esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The conventional feedrate-constraint method is sensitive to the angle of the corner and the length of the line segment, and the feedrate-constraint value obtained is unreasonable, which will result in damages to the machining quality. In this paper, the FNA method was proposed, in which the line segment path sampling, the machining trajectory prediction, and the acceleration filtering were used, and they can significantly improve the reasonableness and stability of the feedrate-constraint value.  
In the simulation of the 2D machining path, the feedrate-constraint value obtained by the FNA method was more stable and reasonable compared with the angle-constraint method, and was almost equal to the theoretical value. In the simulation of the 3D machining path of the free-form surface, compared with the angle-constraint method, the relative range of the feedrate obtained by the FNA method decreased from 90.8% to 4.3% and the standard deviation decreased from 602 to 17, which means that the stability of the feedrate was significantly improved. Compared with the curvature-constraint method and acceleration-constraint method, the proposed method also worked much better. The machining experiments showed that the proposed FNA method ran stably and can both reduce the machining time and improve the machining quality, thus helping to promote the application of the continuous small line segments in the high-speed and high-precision CNC machining.  
In view of the limited time, the FNA method only worked for line segments. It will be improved to be applicable for the machining path consisting of the line segment, the arc, and the parametric curve. Additionally, the current method was only implemented in the three-axis CNC machining and has not yet been applied to the five-axis, which will be realized in the future.  
Conceptualization, P.G., Y.W., and R.W.; methodology, P.G. and R.W.; software, P.G.; validation, P.G., Z.S., H.Z., and P.Z.; writing--original draft preparation, P.G. and R.W.; writing--review and editing, P.G., Y.W., and F.L.; visualization, H.L.; project administration, Y.W.; funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was sponsored by National Natural Science Foundation of China (No.51275462) and Science and Technology Innovation 2025 Major Project of Ningbo (No.2018B10069).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Subsection Parameter Tuning in Band Selection  
AVIRIS Sensor data is observed to be spread over three partitions. The first partition is the visible region (VIS) in the wavelength region of 400-700 nm and is equipped with  
Figure 4: Different crops of AVIRIS Sensor data (**a**) Indian Pines (**b**) Salinas.  
Figure 5: Different class regions (**a**) Indian Pines (**b**) Salinas.  
Figure 3: Different classes of AVIRIS Sensor data (**a**) Indian Pines (**b**) Salinas.  
32 bands (d\({}_{1}\)). The second partition is the near-infrared region (NIR) in the wavelength region of 700-1000 nm and is equipped with 40 bands (d\({}_{2}\)). The third partition is the short wave infrared region (SWIR) in the wavelength region of 1000-2500 nm and is equipped with 148 bands (d\({}_{3}\)).  
The threshold value to select bands from the VIS region is set as 4.6 (81), and the number of bands selected is 15 (m\({}_{1}\)) for Indian Pines data, and for Salinas data, these values are set as 4 and 12, respectively. The threshold value to select bands from the NIR region is set as 0.2 (82), and the number of bands selected is 15 (m\({}_{2}\)) for Indian Pines data, and for Salinas data, these values are set as 0.11 and 16, respectively. The threshold value to select bands from the SWIR region is set as 0.62 (83), and the number of bands selected is 15 (m\({}_{3}\)) for Indian Pines data, and for Salinas data, these values are set as 0.99 and 15, respectively.  
The red band and green band information is essential to calculate NDVI and MNDWI. These are shown in Figure 6 for both datasets [48; 49].  
The 81, 82, and 83 values were set as shown in Figure 7. Using the number of available bands (224) from the AVIRIS Sensor, we considered 1/5 th of the number of the bands. Accordingly, we set m\({}_{1}\), m\({}_{2}\), and m\({}_{3}\) values.  
Figure 6: Different bands of Indian Pines data. (**a**) Red band with band number 29, (**b**) Green band with band number 15. For Salinas data (**c**) Red band with band number 29, (**d**) Green band with band number 15.  
From Indian Pines data, a total of 45 (m) bands were chosen with following band numbers: 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 37 38 39 41 42 43 44 45 47 48 49 50 51 52 53 151 152 153 154 155 156 157 158 159 160 161 162 163 219 220.  
From Salinas data, a total of 42 (m) bands were chosen with following band numbers: 21 22 23 24 25 26 27 28 29 30 31 32 40 41 42 44 45 46 47 48 50 51 52 53 54 55 56 108 109 110 154 155 156 157 158 159 160 161 162 163 164 165.  
The graphical representation, which shows the entropy of visible region bands, NDVI of NIR region bands, and MNDWI of SWIR region bands, is in Figure 7 for both datasets.
## 2 Materials and Methods
### Analytical Methods  
The pH of the samples was determined using a portable pH meter (HI 8424, HANNA Instruments, Padova, Italy). To evaluate dissolved organic carbon (DOC), samples were first filtered (Whatman Puradisc 25, pore size 0.7 \(\upmu\)m) and then subjected to catalytic oxidation using a TOC-L analyzer (Shimadzu, Kyoto, Japan). True color was determined on filtered samples, following the standard method for clear liquids [33], which involves measuring absorbance at 400 nm using a UV/visible spectrophotometer, with results expressed as platinum-cobalt units. Alongside DOC, the presence of natural organic matter (NOM)--a heterogeneous group of organic compounds--was assessed by measuring UV absorbance at 254 nm [34] using a UV/vis spectrophotometer (VWR(r) UV-6300 PC, Avantor, Radnor, PA, USA). Dissolved nitrogen was quantified using a SHIMADZU TOC  
Figure 2: Experimental setup used to perform coagulation–flocculation–sedimentation assays in continuous mode.  
L/TNM-L analyzer, while nitrite, considered toxic to fish and associated with 'brown blood disease' [35], was quantified by the standard colorimetric method (4500-NO2\({}^{-}\) B) [33]. Total phosphorus was also determined based on established procedures (4500.B and E) [33]. For biochemical oxygen demand (BODs), samples were analyzed using a BOD Sensor Set from Velp Scientific (Usmate, Italy), where they were incubated in the dark for 5 days at 20 \({}^{\circ}\)C inside a thermostatically controlled cabinet. Metals and nutrients in both untreated and treated samples were quantified by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) using a Thermo X Series system (Thermo Fisher Scientific, Waltham, MA, USA). Before analysis, samples were passed through a 0.45 \(\upmu\)m syringe filter (Chromafil A-45/25, Macherey-Nagel, Duren, Germany) to eliminate particulate matter generated during coagulation, thus avoiding instrument clogging. Sludge quantification was performed by filtering a defined volume of treated water through a 1.5 \(\upmu\)m glass microfiber filter (VWR), followed by drying at 60 \({}^{\circ}\)C for 24 h. The dry weight was recorded as the amount of sludge produced at the optimal dose of each coagulant. In a second step, sludge samples were acid digested (HCl/water solution 1:20 \(v/v\)), concentrated to under half their volume, then brought back to 50 mL and filtered. Finally, both water and sludge samples were analyzed for the presence of Al, Ca, Fe, and Mg.
# Spatially Adaptive Regularization in Image Segmentation
## 2 Defining Local Regularization by Exploiting Spatial Information
### Regularization Based on Thresholding  
This approach implicitly exploits the definition of \(\Sigma\) in (4) in order to set \(\lambda_{i,j}\). The idea is to use large values of \(\lambda_{i,j}\) when \(u_{i,j}\) is close to 1 and small values when \(u_{i,j}\) is close to 0. Therefore, the parameters \(\lambda_{i,j}\) are not defined in terms of the given image \(\bar{u}\) only. If the function \(u\) identifying the segmentation were known a priori, we could define \(\lambda_{i,j}=f(u_{i,j})\) as follows:  
\[f(u_{i,j})\equiv f^{\text{THR}}(u_{i,j})=10^{\eta_{i,j}}\in[\lambda_{min}, \lambda_{max}], \tag{12}\]  
where  
\[\eta_{i,j}=emax-(1-u_{i,j})(emax-emin),\]  
\(emax=\log_{10}\lambda_{max}\) and \(emin=\log_{10}\lambda_{min}\).  
Because the function \(u\) must be computed by minimizing (3) and this is done by using an iterative procedure, we decided to update \(\lambda_{i,j}\) at each iteration, while using the current value of \(u_{i,j}\). On the other hand, in this case evaluating \(f\) is computationally cheaper than in the previous approaches, which apply two-dimensional convolution operators; thus, the cost of the iterative update of \(\lambda_{i,j}\) is practically negligible.
## 3 Thin-Film Materials and Their Applications
### Organic Thin-Films  
Organic thin-films possess excellent properties, for instance, mechanical flexibility, low cost, and relative ease of fabrication, which have myriad applications. Over the past two decades, there have always been new applications, which are not easy to realize with traditional electronics with inorganic semiconductors. It has resulted in pushing people to do more research on organic transistors [170; 171], photovoltaic cells, energy harvester [61], light-emitting diodes [172], electronic devices [173], sensors [174], and memristors [20]. Compared with inorganic counterparts, organic technologies can address energy and cost inefficiency issues. Moreover, organic thin films play an important role in green electronics, such as degradable small-molecule and polymer thin-film materials [2; 72; 175]. In this review, the distinctive research and the development of thin films made with organic materials for environmental-friendly applications will be introduced in the following sequence.
# A Knowledge Graph Framework to Support Life Cycle Assessment for Sustainable Decision-Making
## 3 Methodology
### Synthetic Data Integration
The implementation of the approach begins with data pre-processing and vectorization, where existing LCI data from databases are collected and standardized to ensure consistency. These data, which include, for example, material properties, production methods, and environmental impacts, are transformed into vector embeddings using pre-trained language models such as GPT-4 and vectorization tools like sentence transformers. These embeddings encode semantic relationships between variables and are stored in a vector database to facilitate the efficient retrieval of contextually relevant data. When missing data are identified, a structured query is constructed based on the known attributes of the target entity, such as material type, production process, and application context. This query is executed against the vector database using similarity search algorithms to retrieve the most contextually similar entries. These entries serve as the basis for estimating the missing data. A prompt is then constructed to guide the language model in generating the estimate. This prompt incorporates the retrieved context and specifies the known attributes of the target material, ensuring that the model has sufficient information to produce a plausible estimate. The estimated data are integrated into the KG as a property of the relevant node. Furthermore, each estimated value is accompanied by metadata indicating that the data are estimated, rather than sourced from a database. This ensures that the estimation process is transparent and traceable within the KG. As empirical data become available, the estimated values are validated and replaced as necessary. The system supports iterative updates to refine the estimation process, using the KG's ability to maintain historical annotations for traceability. This iterative approach ensures that the KG evolves over time, progressively improving its accuracy and reliability.
## 4 Experimental Results and Analysis
### Key Point Detection Experiment
#### 4.1.2 Experimental Results  
Using the above training strategy, the HRnet key point detection model was trained using the \"measurement unit\" key point dataset. The results are shown in Table 4, with an accuracy of 95.6% and a recall rate of 96.7%. The visualization results are shown in Figure 11. Figure 11 shows the detection results of the model trained for 150 epochs, and Figure 11 shows the detection results of the model trained for 50
# Integration of _Salmonella_ into Combination Cancer Therapy
## 3 _Salmonella_ in Combination with Other Approaches to Cancer Therapy
### Combination with Immunomodulatory Cytokines  
The approval of using immunomodulatory cytokines as a part of cancer therapeutics dates back to late 20 th century, when IL-2 was approved by the USFDA for treatment of metastatic kidney cancer and metastatic melanoma [146] and IFN-alpha for hairy cell leukemia [147]. In Europe, TNF was licensed for treating irresectable soft tissue sarcoma. Nowadays, these cytokines are no longer used in clinical applications due to the severe toxicities associated with their systemic administration and their limited effectiveness [3; 148]. _Salmonella_'s ability to specifically target tumor tissues and the fact that it can be easily genetically modified raised the notion of utilizing _Salmonella_ as a vector to carry immunomodulatory agents to tumor tissues and express them under a controlled expression system. For example, the oral administration of _S. typhimurium_ expressing human IL-2 was successful in enhancing the anti-tumor effects and reducing the number of metastasis in mice with unresectable hepatic malignancies [87]. It was also shown that CD8\({}^{+}\) T cells and NK cells were responsible for the antitumor activity [149]. This was also shown in other studies that utilized _Salmonella_ carrying IL-2 gene for the treatment of murine osteosarcoma [150] and melanoma [56]. In a canine osteosarcoma model, _Salmonella_ expressing IL-2 resulted in a prolonged disease-free interval [151]. In patients with metastatic gastrointestinal cancer, attenuated _Salmonella_ carrying IL-2 resulted in an increase in circulating NK and NKT cells but with no survival advantage [152]. In the B16F10 melanoma model, subcutaneous administration of _S. typhimurium_ expressing IFN-\(\gamma\) resulted in efficient tumor inhibition and prolonged survival mainly driven by the NK cell response [153]. _S. typhimurium_ harboring TNF-\(\alpha\) also showed potential against murine melanoma, with an ability to enhance the outcome of other cancer therapeutics [154]. In a study conducted by [PERSON] et al., _Salmonella_ expressing the chemokine CCL21 significantly inhibited the growth of multi-drug resistant murine carcinomas in a CD4\({}^{+}\) and CD8\({}^{+}\) cell-dependent manner. This inhibition was associated with elevated levels of IFN- \(\gamma\), CXCL9 and CXCL10 [155]. Several other studies [156; 157; 158; 159] utilized different cytokine-expressing bacteria and emphasized the remarkable potential of _Salmonella_ to deliver immunomodulators in an efficient and well-tolerated manner.
## 5 Simulation Results and Discussions
### Effects of Different Ambient Light Sources
The results demonstrate that QPSK outperforms both NRZ and RZ in terms of data rate due to its higher spectral efficiency, achieving up to 1000 Mbps in SISO and 4000 Mbps in MIMO (4 \(\times\) 4) configurations. NRZ, with a spectral efficiency of 1 bit/symbol, provides a balanced performance with data rates of 500 Mbps (SISO) and 2000 Mbps (MIMO), making it a viable choice when both robustness and throughput are important. Conversely, RZ shows the lowest data rates (250 Mbps in SISO and 1000 Mbps in MIMO) due to its lower duty cycle and lower energy per symbol. The use of MIMO architecture significantly enhances data rate across all modulation schemes through spatial multiplexing, showing a 4-times increase in data rate compared to SISO when using 4 \(\times\) 4 configurations. Figure 18: Comparison of BER performance versus ambient light power under various ambient lighting conditions.
## 5 Designs of TDMA Protocol-Based Distributed Coalition Formation Games
### Slot Allocation Protocol of DCF Game  
In the slot allocation protocol, UAV nodes are divided into one network header and the members. They transmit data at pre-allocated slots after time synchronization. We assume that UAV nodes in the multi-UAV network undergo time synchronization via a GPS system. As shown in Figure 4, the control time slot \(C_{ik}\) is pre-allocated to UAV-\(i\), \(k\) = 1:10, and the data time slot \(D_{ik}\) is pre-allocated to UAV-\(i\), \(k\) = 1:40. Before the slot allocation protocol operates, the UAV nodes send control and data packets in their own pre-allocation slots. Each node in the network achieves its three-hop neighbors via the neighbor discovery protocol, which runs periodically at the control slots.  
The network header is the organizer of the DCF game for the whole network, which starts the DCF game and confirms the coalition structure. The member UAV node operates the join-and-leave rule and sends a request-of-new-coalition (ROC) message to the network header. The network header collects all ROC messages of the whole member UAV nodes and decides the next coalition structure. All the messages are transmitted to the pre-allocated control slot. The detailed slot allocation process is described as follows:  
1. The network header broadcasts the DCF message to the whole network in its control slot. The DCF message includes the current coalition structure \(\pi(t)=\{S_{1}(t),\ldots,S_{M}(t)\}\) with a sequence number, which is confirmed by the network header.
2. Every member node forwards the DCF message to other nodes once in the form of a broadcast at the pre-allocated control slot after it receives and stores the coalition structure \(\pi(t)\). All UAV nodes transmit data packets in the data slots based on the slot allocation scheme defined by the coalition structure in the next repetition interval.
3. An ROC message is generated after operating the join-and-leave rule based on the coalition structure of the DCF message broadcast by the network header. Then, the member node sends the ROC message to the network header in the form of a broadcast.
4. Other member nodes forward the ROC message once in the form of a broadcast to ensure that it can be received by the network header.
5. After every member node has sent ROC messages, the network header decides the new coalition message structure based on the arrival order of ROC messages. Arriving earlier means the node is closer to the network header and its pre-allocated slot is in the front.
6. The network header broadcasts the new DCF message, which includes the new coalition structure, to the whole network.
7. Step 1 to step 6 are repeated in the whole network running time. The data packets are transmitted to the data slots allocated by the coalition structure simultaneously without waiting for the Algorithm 2 to converge.
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 3 Experiments
### Measurement Results of Real Human Faces  
The measurements to further verify that this proposed structured light system can work well for real human faces were performed. Figure 9 shows the 3D measurement results of two real people faces. One of the captured fringe patterns for each measurement is shown in Figure 9a,c. Figure 9b,d show, respectively, the 3D shape of the first face measurement and the second face measurement results in a front view.  
Because of the influence of face skin colour and reflectivity, the surface quality of the facial reconstruction is slightly worse than the face mask reconstruction, especially in terms of noise and smoothing. In any case, the method provides enough face data for three-dimensional face  
Figure 8: The measurement results of a face mask. (**a**) One of the captured fringe images; (**b**) Unwrapped phase map by using the projector of MEMS; (**c**) Reconstructed 3D geometry using phase map shown in (**b**); (**d**) Reconstructed 3D geometry by using the projector of DLP; (**e**) 3D data comparison in a cross-section between MEMS projection and DLP projection, the cross-section on the forehead with purple line as shown in (**c,d**); (**f**) 3D data comparison in a cross-section between MEMS projection and DLP projection, the cross-section on the middle face with red line as shown in (**c,d**).  
Figure 9: The measurement results of capturing the real faces. (**a**) One of the captured face images with infrared fringe from the first person; (**b**) 3D facial reconstruction result in front view of the first person; (**c**) One of the captured face images with infrared fringe from the second person; (**d**) 3D facial reconstruction result in the front view of the second person.  
shape reconstruction, which can be improved by adjusting optical parameters and also by using anti-glare spays.
## 2 Theoretical Foundation
### Shortest Path Search  
The Dijkstra algorithm is an algorithm that calculates the minimum cost between the vertices--in this paper, called \"points\"--the origin point, and the destination point in a graph (\(G\)). All points are connected by weighted edges where the weights represent the distances between the points. This algorithm is capable of calculating the minimum cost from the starting point to all other points in the graph.  
Its complexity \((O(E\log P))\), where \"\(E\)\" represents the number of edges and \"\(P\)\" the number of points of the algorithm, can be influenced by the number of points and edges present in the graph, the weight of its edges, and the distance between neighbors.  
The greater the number of neighbors that a node has, the greater its complexity will be as the algorithm will need to perform a greater number of tests with less weight, thus leading to an increase in execution time.