esa-sceva/satcom-chunk-collection huggingface dataset:
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 5 Conclusions  
To improve the performance of semiconductor manufacturing systems, this research proposed a PSO-SVM-based scheduling method with performance driving and a feedback mechanism. We designed a dynamic dispatching rule that integrated equipment maintenance, process constraints, and the correlated real-time status of the manufacturing system. Using the parameter optimization model, we could obtain the most suitable dispatching parameters to update the dispatching rule to adaptively acclimatize to the new production environment. The simulation results of this study indicated that the proposed PSO-SVM-based scheduling method outperformed several other conventional scheduling policies in average cycle time (CT), daily movement (MOV), average equipment utility (EU), on-time delivery rate (ODR) and process constraints satisfaction rate (PCSR). Compared to CDR, the proposed scheduling method improved MOV, PR, EU_O, EU_P, PCSR, and ODR by 7.85%, 5.11%, 8.76%, 8.14%, 6.60%, and 7.33%, respectively. Additionally, it may self-adaptively fulfill the manufacturing system's dynamic environment and enhance overall system performances to produce semiconductors.  
Although the proposed method performs better than other common heuristic scheduling methods, this research did not consider the adjustable parameters of the PSO-SVM, which could influence the performances of the whole manufacturing system. Owing to the research size and time limitation, this research did not consider other deep learning  
Figure 13: Comparisons among different performance driving.  
methods that have similar functions. In conclusion, the dynamically adjustable parameters of the PSO-SVM need to be considered to further improve the overall performances, and we will adopt other deep learning methods to complete the proposed methodology to make comparisons.  
All authors contributed to the study's conception and design. Material preparation, coding, data collection, and analysis were mostly performed by [PERSON] and partly completed by [PERSON], [PERSON], [PERSON] and [PERSON]. The first draft of the manuscript was written by [PERSON] and [PERSON] and all authors commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work has been supported in part by the National Natural Science Foundation of China under Grant 72171172 and 62088101, in part by Shanghai Municipal Science and Technology, China Major Project under grant 2021 SHZDZX0100, and in part by the Fundamental Research Funds for the Central Universities under grant 22120220613.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Platform Design
### ToU Ranges Using GRU Model Predictions and K-Mean Clustering  
The aim is to dynamically set the ToU peak and off-peak ranges. Based on past consumers' electricity consumption data available on the shared ledger blockchain, future electricity consumption predictions are then made, using the GRU model, subsequently, the ToU ranges for the next trading round are set by performing K-mean clustering on the blockchain as illustrated in Figure 2. The purpose behind it is to have a transparent, decentralized dynamic demand response.
## 7 Research Challenges and Recommendations for Future Works
### Encryption Factors  
One of the key factors thwarting the full realization of the LPWAN is the problem of inefficient encryption measures. The encryption in most existing LPWAN standards provides a weak level of data confidentiality and integrity. The use of symmetric encryption is not secured as it uses single key to execute encryption process. Although some LPWAN standards use asymmetric encryption like RSA and strong encryption methods such as AES, better and faster encryption methods for LPWAN should be core interest of future research works [20].  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Refs.** & **Security Threat(s) Addressed** & **Security Requirements** &
\begin{tabular}{c} **Brief Summary of Approach, Highlight Strength** \\ **and Limitation** \\ \end{tabular} \\ \hline
[22] & Replay attack & Integrity &
\begin{tabular}{c} Proposed an AES-128 based Secure Low Power \\ Communication (SeLPC) method to boost the security \\ level of LoRaWAN communication. \\ **Strength:** Efficient power consumption. \\ Used unified join request messages to prevent replay \\ attack. \\ \end{tabular} \\
[71] & Replay attack & Integrity &
\begin{tabular}{c} **Strength:** Fully support secure key exchange. \\ **Limitation:** The approach does not support the perfect \\ forward secrecy nor end-to-end security. \\
[72] & Replay and Decrypt attack & Integrity &
\begin{tabular}{c} Proposed the increment in the size of DevNonce and \\ AppNonce value with no repetition. \\ Network server store all DevNonces used in the \\ previous join procedure in order to prevent the attack. \\ IDS that is based on KLD and HD was used for \\ detecting jamming attacks in a LoRaWAN Network. \\ \end{tabular} \\
[19] & Jamming attack & Availability &
\begin{tabular}{c} **Strength:** Ability to detect and respond quickly to \\ anomalous behavior. \\ Ability to detect new forms of attacks which might \\ deviate from the normal behavior. \\ **Limitation:** Prome to false positives. \\ Used data counter to prevent the attacks. \\ \end{tabular} \\
[86] & DoS attack & Availability &
\begin{tabular}{c} The Appskey derivation mechanism need to be \\ changed and a special case for join procedure \\ delegation must be introduced. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.
## 3 Hybrid Algorithm--RSO-IGA
When selecting a placement space, ensure that the space can fully accommodate the parcel, and the difference between the base area of the parcel and the base area of the space should be as small as possible;
3. If multiple spaces have base areas similar to that of the current parcel, choose the space that, after accommodating the current parcel, can be divided into the largest possible subspace. A comprehensive evaluation metric for (2) and (3) is proposed as shown in the following equation:

\[Space_{i}(I)\geq box_{local}(I),Space_{i}(w)\geq box_{local}(w),Space_{i}(h) \geq box_{local}(h) \tag{21}\]

\[f(box_{local},Space_{i})=(Space_{i}(I)+box_{local}(I)-\gamma)\cdot(Space_{i}( w)-box_{local}(w)+\gamma) \tag{22}\]

Equation (21) indicates that the placement space must be able to accommodate the box. In Equation (22), \(\gamma\) is a correction parameter for the comprehensive evaluation metric, and its value is a positive number significantly smaller than the shortest edge of the smallest parcel. This parameter is introduced to avoid the situation where the comprehensive evaluation metric cannot be optimized when any edge of the placement space equals any edge of the box. As shown in Figure 5, two identical parcels, \(b_{i}\) and \(b_{j}\), are loaded into a delivery vehicle. Without the correction parameter, the comprehensive evaluation metrics \(f(b_{j},Space_{1})\) and \(f(b_{j},Space_{3})\) would both be 0. According to placement rule (3), the spatial configuration shown in Figure 5a is obtained. When the correction parameter is included, the comprehensive evaluation metrics are calculated as \(f(b_{j},Space_{1})=-\gamma^{2}\) and \(f(b_{j},Space_{3})=-\gamma\cdot(Space_{3}\ (I)-b_{j}(I))\). Clearly, the former is larger, resulting in the spatial configuration shown in Figure 5b. From Figure 5, it is evident that the corrected residual space set provides more valuable reference for subsequent packing. Figure 4: (**a**,**b**) represent two cutting methods: (**a**) represents horizontal cutting and (**b**) represents vertical cutting. The specific pseudocode steps of the remaining space optimization Algorithm 2 are as follows:
```
Input: Route \(R\), parcels list with dimensions, LIFO stack order Output: Packing feasibility (True/False), loading rate \(V\)
1: Initialize:
2: Residual spaces: [Space0 = (\(L\), \(W\), \(H\), corner = (0,0,0))]
3: Space_avi = True # Space availability flag, initially True
4: Plan_list = [] # List to store packing plans, initially empty
5: Sort parcels by base area (largest-first) and push to LIFO stack
6: Sort parcels: Sort by base area descending order, then push to LIFO stack
7: Update residual spaces and total space count:
8: Update Space_list (list of available spaces)
9: Total number of available spaces: num_S
10: Main Packing Loop:
11: while parcels exist in LIFO stack:
12: a. Pop the next parcel \(p\) from stack
14: b.
## 3 Results and Discussion
### Experiment 2: Optimum Forest Size  
Most of the experiments using RF classification are done with a large number of trees, which is time consuming [27; 48]. The default tree number of 200-1000 used in some investigations greatly increases the computation cost [6]. Hence, the optimum forest size for each dataset should be investigated and determined. For this purpose, after measuring the optimum sample size in experiment 1, the optimum forest size is also investigated through increasing trees from 10 up to 1000 trees to reduce the time of further calculations. In this experiment, the RF classification is also conducted with 15 times replacement in case 1 using only the spectral bands of all images. As shown in Figure 7, the overall accuracy and kappa coefficient were increased by less than 1% after 50 trees, up to 1000 trees, but instead, the computation time increased from 165 s to 2176 s (Table 6). Thus, the number of optimal trees in this experiment was set to 50.  
Table 6 shows the processing time of each RF classifier implementation. As can be seen, by increasing the number of trees, required time for each run increases linearly.  
Figure 6: Overall accuracy and kappa coefficient changes to obtain the optimum training sample size.  
Figure 7: Effect of the number of trees on (**a**) overall accuracy (OA) and (**b**) kappa coefficient.
# Engineering Applications of Adaptive Kalman Filtering Based on Singular Value Decomposition (SVD)
## 6 Computational Complexity  
Computational complexity is evaluated as time complexity as a function of input size, where m and \(n\) denote the input size and the complexity of the number of required operations (big \(O\)), the matrix inversion is \(O(n^{3})\), and an economic SVD rank reduction step requires \(O(m^{2}n)\) operations. With the matrix multiplication \(O(n^{3})\), to compute the Kalman gain \(k_{t}\) requires \(O(sn^{2}+n^{3})\). The Cholesky decomposition of the commonly used algorithm is \(O(n^{3})\). Computing the matrix covariance is \(O\Big{(}N.n^{2}\Big{)}\), and in the worst case the algorithm complexity is \(O(n^{3})\), and this is still within an acceptable range when compared with the conventional KF, where it has been established that the number of arithmetic operations to be executed at each sampling period is \(O(n^{3})\)[24].
# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 2 Materials and Methods
### Study Areas and Data  
Prague was selected as the area of interest for analysis of the occurrence of people on streets. The study area in Prague consists of three subsets: Prague Castle, Charles Bridge, and the Old Town Square, including its surroundings (Figure 2). Two reasons for these selections apply: the areas contain various surface types (to verify the ability to detect people in various urban conditions) and the frequent occurrence of people in these locations at the given time (Figure 3). One full WorldView-3 scene from the morning of 23 July 2019, covering 25 km\({}^{2}\), was obtained. The spatial resolution of the WorldView-3 panchromatic and multispectral images is 0.3 m and 1.6 m, respectively.  
Figure 3: Details of Location 7 (pixel resolution is 30 \(\times\) 30 cm).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Location** & **Number of People** \\ \hline
1—Old Town Square—surface 1 & 214 \\
2—Old Town Square—surface 2 & 15 \\
3—Old Town Square—surface 3 & 19 \\
4—Old Town Square—surface 4 & 9 \\
5—Charles Bridge & 479 \\
6—Prague Castle—surface 1 & 63 \\
7—Prague Castle—surface 2 & 116 \\
8—Prague Castle—surface 3 & 35 \\ Total & 950 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ground truth data in Prague.  
Figure 2: Study areas in Prague (pixel resolution is 30 \(\times\) 30 cm). The numbers represent locations mentioned in Table 1.  
In these study areas, ground truth data were obtained by visual vectorization. Ground truth data (Table 1) are needed for training and accuracy assessment. After vectorization, an exploratory data analysis was performed in which it was found that people in the satellite images were shown as 4 pixels that shared a neighborhood (Figure 3). The digital numbers (DNs) of these pixels were significantly higher than the DNs of surface pixels.  
The surface types were as follows:  
* Location 1--interlocking pavement.
* Location 2--small square light cobbles, forming large square areas, bordered by small square dark cobbles.
* Location 3--small square light cobbles, forming small square areas, bordered by small square dark cobbles.
* Location 4--small square light cobbles, forming large rectangular areas, bordered by small square dark cobbles.
* Location 5--interlocking pavement.
* Location 6--square light cobbles, forming large square areas, bordered by square dark cobbles.
* Location 7--interlocking pavement.
* Location 8--marble pavement.  
Vectored digital data for buildings and greenery were obtained from digital technical maps of IPR Prague (The Prague Institute of Planning and Development) as appropriate auxiliary data. These data enabled the creation of masks that improve the quality of people detection by hiding or blocking certain parts of the input imagery, allowing the neural network to focus on a specific area of interest. By excluding irrelevant areas using the mask, computation can be reduced, leading to a potential increase in detection speed.
# Effect of Blood on Synovial Joint Tissues: Potential Role of Ferroptosis
## 3 Interplay of Erythrocytes, Hemoglobin/Iron, and Synovium  
Intact erythrocytes can communicate with other cell types via chemical signaling as they are known to be reservoirs of chemokines. The median concentration of cytokines, chemokines, and growth factors in RBCs is 12-fold higher than in the plasma [45]. RBC responses to shear can modulate its secretome, such as the increase in endothelial nitric oxide synthase [46]. Erythrophagocytosis resulting in lysis of RBCs by synovial cells occurs in culture, during which synovial cells absorb hemoglobin from the culture medium within 4 h of blood introduction and convert the iron to ferritin [47; 48]. Ferric iron may either be reduced to the ferrous form and promote the formation of toxic-free radical species, or stimulate collagenase and prostaglandin release from synovial macrophages [49; 50]. Synovial cells, and in particular the Type A or macrophage-lining cells, have been reported to have a strong affinity for iron [51]. While blood itself may not persist in the joint space, the remnants of erythrocytes including hemoglobin and deposited iron (hemosiderosis) may have longer-lasting or permanent effects as heme interacts with immune and synovial cells to induce the production of pro-inflammatory cytokines (e.g., IL-1 and TNF\(\alpha\)) as well as fibroblast proliferation leading to tissue hyperplasia [49; 52]. Researchers have shown that following hemorrhosis, patients exhibited an increase in pro-inflammatory cytokines in their SF 24 h after injury, followed by a time-dependent decrease [53].
## 2 Materials and Methods
### Electrical Characterization and Electrical Fatigue Method (EFM)  
The experimental setup for measuring the I-V characteristic, resistance, and electrical fatigue method (EFM) is shown in Figure 2. For all electrical tests, the wire length was fixed at 50 mm. Current-voltage (I-V) measurements were conducted by incrementally increasing the current from 0.00 A to 0.40 A in 0.05 A steps, followed by 0.01 A increments beyond 0.40 A until electrical fusing occurred. The fusing current (I\({}_{\mathrm{fuse}}\)) was recorded, and dynamic resistance was calculated by applying [PERSON]'s law within the linear region of the I-V curve.  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline Current (A) & 0.00 & 0.10 & 0.20 & 0.25 & 0.30 & 0.35 & 0.37 & 0.39 & 0.41 \\ \hline J (A/ cm\({}^{2}\)) & 0.00 & 5.09 \(\times\) 10\({}^{3}\) & 1.02 \(\times\) 10\({}^{4}\) & 1.27 \(\times\) 10\({}^{4}\) & 1.53 \(\times\) 10\({}^{4}\) & 1.78 \(\times\) 10\({}^{4}\) & 1.88 \(\times\) 10\({}^{4}\) & 1.99 \(\times\) 10\({}^{4}\) & 2.09 \(\times\) 10\({}^{4}\) \\ \hline Temp. (\({}^{\circ}\mathrm{C}\)) & 25 & 43–83 & 182–222 & 288–328 & 425–465 & 594–634 & 675–715 & 770–810 & 840–880 \\ \hline \end{tabular}
\end{table}
Table 1: Electrothermal simulation under varying current levels and current densities.  
Figure 1: (**a**) Schematic of cold drawing and spring forming; (**b**) schematic diagram of electrically charged spring probe.  
The electrical fatigue method (EFM) was implemented by applying intermittent direct current to wire specimens clamped at a 50 mm length. Each current period consisted of a 60-s ON and 5-s OFF cycle. The applied current levels were 0.30, 0.35, 0.37, 0.39, 0.41, 0.45, and 0.47 A. For each condition, the number of cycles to failure was recorded. Fracture morphology and surface features were subsequently examined using scanning electron microscopy (SEM, SU-5000, Hitachi, Tokyo, Japan).
## 3 Iterative Scheduling Algorithm for Throughput Optimization
### Transmission Power Subtask Optimization  
When the location and bandwidth sub-optimization blocks are fixed, the sup-optimization problem of UAV transmission power consumption can be built by the same format. As transmission power multiplying \(P_{ug}\) term, it can influence the objective function:  
\[\begin{split} Objevtive&=\sum R_{i}\\ &=\sum B*log_{2}(1+\frac{P_{ug}(u_{i},e_{j})}{\sigma^{2}})\\ &=\sum B*log_{2}(1+\frac{P_{u}G(d_{j})}{(k_{0}d_{ij})^{n}(p_{LoS} \phi_{LoS}+p_{NLoS}\phi_{NLoS})}\cdot\frac{1}{\sigma^{2}}).\end{split} \tag{17}\]  
At every time slot, the objective function \(R_{i}\) corresponding to ground user \(i\) can be calculated based on transmission power, effective communication bandwidth distributed to user \(i\), and current UAV location. In the power optimization block, the initial bandwidth allocation and UAV moving strategy and fixed as follows, which are simply compliant with velocity and bandwidth constraints:  
\[\begin{split} B_{i}&=B/K.\\ u_{n}&=u_{n-1}+[v_{0}\cdot cosa_{0},v_{0}\cdot sina_{0}]. \end{split} \tag{18}\]  
When other environmental conditions are static, communication efficiency can grow with transmission power. Thus, to optimize UAV transmission power \(P_{u,n}\) at time slot \(n\), necessary constraints need to be specified. Relative constraints focus on the upper range of transmission power and total energy consumption, and the sub-optimization problem is shown as follows:  
\[\begin{split}\max_{P_{u,n}}&\sum_{n}^{N}\sum_{i}^{K}R _{i,n}\\ s.t.&\left\{\begin{array}{l}0\leq P_{u,n}\leq P_{ max}\\ \sum_{n}^{N}P_{u,n}\Delta T+E_{n}^{F}\leq E_{max}\end{array}\right.\end{split} \tag{19}\]  
Single decision variable \(P_{u,n}\) denotes the transmission power of UAV at time slot \(n\), while the upper bound is set as \(P_{max}\). Energy constraints are consistent with that in the location optimization block discussed above, limiting transmission power from selecting the maximum at all times.
# Development and Performance Assessment of a Low-Cost UAV Laser Scanner System (LasUAV)
## 5 Conclusions  
This paper describes the development of a UAV laser scanner system, including the process for generating 3D point clouds based on the precise UAV position and orientation. The integration of a GNSS RTK receiver with a dual-antenna GNSS-aided INS is an efficient solution to provide sufficient accuracy for forest monitoring applications by means of laser scanning.  
The performance assessment of the system was conducted in a point cloud obtained from scans over targets with a controllable and measurable geometry.  
The experiments conducted and the analyses carried out allow us to conclude that LiDAR point cloud uncertainty is most influenced by the GNSS RTK sensor, and for this reason, the biggest economic investment should be dedicated to this sensor.  
In addition, specific analyses accomplished in the combination of passages made over the target allowed us to conclude that a single passage over the area of interest is recommended to reach the lowest values of uncertainty and maximize the surface scanned over wide forest areas.  
Future improvements of LasUAV will include inserting a filter to screen interactions between the dual antenna of GNSS-aided INS and the UAV rotors, and using novel batteries to increase flight endurance and avoid frequent take-offs and landings.  
Future studies will address assessing the performance of the system in forest surveying. Understanding the added value of a UAV-borne laser scanner system, such as LasUAV, with respect to the airborne laser scanner system, is important for making decisions in forest monitoring, management, and planning.  
Conceptualization, C.T. and B.G.; Methodology, C.T. and B.G.; Software, C.T., F.C. and B.G.; Validation, C.T. and B.G.; Formal Analysis, F.C. and B.G.; Investigation, C.T., A.B., A.Z. and B.G.; Resources, A.B., U.C., F.M. and A.Z.; Data Curation, C.T., A.B. and B.G.; Writing-Original Draft Preparation, C.T.; Writing-Review & Editing, C.T., U.C. and B.G.; Visualization, C.T., F.C. and B.G.; Supervision, C.T. and B.G.; Project Administration, C.T., U.C., F.M. and A.Z.; Funding Acquisition, C.T., U.C. and A.Z.  
This research was funded in part by the CARITRO Foundation under project UAV4 PRECIFOR (2014.0408, 2143/14) and in part by the Tuscany Region under project SPIRIT (plan FOR FESR 2014-2020 for research and development).  
The authors declare no conflict of interest.
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Kinds**} & \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Control**} & \multicolumn{2}{c}{**Content (ug/kg)**} \\ \cline{3-5}  & & & **0.05\% RE** & **0.10\% RE** & **0.15\% RE** \\ \hline Ketone & Acetoin & 0.026 \(\pm\) 0.004 & 0.023 \(\pm\) 0.001 & 0.020 \(\pm\) 0.004 & 0.024 \(\pm\) 0.010 \\ \hline \multirow{4}{*}{Acids} & Butanoic acid & 0.008 \(\pm\) 0.001 ab & 0.005 \(\pm\) 0.001 a & 0.010 \(\pm\) 0.001 b & 0.021 \(\pm\) 0.004 c \\  & Hexanoic acid & 0.025 \(\pm\) 0.001 a & 0.025 \(\pm\) 0.004 a & 0.035 \(\pm\) 0.003 b & 0.053 \(\pm\) 0.003 c \\  & n-Decanoic acid & N & N & 0.009 \(\pm\) 0.001 & 0.008 \(\pm\) 0.005 \\  & Octanoic acid & 0.011 \(\pm\) 0.000 a & 0.014 \(\pm\) 0.001 a & 0.023 \(\pm\) 0.002 b & 0.022 \(\pm\) 0.003 b \\ \hline \multirow{4}{*}{Alkanes} & Decamethyl cyclopentasiloxane & 0.012 \(\pm\) 0.001 ab & 0.006 \(\pm\) 0.002 a & 0.018 \(\pm\) 0.004 b & 0.027 \(\pm\) 0.006 c \\  & Dodecamethyl cyclohexasiloxane & 0.026 \(\pm\) 0.002 a & 0.026 \(\pm\) 0.005 a & 0.046 \(\pm\) 0.003 b & 0.052 \(\pm\) 0.008 b \\ \cline{1-1}  & Hexadecamethyl cyclocatosiloxane & 0.003 \(\pm\) 0.002 & 0.004 \(\pm\) 0.001 & 0.006 \(\pm\) 0.002 & 0.004 \(\pm\) 0.001 \\ \cline{1-1}  & Octadecamethyl cyclononnaissance & 0.007 \(\pm\) 0.001 & N & N & N \\ \cline{1-1}  & Tetradecamethyl Cycloheptasiloxane & 0.010 \(\pm\) 0.002 a & 0.010 \(\pm\) 0.001 a & 0.016 \(\pm\) 0.004 b & 0.015 \(\pm\) 0.001 b \\ \hline \hline \end{tabular} Values are given as the mean \(\pm\) SD (n = 3). “N” means not detected. Values in a row with different superscripted letters differ significantly (\(p<\) 0.05) according to one-way ANOVA analysis with a Tukey test.  
\end{table} 
Table 1: Concentration of volatile flavour compounds in yoghurt.