changing, revolutionizing not only how companies are managed, but how any large enterprise is done, especially ad hoc ones, such as global relief efforts for catastrophes [25].  
As noted previously, there are supply chain scenarios that cannot be handled by workflow and new technology is needed. It would be difficult to handle even the example of DellAuto's supply chain with the World Wide Workflow without some coordination mechanism.  
There are various ways to handle the management of dependencies and changes that propagate through them. The logical spreadsheet mechanism of [26] does so by noting whether or not \"false\" can be deduced from any of the logical expressions and then denoting which values of arguments to predicates lead to these inconsistencies. This requires paraconsistency: being able to reason sensibly even when there are logical inconsistencies, which is useful since logical inconsistencies may arise from a number of sources in distributed systems [27].  
Another general technology proposed is that of [28] in which dependencies among web services are represented. Like the logical spreadsheet, this is a generalized notion of dependencies. Neither propose a specific method of notifications, although both provide the basic mechanism for identifying who is the owner of the variable value assignments that are in conflict.  
We summarize in the next section a solution that does elaborate coordination notifications, which has long existed in the field of concurrent engineering [29], and is a model consisting of special dependencies for general design covering many situations. This model specifically defines a set of notifications that should be sent in specific situations.
## 8 Potential Challenges in the Development of PICs
One primary issue is the mismatch between the mode field diameters of optical fibers and the much smaller WGs in PICs, which can lead to high coupling losses [199]. Material compatibility also poses challenges, as the refractive index differences between Si-based PICs and glass optical fibers can cause reflection and scattering losses at the interface [200]. Additionally, the physical alignment precision required to couple fibers to PICs efficiently is stringent, necessitating advanced packaging and alignment techniques to minimize insertion loss. Ensuring thermal stability and mechanical robustness in the coupling interface is crucial to maintaining performance over the operational lifespan [201]. Continuous investment in research and development is crucial, as is the adoption of advanced simulation and modeling tools that can predict the behavior of photonic devices with high accuracy. The integration of AI and ML in the design and manufacturing processes can further enhance efficiency and performance, enabling the rapid development of next-generation photonic devices [202; 203; 204]. Furthermore, establishing industry standards and fostering collaboration across the photonics ecosystem will drive the adoption of best practices and accelerate technological advancements. By addressing these challenges through innovation and collaboration, the development of integrated photonic devices can progress, unlocking new opportunities and applications in telecommunications, computing, sensing, and beyond.
## 4 Experimental Results and Analysis
### Base Classifiers and MFELCM
#### 4.2.4 XGBoost Experimental Results
It seems that the XGBoost3 performs better than the XGBoost2, but this is because the samples (\(TSF^{i=}\)) from the same ship (\(TSF^{i=}\)) appear in the training set and the testing/validation set at the same time. This data leakage leads to the performance of XGBoost3 being overestimated.
# Deflection Reduction Shaping Commands with Asymmetric First-Order Actuators
## 1 Introduction
In these approaches, even if actuator nonlinearities were directly considered for an input shaper redesign, simultaneous consideration of the transient and residual deflection reductions of the flexible modes were not undertaken. For the transient and residual deflection reduction with limited-state commands, a unit magnitude zero vibration shaper with asymmetric first-order actuators (UMZV\({}_{\text{DRF}}\)) is analytically developed with the phasor vector diagram for an input shaper redesign and an approximated deflection expression to limit transient deflection with respect to multiple step inputs. In the next section, an optimized unit magnitude shaper with asymmetric first-order actuators, so called UMZV\({}_{\text{DRFO}}\), is formulated with the parameter optimization formulation and the modified expression of the final impulse amplitude of the input shapers for either an alternative shaping command design or a justification of the proposed approximated approach. The proposed input shaping controllers are numerically analyzed with respect to system parameters, deflection reduction ratios, and actuator time constants using a benchmark model. Lastly, the deflection reduction shaping commands are experimentally validated using a mini-bridge crane.