esa-sceva/satcom-chunk-collection huggingface dataset:
# CoFi: Coding-Assisted File Distribution over a Wireless LAN
## 3 Coding Based Batching and Caching in CoFi
### Coding Based MAC Layer Caching
#### 3.3.2 The Relay Scheduling Problem  
The scheduling scheme in CoFi triggers the relay to transmit cached packets to the intended client in order to save the channel time wasted on the lossy link between the AP and that client. Once the best relay can decode an entire overheard batch, it will take over the role of the AP, serving the intended client by sending a random linear combination of packets in the batch. When the AP overhears a coded packet from the relay, it stops transmitting to the weak client since it knows the relay has a full batch of data that can rescue the target client.  
Beside triggering the relay, the scheduling algorithm must ensure liveness, i.e., it can continue to the next batch after the client can decode the current one. Towards this end, we marked the relay or the client as \"FULL\" whenever they can decode the current batch. A marked node will broadcast an ACK whenever it receives another packet with the current batch sequence, which implies that the sender is still unaware of the decodability at this node. Such a persistent feedback scheme essentially allows the relay to forward the ACK to the AP, so that it can proceed to a new batch of data.
## 3 Results  
Results showed that, among the total selected research papers (91), 15 were CYM related and 76 were crop traits retrieval related. Year-wise and source-wise publications are given in Tables 1 and 2.  
It can be observed that the highest number (21) of papers were published in 2022, followed by 2021 and 2020. The lower number of publications (8) during 2023 as compared to 2022 and 2021 is due to the half-year period of consideration of publications, ending on 30 June 2023. The lowest number (4) of publications were available for the year 2017. From this, it can also be concluded that RTM use for yield modeling and crop traits retrieval is a new dimension and will be popularized in the coming era, particularly in the context of precision and smart agriculture.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Years**} & \multicolumn{2}{c}{**Crop Yield Prediction**} & \multicolumn{3}{c}{**Crop Traits Retrieval**} \\ \cline{2-7}  & **RTM** & **Integrated with CGM** & **LAI** & **LAI and Others (CWC, CCC, etc.)** & **Other than LAI** & **Total** \\ \hline
2023 & 0 & 0 & 2 & 3 & 3 & 8 \\ \hline
2022 & 2 & 2 & 3 & 9 & 5 & 21 \\ \hline
2021 & 1 & 1 & 1 & 5 & 7 & 15 \\ \hline
2020 & 0 & 3 & 2 & 0 & 5 & 10 \\ \hline
2019 & 0 & 1 & 2 & 2 & 3 & 8 \\ \hline
2018 & 0 & 2 & 2 & 4 & 3 & 11 \\ \hline
2017 & 0 & 0 &Among database sources, ScienceDirect was a significant source, having the highest number of publications (53), followed by Scopus (18) and MDPI (13).  
As per taxonomical arrangement, research articles were segregated into two categories of crop yield prediction and crop traits retrieval. Among the 91 total articles, only 15 were relevant to crop yield prediction and 76 had RTM applications for the retrieval of crop traits. A taxonomical summary of the research studies is given in Figure 2. This taxonomical development gives better representation of results and easier understanding of this study's analysis. A further detailed taxonomical segregation of each category is also summarized in the results.
# Spreadsheets as Interoperability Solution for Business Process Representation
## 5 Mapping a Spreadsheet with a BPMN Model
### Supported Process Elements and Their Spreadsheet Model  
The proposed approach supports the following process elements: start events, intermediate events, end events, tasks, logical gateways, swimlanes and data objects. These individual elements together with their spreadsheet model representation have been described in the subsections below and listed in Table 4.
## 2 Materials and Methods
### Method Validation
#### 2.4.2 Linearity  
The method's linearity was evaluated through matrix-matched calibration using seven concentration levels (0.01, 0.02, 0.05, 0.1, 0.5, 1.0, and 2.0 \(\upmu\)g/mL) covering the expected analytical range for all six macrolides. Calibration standards were prepared in blank matrix extracts and analyzed in randomized order to avoid sequence effects. Quantitative analysis was based on the averaged peak areas (y-axis) plotted against nominal concentrations (x-axis, \(\upmu\)g/L). A weighted (1/x) least squares regression model was applied to establish the calibration curve, with the resulting regression equation (y = ax + b) and coefficient of determination (R\({}^{2}\)) used to evaluate linearity. Each calibration level was analyzed in triplicate to verify reproducibility across the working range.
## 4 Results and Discussions
### Experimental Results
#### 4.2.1 Unbalanced Grid  
In this test, initially the grid is balanced, i.e., no negative and zero sequence components. Suddenly, the positive sequence amplitude had a step change of \(-\)0.2 p.u. Moreover, negative sequence and positive sequence voltages of 0.2 p.u. and 0.1 p.u. are added into the grid. In addition, the phase angle of the positive, negative, and zero sequence components had a step change of \(-\)22.5\({}^{\circ}\), \(-\)22.5\({}^{\circ}\), and 22.5\({}^{\circ}\), respectively. Experimental results for this test are given in Figure 6. From the estimated frequencies, it can be seen that the three techniques have roughly similar dynamic performance and converged in \(\approx\)2 cycles. This validates the gain tuning. GAO converged faster than the others, however, with the highest peak overshoot. Similarly, SAO had the lowest peak overshoot and the same time the slowest convergence time among the selected techniques. The sequence extraction performance of all the techniques are similar. The techniques extracted the positive, negative, and zero sequence components from the measured three-phase grid voltage in a fast and accurate manner.
## 5 IETF ROLL Working Group
### Implementation and Evaluation
#### 5.2.4 Multipoint-to-Point Traffic  
In many IoT use cases, different sensors send their sensed data to a central sink. For this type of traffic RPL requires very limited control overhead. This overhead is further reduced by the use of the Trickle timer [45], which decreases the frequency of the sending of DIO messages when the network isstable. Also the responsibility of maintaining routes from leafs to the sink is delegated to each node by only selecting a parent which is closer (according to the OF) to the sink.
## 4 Results  
### Heuristics and Correlation Analysis  
Establishing guidelines or approximations used to evaluate performance data is known as heuristics. Figure 6's graphical representation verifies which words perform better than others in a variety of parameters. Moreover, correlation analysis measures how strongly and in which direction the metrics are related to one another. These analytics aid in identifying trends and revelations. The graphical representation in Figure 5 can be used to analyze phrase lengths across the dataset and offers insight into possible input size variability that affects model efficiency and resource requirements.  
Figure 5: Diagram for transformer embedding generation.  
Figure 6 consists of two histograms representing the following information:  
1. Distribution of unique senses per word (left)  
Here, the X-Axis represents the number of unique senses per word and the Y-Axis represents frequency (number of words with that many senses). The observation from the diagram indicates that most words have fewer unique senses (1-3 meanings). The frequency drops sharply as the number of senses increases. There are few outlier words with more than 10 senses, indicating highly polysemous words. A density curve (smoothed blue line) shows the distribution trend, confirming that words with higher polysemy are less frequent.
2. Distribution of Sentence Lengths (Right)  
Here, the X-Axis represents sentence length in terms of word count and the Y-Axis represents frequency (number of sentences of that length). The key observation from the diagram shows that the distribution is highly skewed, with most sentences having very few words (mostly 2-3 words). There are very few long sentences (above 6 words). A density curve (smoothed green line) follows the histogram, showing the extreme concentration of short sentences.
# An Overview of Quantum Machine Learning Research in China
## 4 Innovation Aspect Analysis
### Strategy Selection  
Innovation in strategy selection refers to the introduction of computational strategies that have not been previously applied to a certain QML algorithm. By selecting appropriatecomputational strategies and integrating them into QML models, researchers can acquire new QML models with better performance. A few study examples are presented as follows.  
[PERSON] et al. creatively applied particle swarm optimization (PSO) algorithm for the training of QNN models. As a collective intelligence-based optimization strategy, PSO was able to outperform conventional gradient descent-based optimization strategy (such as Adam) when dealing with certain problems such as 2D Hamiltonian ground state solution and many-body system quantum phase classification in terms of fewer optimization iterations and higher classification accuracy [44].  
For the SEQNN model developed in the aforementioned work [42], the innovative scheme was based on the combination of superposition strategy and one-vs.-all strategy. For the multi-class image classification task on the MNIST dataset, the SEQNN model achieved an accuracy of 87.56%, higher than other QML models like HQNN-Quanv and QNet.  
[PERSON] et al. first applied game theory strategies to privacy protection in QML and proposed a privacy game model of user-server-attacker in a hybrid classical quantum back-propagation neural network. Unlike previous studies, this model can set game strategies based on users' privacy requirements in practical applications to maximize the interests of users with different roles [46].
# Optimization and Hydration Mechanism of Ecological Ternary Cements Containing Phosphogypsum
## 2 Materials and Methodology  
### Materials, Mix Composition and Specim Preparation  
#### 2.1.1 Properties of Materials  
Phosphogypsum (PG), fly ash (FA) and Portland cement (PC) were utilized to prepare the phosphogypsum-based composite cementitious materials (PBM) in this study. The PG was excavated from the surface layer of a storage site, which belongs to a phosphogypsum tailings pond in Hubei province (China) and the FA was acquired from Ezhou Power Plant Group (Ezhou, China). The mean humidity of raw PG obtained from six samples was 56.7%. The PC (M 42.5 OPC, Chinese National Standard, GB 175-2007) was brought from Huaxin Cement Co., Ltd. (Wuhan, China), and the mineralogical compositions were obtained using an X-ray diffraction (XRD) analysis (see Figure 1, data provided by Huaxin) and were as follows: alite (C\({}_{3}\)S, 40.65%), belite (C\({}_{2}\)S, 34.52%), calcium aluminate (C\({}_{3}\)A, 10.87%) and ferrite (C\({}_{4}\)AF, 9.78%). The chemical compositions of PG, FA and PC were investigated by the means of X-ray fluorescence spectroscopy (XRF) and the results are presented in Table 1. The chemical characterization of raw materials is shown in Figure 2. Moreover, the physical properties of PG, FA and PC raw materials are shown in Table S1. Ordinary tap water (TW) was used to mix the raw materials.  
Hemihydrate gypsum (HG) (\(\beta\)-CaSO\({}_{4}\bullet\)1/2H\({}_{2}\)O, purity \(\geq\) 97.0%) was obtained from Acros Organics, Germany and dihydrate gypsum (DG) (CaSO\({}_{4}\bullet\)2H\({}_{2}\)O, purity \(\geq\) 99.0%) was purchased from Sinopharm Chemical Reagent Co. Ltd., China. A polycarbonate ether-based superplasticizer (PES) that met the ASTM C 494/C 499 M requirements. Type
# An Interactive Real-Time Locating System Based on Bluetooth Low-Energy Beacon Network +
## 4 Implementation and Experiments
### Experiment 1  
In this experiment, we would like to measure the response time from the Mobile App users' perspective. Specifically, we put the smartphone beside the Mesh Beacons and then open the Mobile App. At each of the four locations (\(A\) through \(D\)), we measure the response time five times, and show the results in Figure 9. Note that since the locating process is based on an internal table lookup in the Mobile App, the time needed to finish the locating process does not depend on the real locations. Therefore, in Figure 9 we only show the time taken for the Mobile App to receive the acknowledgement message.  
From Figure 9 we can see that at both locations \(A\) and \(B\) the observed response times are relatively stable, but at locations \(C\) and \(D\) some of the response times are more than 2.7 s. The reason is that at locations \(A\) and \(B\), they are only one hop away from the Mesh Router; while at locations \(C\) and \(D\), they are 2 and 3 hops away, respectively. It is straightforward that with a greater number of hops on the path, the probability of a lost presence message or acknowledgement message is also higher. Furthermore, we need to describe the behavior of the Mobile App in order to justify the result. In our  
Figure 8: Screenshots of our Mobile App (**a**) during the locating process (**b**) physical location determined (**c**) acknowledgement message received.  
Figure 9: Time taken for the Mobile App to receive the acknowledgement message in Experiment 1 (s).  
implementation, the Mobile App takes about 0.7 s to initialize the advertisement process, and then proceed to advertise the presence message for 1 s. After that, the Mobile App will start listening to the acknowledgement message from the Mesh Router. This explains why the observed minimum response time is around 1.7 s. If no acknowledgement message is received within 1 s, the Mobile App will advertise the presence message again for another 1 s. Knowing the behavior of the Mobile App, we can conclude that at locations \(A\) and \(B\), the Mobile App successfully received the acknowledgement message after the first advertisement period. However, from the measurement results at locations \(C\) and \(D\), we can find that some of the acknowledgement messages arrived after the second advertising period. Nevertheless, we think that the response time can be well-controlled if we place any two neighboring Mesh Beacons within a reasonable distance to keep the packet loss rate low. Furthermore, it is also possible to reduce the response time by shortening the advertising period and the waiting period because this is simply an implementation issue. Since the 0.7-s initialization process is inevitable, we expect that in the best case the minimum response time can be within 1 s.
# Router Activation Heuristics for Energy-Saving ECMP and Valiant Routing in Data Center Networks
## 1 Introduction
We propose an alternative solution, i.e., efficient heuristic algorithms for adaptive activity control of DCN nodes and energy-aware multipath routing calculation, and simulation-based analysis. Using simulation allows us to analyze dynamic scenarios and observe many phenomena outside the mathematical model's scope, e.g., congestion and packet loss. To sum up, this work's main contribution is to present and discuss several heuristics for equal-cost multipath (ECMP) and Valiant routing that reduce the energy consumption in networks interconnecting computing servers in large-scale data centers. It assumes selective deactivation of switches in periods of low demand and activation according to increasing traffic. The results of simulations of energy-efficient DNC management using these heuristics conducted on computing centers with different architectures are compared with the solution of the optimization problem mentioned above for energy-aware network management. All numerical results are presented along with conclusions and further research directions. The risks of implementing energy-saving algorithms in production environments are identified and discussed. It should be noted that the algorithms discussed, similar to all other energy-aware DCN management methods, are applicable in situations of incomplete network loading. In heavily used DCN, there is usually no room for energy saving, i.e., all devices have to work to guarantee the assumed quality of service. The paper is organized as follows. The problem of communication management in real data centers and the description of ECMP routing and Valiant protocol are reported in Section 2. Section 3 discusses related work on power control in computer networks and data centers provided in the literature. In Section 4, the DCN optimization problem for calculating energy-aware routing is defined and formulated. The heuristic algorithms for energy-efficient computer network management are presented in Section 5. The results of simulations are described and discussed in Section 6. Finally, conclusions and future research directions are drawn in Section 7.
## 1 Introduction
All of the above algorithms are suitable for the FDE of vehicle navigation systems in complex urban environments. However, the systems are mainly constructed using KF, EKF, and UKF filters, and the structure is challenging to change adaptively for sensor isolation and recovery [30]. Therefore, building a flexible system structure with multi-sensors is necessary to adapt to different scenarios. In this paper, a multi-source integrated navigation system with interactive multiple models based on factor graph optimization and multi-stage fault detection, isolation, and recovery functions for urban positioning is proposed to deal with the existing problems. On the one hand, the IMU/GNSS/LiDAR factor graph navigation system is constructed, and the bias of IMU pre-integration is estimated and corrected by GNSS pseudo-range difference measurements and LiDAR iterative closest point (ICP) measurements [31]. Therefore, multi-source asynchronous sensors are fused based on the high-frequency IMU integration by factor graph optimization. Then, the sliding window algorithm is applied to marginalize the historical information and guarantee the real-time performance of the whole system [32]. On the other hand, the location-level interactive multiple models algorithm based on factor graph optimization (IMMFGO) of GNSS and LiDAR is performed to update sensor weights in global optimization. The multi-stage fault detection, isolation, and recovery (MSFDIR) method is applied to reduce the impact of fault measurement and to adaptively optimize system structures. Vehicle experiments with adequate analysis have verified the accuracy and efficiency of the proposed method, which can realize good performance for positioning and fault detection for vehicle navigation in urban canyons. The main contributions of this paper are as follows:  
* An IMU/GNSS/LiDAR integrated navigation system framework based on factor graph optimization is constructed. Meanwhile, the error models of sensors are constructed in a formulaic factor graph optimization system, and the ISAM2 sliding window algorithm is applied to marginalize the historical information and improve the real-time performance. * The IMMFGO algorithm of GNSS and LiDAR sub-model measurements is applied to update the weights of the sensors in global optimization. The MSFDIR strategy is employed to detect, isolate and restore fault sensors and reconstruct the system frameworks to adapt to external disturbances. * Vehicle experiments are conducted to verify the effectiveness of the proposed methods. Qualitative and quantitative analyses are applied to comprehensively compare the performance of proposed IMMFGO and MSFDIR with other typical algorithms to demonstrate the superiority of the proposed method and the possibility of application in natural vehicle navigation systems.
## 2 State of the Art at TAE  
The TRIMET Aluminium SE Essen (TAE) plant consists of 360 pots in three production potrooms with EPT-14 PreBaked Point Feeder (PBPF) cells in the first two potrooms and EPT-17 PBPF cells in the third potroom. The prefix EPT stands for \"elektronische Prozesssteuerung Tonerde\" (computer controlled alumina feed). The number 14 indicates that the potlines originally operated at 140 kA [17]. Under normal process conditions a balanced feed strategy is applied, using a derived pseudo-resistance slope calculation to determine when the feed strategy should change from underfeed to overfeed [5]. During train and test time the first potroom was running at an amperage of 173 kA with a standard deviation of around 5 kA, while the second potroom was running at 165 kA with a standard deviation of around 2.5 kA and the third potroom was running at 171 kA with a standard deviation of only 1 kA.  
In industry a commonly used definition for AE detection by the process control system is when pot voltage exceeds 8 V for more than three consecutive seconds [4]. At TAE these thresholds are 7.5 V for 3.96 s. An AE is terminated when pot voltage is below 6.6 V for more than 39.96 consecutive seconds. An example of the voltage behaviour during an AE with four restarts can be seen in Figure 1. The termination of AEs are achieved in a quenching procedure, where the anode beam is lowered and additionally a massive overfeed of alumina is generated.  
If this does not attain any success AEs are terminated manually with wood poles. [PERSON] et al. [4] state that beneath conventional AEs (typically start on a few anodes and propagate to all anodes within seconds) there are also so called low voltage propagating AEs (LVP-AE) that exhibit peak voltages that fall below the threshold voltage (<8 V). LVP-AEs have a very similar PFC emission characteristic but their propagation is limited to a section of the pot and after propagation they self terminate. Furthermore [PERSON] et al. found so called non-propagating AEs (NP-AE) with no visible voltage signature. NP-AEs have a different PFC emission characteristic compared to conventional AEs and remain on several localised anodes. It is important to note that [PERSON] et al. claim that there is some evidence [18] that LVP-AEs and NP-AEs are more prevalent for large-sized, high amperage (\(>\)300 kA) pots. TAE's pots with a number of 26 anodes and a pot size of 7.2 m\(\times\) 3.4 m do not belong to the group of large-sized pots.  
Currently there is no AE prediction model implemented. As [PERSON] et al. [5] reported, AEs have only been reduced from a maintenance perspective until now.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Machine Learning Models
K-Nearest Neighbors (KNN) is an instance-based learner that classifies a point based on the majority class among its nearest neighbors, remaining straightforward to implement with minimal explicit training [115]. Finally, artificial neural networks (ANNs) utilize layers of interconnected units or \"neurons\" that learn complex, often non-linear mappings from inputs to outputs, excelling in tasks involving intricate feature interactions [116]. Differences among these algorithms revolve around their assumptions (e.g., independence in Naive Bayes), interpretability (e.g., Decision Trees and Random Forests), computational efficiency (e.g., LightGBM and XGBoost), and capability to capture non-linear relationships (e.g., ANNs and SVMs), making each algorithm more or less suitable depending on data characteristics and application requirements. All algorithms were implemented using Python v3.9. For the algorithms SVM, Decision Tree, Random Forest, Gaussian Naive Bayes, and K-Nearest Neighbors, specific functions for each were utilized from the'sklearn' package [117; 118]. The 'lightgbm' and 'xgboost' packages were used for LightGBM and XGBoost algorithms. Lightgbm is a gradient boosting framework developed by Microsoft, utilizing tree-based learning algorithms designed for speed and efficiency [112; 119]. Xgboost is a separate library providing a highly efficient implementation of gradient boosting [113; 120]. For the ANN algorithm, the TensorFlow package was employed [121; 122]. TensorFlow is an open-source machine learning framework developed by Google, primarily utilized for deep learning tasks.