esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Purification and Preliminary Characterisation of PSPE  
The crude polysaccharides were extracted by EAE, and the free protein was removed by Sevag method. Then the polysaccharide solution was further purified with Q-Sepharose FF column and Sephadex G-25 column, and one main fraction named PSPE was obtained. The yield of PSPE counted with the crude polysaccharides was 18.24%, and the sugar content of PSPE was 87.25% with the protein content of 8.05%. The UV spectrum of PSPE was shown in Figure 4A, the feeble absorption at 260-280 nm also indicated the existence of protein. The purity and molecular weight of PSPE were determined by HPLC, as shown in Figure 4B, PSPE was eluted as a single symmetrical peak, which indicated that PSPE was homogeneous. According to the retention time of the standard samples, the regression equation correlated the molecular weight was \(\mathrm{lg}M_{\mathrm{w}}=-0.352t+9.83\), \(R^{2}=0.9987\). Thus, the molecular weight of PSPE was calculated as 16,700 g/mol (\(t=15.928\) min). In our earlier work, HWE and UAE have been used to extract the crude polysaccharides from pumpkin seeds, and then the purified polysaccharides (PSP-I and PSP-I) were gained after further two-step column separation, respectively. According to the molecular weight of PSP-I (21,100 g/mol), PSP-1 (3728 g/mol), and PSPE (16,700 g/mol), PSP-1 extracted by UAE had the smallest molecular weight among three methods (HWE, UAE and EAE), which showed UAE was more suitable to extract polysaccharides with low molecular weight. This result was consistent with the reported literature [16].  
GC analysis (GC profile not listed) showed that PSPE contained three monosaccharide species (Man, Glc and Gal in a molar ratio of 1.00:3.84:1.62), which was well matched with that found in PSP-1 (Man, Glc and Gal in a molar ratio of 1.00:4.26:5.78). However, the distinction between the two molar ratios was obvious, indicating that PSPE was a new and different polysaccharides.
# Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT
## 3 Results
### Artifact Correction Effects on Material Samples  
Figure 6 demonstrates the aliasing artifact correction effects of the proposed method when applied to material samples. These samples are composed of tungsten (W), silicon carbide (SiC), and titanium (Ti)(referred to as TiW). The bright areas on the CT slices represent W, while the ring structures surrounding W are made of SiC, and the remaining structures consist of Ti. There are a total of four samples, of which two are used for training (3930 slices), one for validation (1200 slices), and one for testing (1500 slices). The input to the network are slices reconstructed from 200 projection images, and the network labels are slices reconstructed from 1200 projection images. The network model and parameter settings are consistent with those used for the mouse brain data. Figure 6a shows the slice image reconstructed directly using FBP, Figure 6b shows the artifact correction result using the algorithm proposed in this paper, and Figure 6c is the label image. After using the proposed algorithm, the aliasing artifacts were corrected, and the image contrast was improved. Figure 6(a2,b2) are the absolute error images between Figure 6a,b and the label images. The deviation between the slice after applying the artifact correction algorithm and the label decreases, as illustrated in Figure 6(b2). This result demonstrates that the proposed method can be used for aliasing artifact correction in different types of data.  
Figure 5: Artifact correction effects of different algorithms under 400 views. (**a**) Slice reconstructed by FBP, (**b**) slice corrected by DD-Net, (**c**) slice corrected by FBPConvNet, (**d**) artifact correction using proposed method, (**e**) full-view CT slice. (**a1–e1**) Enlarged images of the red box in (**a–e**). (**a2–d2**) Absolute error images between (**a–d**) and (**e**). The display window is \([0,0.12]\) for the absolute error images.
# Non-Cooperative Spacecraft Pose Measurement with Binocular Camera and TOF Camera Collaboration
## 4 Experiment and Analysis
### Rotation Experiment at High Speed
For the improved ICP point cloud registration step, the complexity of the algorithm is O(N). As shown in the figure below, the calculation times of the initial pose obtained through the proposed method and FPFH method were compared. All mentioned methods were implemented on a PC (I7-8700 at 3.2 GHz, 16 GB RAM) with Visual Studio 2019. The programming language was C++. The OPENCV library, Point Cloud Library (PCL), and Ceres optimization library were used in this study. As shown in Figure 20, the calculation time of different initial value acquisition methods are compared. Figure 19: The pose errors resulting from different thresholds: (**a**) position error; (**b**) angle error. The running time of the initial pose obtained using the proposed method fluctuated around 0.6 s. However, the value acquired by the FPFH method was within 2.5 to 4.5 s. There was an improvement in the initial pose acquisition time when using the proposed method. In summary, the relative pose error estimated in this paper was the smallest in comparison to similar experiments. The method in this paper displayed less running time, which demonstrates that it is more conducive to the application of actual control.
# A Low Energy IoT Application Using Beacon for Indoor Localization
## 6 Experimental Results
The following Table 5 summarizes the values of true positives (TP), false negatives (FN), false positives (FP) and true negatives (TN). In order to evaluate its performance, taking into consideration one area at a time, tests were carried out verifying the correct classification of the target based on the position in which it was located (whether inside the area or outside). In fact, the tests conducted statically were carried out by randomly varying the presence of the target inside and outside the area. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Beacon 3** & & & & \\ \hline  & (1;4) & (2;4) & (3;4) & (4;4) \\  & (1;3) & (2;3) & (3;3) & (4;3) \\  & (1;2) & (2;2) & (3;2) & (4;2) \\
2 \(m\) & (1;1) & (2;1) & (3;1) & (4;1) \\
**Beacon 1** & _2 m_ & & & & **Beacon 2** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Configuration and zoning. Figure 8: Test on zone (3; 3). In order to evaluate the test conducted, the precision, recall, accuracy and F1-score values were calculated for each zone and the average was then made for each parameter:  
\[Precision_{mean}=\frac{TP}{TP+FP}=0.88\]  
\[recall_{mean}=\frac{TP}{TP+FN}=0.85\]  
\[accuracy_{mean}=\frac{TP+TN}{TP+TN+FP+FN}=0.83\]  
\[F1_{score}=2.\frac{recall_{mean}.precision_{mean}}{recall_{mean}+precision_{ mean}}=0.87\]  
From this experimental phase, it is possible to verify how it is possible to carry out, in the absence of interference sources, that the proposed method lends itself to these types of wearable applications. It is also evident that everything depends very much on the RSSI readings, as in the three masters, a stable and constant localization is not allowed but readings are provided in terms of coordinates (x, y), which are incorrect, greatly compromising the precision. The problem is due to the fact that the value of the detected power signal depends on both the device and the area in which you want to locate the slave device. Therefore, it is essential that the environment is free of interference. Although the performances obtained are valid, these provide us with valid elements of discussion to make changes in order to improve the system. A first could be, being that the outermost areas affected by the greatest error, the addition of a fourth component that can better trilaterate the signal. Another possible improvement could be to use devices with better signal strength for the masters.
## 1 Introduction  
With the rapid development of artificial intelligence, cloud storage, and hyperscale data centers, the speed of data generation and processing continues to accelerate, resulting in a sharp increase in the demand for data storage [1]. In particular, in storage technology, read latency has become a key challenge because it directly affects the response speed and processing efficiency of the system [2, 3]. Optimizing the read latency of NAND flash memory channels can not only improve data access speed but also meet the needs of modern applications for high-speed data processing. At the same time, with the increase in storage density, despite the increase in storage capacity, the reliability of NAND flash memory faces challenges [4, 5]. In an environment with a high bit-error rate, it becomes particularly important to use multi-threshold soft decision decoding technology, which effectively enhances error correction performance by applying multiple read voltages to achieve fine-grained memory perception, but this may also introduce higher perception latency [6, 7]. In addition, the layered normalized minimum sum (LNMS) decoding algorithm has attracted widespread attention due to its high efficiency in the low-density parity-check (LDPC) decoding process. LDPC decoding first calculates the log-likelihood ratio (LLR) of each bit, and then corrects the error bit by iteratively updating the information of the check node (CN) and variable node (VN). This process inherently leads to higher decoding latency [8, 9]. In each iteration of LNMS decoding, it is necessary to traverse each row of the check matrix to update the corresponding VN and CN information. The unit that processes each row during one iteration is called a layered processing unit (LPU), which is the most time-consuming step in the decoding process. Therefore, in application environments with strict real-time requirements, it is particularly important to develop an iterative decoding strategy that can dynamically adjust the average number of LPUs executed during decoding.
# Towards Client Selection in Satellite Federated Learning
## 5 Simulation  
We simulated an LEO constellation with 120 satellites by STK, and imported the information on links between clients and PS as a JSON file. Hence, our program checked the connections between clients and the PS every time slot (0.1 s) based the JSON file, so we can know which clients were connected to the PS at any time. Then, we compared the SFL between satellite-to-ground and inter-satellite communication, and validated effectiveness of our CS algorithm.
# Real-Time, Deep Learning Based Wrong Direction Detection
## 2 Background
### Vehicle Tracking  
Car tracking is based on object-tracking techniques and includes three main processes: (1) taking an initial set of detected objects; (2) registering the detected cars and giving them unique identifications (IDs); (3) tracking them as they move around a continuous frame, maintaining the ID assignments, and erasing them once the cars disappear. Moreover, object tracking allows us to count several cars in a frame by counting unique object IDs. Ideally, an object tracking algorithm must satisfy the following conditions:  
* require the object detection phase only once;
* be much faster than the actual object detector itself;
* handle a tracked object that \"disappears\";
* be robust to occlusions;
* pick up objects that are \"lost\" between frames.
## 3 Materials and Methods
### Model Architecture
#### 3.2.1 Single Response Block  
The single response block takes a single modality from a single response and passes it through a transformer network. The inputs to this network include tokenized text, raw  
Figure 4: The structure of the two blocks used in the model: (**a**) The prediction head, which takes embeddings from upstream models and produces scores; (**b**) the transformer encoder block described in [9].  
Figure 3: Model architecture of the final PHQ-V and GAD-V model consisting of three different parts: (**a**) A single response block which finely tuned a pre-trained transformer; (**b**) a multimodal block which combines multiple single response blocks using a self-attention transformer block; and (**c**) a multi-response block which combines multiple questions and uses a self-attention transformer combined with question dropout. The final PHQ/GAD layer shows that scores can be assessed at any of the three blocks, or the embeddings can be passed down to further blocks allowing comparison of scores at different levels of abstraction.  
audio, or pre-processed Affectiva features, depending on the modality being trained. We utilized Affectiva in our model to provide a set of 44 features that tracked facial position, emotional response, and facial action units. For text, the transformer network was the pre-trained \"paraphrase-distilroberta-base-v2\" model [29] from huggingface, for audio it was the pre-trained \"facebook/hubert-large-l160 k\" model [12] from huggingface, and for video it was a previously untrained model consisting of a simple network: a linear projection -> relu -> layer norm -> two transformer blocks. For audio and video, a learned embedding was concatenated to the beginning of the sequence (before being passed through any transformer blocks), while for text the sentence token was used. These tokens are referred to as class tokens in Figure 4 and throughout this research.  
The class token of the result was then run through the prediction head (see Figure 4), for the purpose of learning to predict survey scores. All pre-trained models remained unfrozen, so that they could be changed throughout the training process. The model predicts both the actual score (i.e., total score) as well as the classification bucket (i.e., depressed, not-depressed) for both PHQ-9 & GAD-7. A joint loss technique was used to optimize the mean absolute error (MAE) for regression logits and the cross-entropy loss for classification logits, by using a weighted sum of all sub-losses. Losses were scaled to give approximately equal weight to each loss even though they have different scales. Weights were as follows: phq mae = 0.11, gad mae = 0.15, phq cross entropy = 0.62, gad cross entropy = 0.72.  
The main advantage of predicting scores straight from a single question is that the final model can predict scores for any individual question by itself, or can provide a rich embedding that can be combined downstream with other questions and modalities. This allows the use of the assessment with a single question or in combination with multiple questions, as in the multi-response block.
# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 3 Problem Description
### Computational Models
#### 3.2.2 Edge Computing  
In this paper, with the Cloud-Edge-end network system, mobile devices communicate with MEC servers through wireless access point connections, and the MEC and cloud layer communicate by deploying high-speed cables. The non-orthogonal frequency division multiple access techniques are used as a multiple access scheme in the uplink [30]. When user \(n\) offloads the computational task \(\tau_{n}\) to the MEC server for processing, the latency includes the user information set to transfer to the cloud center, the processing latency of the cloud center, the uplink transfer latency of the user task and the execution latency of the MEC server. Since the cloud center has sufficient computing resources and the size of the output results is generally much smaller than the input, we ignore the data return latency of the cloud center and edge servers. The set of users offloaded to the MEC server is noted as \(N_{off}\).  
Set the transmission rate from the end-user to the cloud center to a constant value \(r_{c}\), the channel bandwidth of BS is \(B_{n}\), \(p_{n}\) and \(h_{n}\) are the uplink transmission power and channel gain of user \(n\), respectively, \(\sigma^{2}\) is the noise power and \(\omega\) indicates the interference brought by other users to user \(n\). \(\omega=\sum_{i\in N_{off\setminus\{n\}}}p_{i}h_{i}\), then the data uplink transmission rate is  
\[r_{n}=Blog_{2}(1+\frac{p_{n}h_{n}}{\sum_{i\in N_{off\setminus\{n\}}}p_{i}h_{i }+\sigma^{2}}) \tag{4}\]  
The edge server processing model latency is  
\[t_{n}^{m}=t_{n}^{c,up}+t_{n}^{c,sear}+t_{n}^{m,tran}+t_{n}^{m,exe} \tag{5}\]  
where \(t_{n}^{c,up}\) denotes the time of uploading user datasets to the cloud center, \(t_{n}^{c,up}=\frac{l_{n}}{r_{c}}\); \(t_{n}^{c,sear}\) is the cloud center processing time, \(t_{n}^{c,sear}=\frac{C_{n}}{f^{c}}\); \(t_{n}^{m,trans}\) is the computational task offload time to the edge server, \(t_{n}^{m,tran}=\frac{D_{n}}{r_{n}}\); \(t_{n}^{c,sear}\) is the cloud center processing time; and \(t_{n}^{m,exe}\)is the edge server processing time, \(t_{n}^{m,exe}=\frac{C_{n}}{f_{n}^{m}}\), where \(f_{n}^{m}\) is the computational resource allocated to user \(n\) by the edge server.  
The energy consumption of the edge server processing task is divided into two parts: data transmission energy consumption and server computation energy consumption, and the energy consumption of the cloud computing center is ignored in this paper. The transmission energy consumption is \(e_{n}^{m,trans}=p_{n}\Big{(}\frac{I_{n}}{r_{c}}+\frac{D_{n}}{r_{n}}\Big{)}\); the MEC server computation energy consumption is \(e_{n}^{m,exe}=C_{n}\theta_{n}^{m}\); and the total energy consumption is  
\[e_{n}^{m}=e_{n}^{m,trans}+e_{n}^{m,exe} \tag{6}\]  
The overhead cost of offloading the computational tasks to the edge server for processing is  
\[E_{n}^{m}=\beta_{n}^{t}t_{n}^{m}+\beta_{n}^{e}e_{n}^{m} \tag{7}\]
# Valorisation of Star Wastewater by Anaerobic Fermentation
## 2 Biogas Production
### Reactor Types  
Examples of industrial-scale applications for the anaerobic digestion of starch wastewater are described by [PERSON] et al. [3]. Common approaches are UASB (upflow anaerobic sludge blanket) as well as for increased kg COD m\({}^{-3}\) d\({}^{-1}\) rates, EGSB (expanded granular sludge bed) reactors. The two systems differ in reactor design and influx rate. Whereas UASB reactors are commonly built to be wider in diameter and rather shorter in height, EGSB reactors are known for the opposite. As described in [PERSON] [10], the increased influx rate as well as the smaller diameter expand the granular sludge bed and perform an increased fluidisation. Therefore, a higher contact rate between sludge and wastewater is achieved compared to UASB reactors. [PERSON] et al. [3] also describe modified anaerobic filters which are applied in the starch industry. In these reactors, the biomass is attached to a fixed/packed bed in the reactor. In the literature, other interesting reactor types are described for starch wastewater treatment: anaerobic baffled reactor [11], up-flow multistage anaerobic reactor [12] and anaerobic tapered fluidised bed reactor [13]. For small-scale starch production in Colombia, the flow filter with bamboo as a support is described [14]. Finally, the anaerobic pond treatment of starch wastewater should also be mentioned. [PERSON] and [PERSON] describe such a case study in Thailand [15]. However, these ponds often operated uncovered, with the main focus on the treatment of the wastewater. Consequently, biogas is directly released to the atmosphere and not recovered for the process. Fortunately, there are approaches to cover such lagoons and recover the methane produced [6].
# Anodic Oxidation of 3D Printed Ti6 Al4V Scaffold Surfaces: In Vitro Studies
## 4 Conclusions  
In this study, we proposed the surface treatment of Ti6 Al4V scaffolds using anodic oxidation. We found that the treatment enabled the formation of a nanoporous layer of crystalline titanium oxide. Additionally, we investigated the response of Saos-2 cells, with a focus on cell adhesion, proliferation, and morphology. Despite the chemical composition of the anodized surface promoting cell differentiation at the beginning of the study, the cells had weak binding to the surface containing the nanopores, which resulted in the lowest cell adhesion. Based on the analyzed results, it is evident that the anodic oxidation technique, although well-established in the literature for titanium and its alloys, was unsatisfactory when applied to Ti6 Al4V porous scaffolds, produced via additive manufacturing in our case. These findings highlight the necessity of exploring alternative surface modification techniques for scaffolds that can match micro-surfaces and nano-surfaces. This is because these surfaces play a crucial role in generating stronger bonding strength.  
Conceptualization, T.K.C.d.S. and A.P.R.A.; Methodology, T.K.C.d.S., F.R.M., S.P., R.L.R., A.L.d.A.E., G.A.L. and A.P.R.A.; Software, F.R.M., J.P.A.C. and G.A.L.; Formal analysis, F.R.M., S.P. and J.P.A.C.; Investigation, T.K.C.d.S., F.R.M., J.M.O. and G.A.L.; Resources, R.L.R., J.M.O. and A.P.R.A.; Data curation, T.K.C.d.S., F.R.M., J.P.A.C., G.A.L. and A.P.R.A.; Writing--original draft, T.K.C.d.S. and F.R.M.; Writing--review & editing, T.K.C.d.S., S.P., R.L.R., J.M.O., A.L.d.A.E., J.P.A.C. and A.P.R.A.; Visualization, T.K.C.d.S. and J.P.A.C.; Supervision, A.P.R.A.; Funding acquisition, R.L.R., J.M.O. and A.P.R.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brazil (CAPES)--Finance Code 001, CAPES PrInt--88887.571867/2020-00 and the Sao Paulo Research Foundation (FAPESP) 2020/05612-8. The authors would like to acknowledge the Multiple Users and Shared Facilities from LAprint for the use of the PBF-EB machine at the Center for Information Technology Renato Archer.  
Not applicable.  
All necessary data supporting the reported results have been meticulously included in the manuscript. However, for specific requests or inquiries regarding the data, interested parties are welcome to contact the corresponding author via email. We are committed to transparency and open communication, and we will promptly address any data-related queries.  
The authors declare no conflict of interest.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Performance and Resources Metrics
#### 5.7.1 Computational Load and Communication Efficiency
These factors collectively strain system resources, potentially causing longer simulation run times with a real-time factor of 0.12, ultimately reducing the overall efficiency of the swarm model. The firefly model also demands substantial computational resources, though not to the extent of the honeybee model. The ant and baseline models, however, maintain more manageable resource usage, making them more stable as the number of robots increases. Overall, while the honeybee model excels in efficiency, scalability, and selectivity, its complex control scheme results in extensive computational resource demands. Therefore, selecting the appropriate swarm model based on the specific operational environment and the number of robots involved is crucial, as these factors critically influence the system's performance and scalability.