esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing System Load of Effective Video Using a Network Model
## 3 System Model or Proposal Method
### SST (Spatial Segmented Transform)  
The SST method involves a network that performs conversion after setting an area for a designated object or other object. Since it is a format that learns not only the salient objects in the image but also background features through deep learning, the information about the areas other than the designated area is converted. This can compensate for the problems of underfitting and overfitting [14]. Finally, the undesignated part is converted around the FoV area. The SST method was produced based on spatial transformer networks technology. The proposed method divides the image by region and the image quality is transformed through deep learning to compensate for loss of image quality. When the image is artificially divided into individual areas and passed through a network, quality conversion occurs, so SST is used to prevent data loss in whatever conditions obtain without changing the shape of the image. Figure 13 shows the basic structure of SST.  
Figure 14 shows how the network handles the rest of the original image except for the RoI (Region of interest). In order to determine the RoI at an early stage, a method of using a designated area or target is employed, though information can also be extracted using a network such as CAM Shift. In the last step, non-RoI is extracted and converted into a new frame after the conversion process. This represents the process of outputting as video. The method is used to reduce system load.  
Figure 12: FoV transition event.
# Computational and Communication Infrastructure Challenges for Resilient Cloud Services
## 3 Resilience Challenges
### Computational Infrastructure  
#### 3.2.1 Performance Isolation  
Technologies should be used that provide the necessary mechanisms to package microservices and mitigate the performance degradation resulting from their concurrent execution on shared computational resources. In view of this, developers should use virtualization solutions or software containers. Candidate technologies for microservices packaging include Docker (software containers) [41], Linux LXD Containers (Containers for OS Partitioning) [42], Singularity Containers (containers for scientific workflows) [43], Shifter Containers (containers for HPC applications) [44], and KVM or Xen Virtual Machines (lightweight virtual machines) [45; 46].
# Efficient Removal of Lead Ions from Water by a Low-Cost Alginate-Melamine Hybrid Sorbent
###### Abstract  
A low-cost alginate-melamine hybrid sorbent (named as Alg-Mel) was designed and synthesized for the removal of Pb\({}^{2+}\) from water. The as-prepared Alg-Mel sorbent exhibited high affinity and selectivity to Pb\({}^{2+}\). The selectivity coefficients of the Alg-Mel for Pb\({}^{2+}\)/Cd\({}^{2+}\), Pb\({}^{2+}\)/Cu\({}^{2+}\), Pb\({}^{2+}\)/Cr\({}^{3+}\) and Pb\({}^{2+}\)/Co\({}^{2+}\) were all over 7. It is found that the hybrid sorbent could uptake 95.4% of Pb\({}^{2+}\) from Pb\({}^{2+}\)-containing solutions (0.48 mM), and the maximum adsorption capacity for Pb\({}^{2+}\) reaches 1.39 mmol/g (287.7 mg/g), which is much higher than that of most reported lead ion-sorbents. Furthermore, the Alg-Mel can be regenerated by a simple acid-washing process and used repeatedly. The results of adsorption mechanism analysis reveal that the adsorption of Pb\({}^{2+}\) by Alg-Mel is mainly ascribed to the chemical coordination and ion exchange effects.  
adsorption; alginate; adsorbent; lead ion; wastewater +
Footnote †: journal: Applied
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 2 Related Work
### Landmark Recognition  
Our research aims to detect points of interest, also known as landmarks, such as buildings and monuments. This problem has been extensively studied, focusing on landmark retrieval and recognition, where either similar images to a query image are retrieved, or the depicted landmark is labeled with its name.  
Early approaches relied on handcrafted global image descriptors, as noted by [PERSON] et al. [6]. However, these methods lack robustness against variations, such as illumination and perspective changes. Local feature descriptors, such as the scale-invariant feature transform (SIFT) [7], later became the prominent method due to their ability to better address these challenges. These features can be used for local feature matching [7; 8] or combined into a single representation for fast, large-scale retrieval [9; 10]. Other approaches use a combination of both methods, combining the advantages of fast matching and geometric verification [11; 12].  
In recent years, convolutional neural networks (CNNs) have become the dominant approach in landmark recognition. Embeddings of CNNs trained with classification or similarity losses are used as performant global feature descriptors [13; 14], while CNNsare also employed to produce local features and match images based on them [15; 16]. Recognizing the strengths and weaknesses of both global and local features, [PERSON] et al. have employed a combination of both to achieve state-of-the-art performance [17].  
Although there is extensive related work on landmark recognition and retrieval, the main focus has been on recognizing the image as a whole, without detecting the exact location of the landmark. However, for certain use cases, knowing the precise location is important. One potential solution is to utilize the spatial information of local feature descriptors to extract an estimated position of the landmark, assuming that the most important features will be located on the landmark itself. Another option is to reuse the regional information that some of the related works incorporate. For example, [PERSON] et al. [18] employ a two-stage approach where a landmark object detector first proposes regions of interest. These regions are then used to determine local features, which are aggregated to improve image representation. While these regions are currently not used to provide the location of the landmark, they could be employed for this purpose. Similarly, [PERSON] et al. [19] use a retrained BING objectness detector [20] to propose salient regions, which are used to enhance landmark recognition but not to find the exact location of the landmark in the image.  
Our research also integrates saliency information in combination with Segment Anything [21] to propose potential landmark regions. However, this is performed not only to improve the recognition but also to provide an accurate location of the landmark within the image.
# The Availability of Space Service for Inter-Satellite Links in Navigation Constellations
## 4 Numerical Simulation
### Only Some of the Satellites Are Scheduled to Serve for Multiple Targets (SSMT)  
Based on the analysis of the SSST and ASMT cases, we can easily determine the availability when either six or 12 satellites are scheduled, as shown in Figure 11.  
The results in Figure 11 show that the availability decreases as the number of satellite resources decreases and the number of users increases. The number of users should be no more than eight for strong single-fold coverage when the number of scheduled satellites is 12, and the number of users should drop to three when the locations are higher than 50,000 km. In order to receive four-fold signals, the number of users must be limited to two and they must be within 40,000 km. Only single-fold coverage is possible when there are six satellites scheduled to serve users and, in order to achieve 95% availability, the number of users must be smaller than three or altitudes that are lower than 15,000 km if the users are four.  
Figure 10: The available probability of ASMT (the worst user): (**a**) one or more; (**b**) four or more.  
Figure 9: Availability of multiple users (the worst user).
# Free Space Optical Communication Networking Technology Based on a Laser Relay Station
## 3 Experiment and Analysis
In the case of a harsh communication environment, although the bit error rate occurs in the demodulation method using ADC sampling, the data can still be transmitted, and the FPGA direct demodulation method cannot transmit data. Therefore, this experiment proves that demodulation using ADC sampling can increase the stability of the communication system, and further proves that the scheme proposed by us using pulse width modulation technology to implement a point-to-multipoint free-space optical communication system is feasible. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{Method of demodulation} & \multicolumn{8}{c}{**Bit Error Rate (\%)**} \\ \cline{2-9}  & 2.055 V & 1.914 V & 1.859 V & 1.716 V & 1.661 V & 1.585 V & 1.544 V & 1.508 V \\ \hline ADC demodulation & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \hline Direct demodulation & 0 & 0 & 0 & 0 & 0 & 2.75920 & 3.39679 & 98.95840 \\ \hline \multicolumn{9}{c}{**Bit Error Rate (\%)**} \\ \cline{2-9}  & 1.413 V & 1.364 V & 1.311 V & 1.287 V & 1.233 V & 1.197 V & 1.007 V & 0.965 V \\ \hline ADC demodulation & 0.001605 & 16.64621 & 29.18241 & 38.39984 & 44.65121 & 99.53920 & 100 & 100 \\ \hline Direct demodulation & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bit error rate corresponding to different pulse amplitudes at the receiver. Figure 14: Picture transmission test chart for changing reception angle.
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 4 Experimental Design
### Datasets  
We created two comprehensive datasets from employee data that span from May 2017 to December 2021, covering a total of 3061 employees. Each dataset is tailored to a specific predictive task: one focuses on forecasting vacation leave, while the other targets predicting sick leave. The datasets were constructed using the sliding window  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Topic** & **Question** & **Description** \\ \hline Q1 & **Short-term predictive performance** & How well can we predict employee absences for the upcoming week using data up to one week prior, using a single-target prediction model? & Evaluates the effectiveness of single-target models in predicting immediate future absences, crucial for short-term staffing decisions. \\ \hline Q2 & **Medium-term predictive performance** & What is the predictive performance of models for employee absences two weeks in advance using multi-target prediction? & Assesses the utility of multi-target predictions for medium-term resource allocation and planning, capturing dependencies between consecutive weeks. \\ \hline Q3 & **Long-term predictive performance** & How effectively can our multi-target prediction models predict absences one month ahead (represented as four weeks ahead learning problem)? \\ \hline Q4 & **Comparison of prediction approaches** & Which yields better results in terms of predictive performance for one-month ahead predictions: single-target or multi-target prediction models? & Compares single-target and multi-target models to identify the optimal approach for long-term prediction. \\ \hline Q5 & **Model comparison across time frames** & Does the use of ensemble methods improve predictive performance consistently across all three forecasting periods compared to a baseline single model? & Examines whether ensemble methods, which combine multiple model predictions, offer superior accuracy and robustness compared to single models. \\ \hline Q6 & **Influence of predictive attributes** & Which attributes exert the most significant influence on predicting employee absences for one week, two weeks, and one month ahead for both vacation leave prediction and sickleave prediction? \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of research questions.  
methodology, which is detailed in Section 3.3. The attributes included in the data sets, as described in Section 3.2, were carefully engineered to improve the predictive accuracy of our models. By applying the sliding window technique over the entire period, we accumulated 312,453 data examples in each dataset, with each employee being represented multiple times. This robust approach captures temporal patterns and nuances in employee absences.
## 5 Wind Pressure Field Prediction
### Correlation Analysis  
It should be noted that the wind pressure coherence functions of predicted points obtained based on different interpolation methods are all close to 1 in the low-frequency region and have large discreteness in the high-frequency region, the difference in prediction accuracy based on different interpolation methods is difficult to reflect. Therefore, the correlation coefficient is used in this section to describe the correlation between the wind  
Figure 9: Wind pressure power spectral density of predicted points: (**a**) predicted point C2; (**b**) predicted point E2; (**c**) predicted point F2; (**d**) predicted point K2.  
pressure time history predicted based on different interpolation methods and the test results. The results are shown in Table 7.  
As can be seen from the above table, from the perspective of correlation, the bilinear method and the bicubic method have relatively higher accuracy, and they are still reliable interpolation methods. In the four selected zones, the accuracy results are similar to the analysis results of the previous two sections. The overall error of the windward folded plate (C2) is the smallest, and the correlation coefficients are all above 0.9; the relative error of the side folded plate (E2) is relatively large.
## 2 Mexico's Energy System: Data and Indicators
Mexico lags far behind other countries in deploying renewable sources to produce clean and green energy. More than 80 per cent of the country's energy production depends on hydrocarbons [20], which are mostly imported, thus jeopardising Mexico's energy sovereignty [41]. In 2019, according to the Secretaria de Energia [42], Mexico had 80 gigawatts (GW) of installed generation capacity, including fossil fuel (66%) and hydroelectricity (15%). Fossil resources provide 92 per cent of primary energy in Mexico. Figure 1 shows that in 2019 around 43 per cent of Mexico's primary energy came from oil, 42 from natural gas and 7 from coal. Hydroelectric plants contribute 3 per cent of total primary energy, renewables 4 and nuclear 1. In 2020, Mexico's total power generation was 317,200 GWh [40,42], 80 per cent of which was generated by fossil fuels (combined cycle, thermoelectric, and coal-fired). Hydroelectric, wind, geothermal, and photovoltaic sources accounted for 7, 5, 2, and 3 per cent, respectively. That same year, Mexico's total power capacity was 87.89 GW--around 67 per cent from fossil fuels (combined cycle, thermoelectric and coal-fired), with hydroelectric, wind, geothermal, and photovoltaic capacities 15, 8, 1, and 7 per cent, respectively (Table 1, Figure 2). SENER [43,44] predicted that by the first quarter of 2021, 31 per cent of Mexico's installed capacity would come from renewable energies produced and consumed in Mexico. That share is increasing as more people use electricity generated by solar, wind, and geothermal sources [39]. In 2019, power plants using fossil fuels provided 73 per cent of Mexico's electricity [45]. Over the past decade, with natural gas becoming less expensive  
Figure 1: Mexico’s energy flow and final consumption (2019). Source: Adapted by the authors using SENER data [40,42]. and more available, it has been increasingly used to generate electricity. Renewable energy provides just 10 per cent of the country's electricity [20]. Crude oil is Mexico's dominant energy supply source. In 2019, its total proved oil reserves were 5796 million barrels, 0.3 per cent of the world's total. The ratio of proved reserves/oil production (R/P) is 8.3. However, oil production has fallen by an average 4.6 per cent annually due to maturing oil fields and delayed investments in new fields. Oil consumption decreased from 92.7 in 2007 to 74.9 Mt in 2019. Transportation represents around 61 per cent of demand, followed by power plants (12%) and industry (11%) [46]. This brief overview of Mexico's energy system shows that it can achieve its modernisation goals through domestic energy sources. Mexico's demographic and economic conditions are also generally favourable for transitioning towards sustainable energy. To make the most of its potential, however, Mexico must escape carbon lock-in.
## 3 Problem Formulation
### User Association Algorithm Based on the Cross-Entropy Method (UACE)  
By Algorithm 1, we obtain the optimal content placement strategy of each BS under the \(N\)-Best user association scheme. However, the \(N\)-Best user association scheme does not take into account load balancing and interference management. Under the obtained content placement result, we can further optimize the user association algorithm.  
The user association problem is a constrained non-convex integer programming problem, which can also be solved by the CE method [15; 19]. Considering that the maximum number of BSs associated with a UE is \(N\), the amount of association strategies of a UE will be no more than \(2^{N}-1\). Similarly, the steps of the proposed UACE method is as follows.  
**STEP 1: (Encode Strategy Space)**. In UACE, the decision-making entities are UEs in the network. Suppose that each UE \(m\) can make a decision out of \(N_{m}\) possible strategies, then the strategy set at UE \(m\) can be expressed as \(\mathbf{S}_{m}=[S_{m}^{1},S_{m}^{2},\cdots,S_{m}^{N_{m}}]\). Let \(\mathbf{P_{m}}=[P_{b,1},P_{b,2},\cdots,P_{b,N_{m}}]\) denote the probability distribution of sample strategies at UE \(m\), where \(P_{m,i}\) denote the probability of strategy \(i\) at a specific UE \(m\). \(P_{m,i}\) can be initialized to be equal as follows,  
\[P_{m,i}=\frac{1}{N_{m}},\text{ and }\sum_{i=1}^{N_{m}}P_{m,i}=1,(\forall i=1, \cdots,N_{m}). \tag{22}\]  
**STEP 2: (Generate Samples According to the Probability)**. Samples are generated in this step in a similar way described in STEP 2 of Section 3.2.  
**STEP 3: (Encode Strategy Space)**. The fitness value of strategy samples in UACE is  
\[\sum_{m\in M^{\Omega}}\log_{10}(R_{m})-\lambda\frac{\sum_{m\in M^{\Omega}}V_{ m}}{|M^{\Omega}|} \tag{23}\]  
**STEP 4 (Probability Updating)** and **STEP 5 (Convergence Conditions)** of UACE are also similar to STEP 4 and STEP 5 in Section 3.2. Then the proposed UACE algorithm can be depicted as in Algorithm 2.  
By applying Algorithms 1 and 2, we can obtain suboptimal solutions to problem (15). With the obtained results, optimized performance of the considered UDN in terms of both throughput and backhaul load is achieved.  
```
1:Execute the proposed CPCE Algorithm 1 under popular contents' statistics.
2:Map UEs \(\longleftrightarrow\) Decision-making entities in CE method.
3:Map association strategy set for a specific UE \(\longleftrightarrow\) Strategies in CE method.
4:Map network throughput of all the cell edge UEs \(\longleftrightarrow\) Fitness value in CE method.
5:Execute UACE.
6:Map the obtain solution into optimal user association solution and output X.
```  
**Algorithm 2** User Association based on Cross-Entropy Algorithm (UACE)
## 5 Results and Discussion
### Sensitivity Analysis  
The estimates presented in Section 5 were produced for fixed ensemble size for the importance sampling, and fixed time step size and mesh density for the FE model. These parameters influence the quality of the numerical model and the estimation procedure while also impacting the computational load. The estimates presented in the previous sections, for example, took 140 h for 16 parallel threads on a AMD Ryzen Threadripper 1950X with 16 cores at 3.4 GHz and 128 GB of RAM to run the importance sampling for all 50,000 samples. As such, there is motivation to use a coarser model (larger/fewer elements and larger time step) and fewer samples to decrease the computational load. To ensure that the estimates presented in this work are not conditional on the numerical parameters being used in the model we explore the sensitivity of the results to them in this section. We use the case without medial compression, again, as the exemplar.
## 3 Online Root Fault Identification Method
### Feature Vector Extraction
#### 3.1.4 Representing Data by Vectors  
The occurrence of an alarm can be recorded as a binary value. If the alarm is present, the value is equal to one; if the alarm is not present, the value is equal to zero. The binary value is expressed as:  
\[v_{i}=\left\{\begin{array}{ll}1,&\text{if alarm $i$ is present}\\ 0,&\text{if alarm $i$ is not present}\end{array}\right., \tag{1}\]  
where \(i=1,2,\ldots,N\) and \(N\) is the total number of alarm types configured in the SCADA system.  
A list of alarms may be represented either by a sequence of alarms or by a vector of alarms. In a sequence, the alarms are ordered by their time of appearance. In a vector, their time of appearance is not considered. Only the fact that the alarms are present is considered. In this paper, an alarm list is represented by a vector. The \(j\)-th alarm vector is expressed as:  
\[V^{j}=\left[v_{1}^{j},v_{2}^{j},\ldots,v_{i}^{j},\ldots,v_{N}^{j}\right]^{ \mathsf{T}}, \tag{2}\]  
where \(v_{i}^{j}\) is the binary value of alarm \(i\) in the \(j\)-th alarm vector; \(j=1,2,\ldots,M\); \(M\) is the total number of alarm vectors. It should be noted that each alarm has two records: one record represents the start of the alarm; another record represents the end of the alarm. In an alarm vector, as long as one alarm occurs, the binary value of the alarm is set to one.  
Figure 2: The match criterion of alarm lists and maintenance records.  
Fault \(k\) in the \(r\)-th maintenance record is expressed as:  
\[f_{k}^{r}(r=1,2,\ldots,Q,k=1,2,\ldots,P), \tag{3}\]  
where \(Q\) is the total number of maintenance records and \(P\) is the number of fault types. Because the same fault can happen multiple times, \(P\) is smaller than \(Q\). All the faults in records are expressed as:  
\[F=\left\{f_{1}^{1},f_{1}^{2},\ldots,f_{1}^{l_{1}},\ldots,f_{k}^{1},f_{k}^{2} \ldots,f_{k}^{l_{k}},\ldots,f_{P}^{l_{P}}\right\}, \tag{4}\]  
where \(f_{k}^{l_{k}}\) is fault \(k\) in the \(l_{k}-\)th maintenance record; \(l_{k}\) is the number of records belonging to fault \(k\); and \(l_{P}\) is the number of records belonging to fault \(P\). Thus, the set of records belonging to fault \(k\) is expressed as:  
\[F_{k}=\left\{f_{k}^{1},f_{k}^{2},\ldots,f_{k}^{l_{k}}\right\}, \tag{5}\]  
where \(f_{k}^{l_{k}}\) is fault \(k\) in the \(l_{k}-\)th maintenance record; \(l_{k}\) is the number of records belonging to fault \(k\); \(k=1,2,\ldots,P\). Thus, \(\sum\limits_{k=1}^{P}l^{k}=Q\).  
After the match process, the alarm vectors are labeled with their root faults. The pairs of alarm vectors and their root faults are expressed as:  
\[\left\{(V_{k}^{1},f_{k}^{1}),(V_{k}^{2},f_{k}^{2}),\ldots,(V_{k}^{l_{k}},f_{k} ^{l_{k}})\right\}, \tag{6}\]  
where \(V_{k}^{l_{k}}\) is the \(l_{k}-\)th alarm vector of fault \(k\); \(f_{k}^{l_{k}}\) is fault \(k\) in the \(l_{k}-\)th maintenance record; \(k=1,2,\ldots,P\).