esa-sceva/satcom-chunk-collection huggingface dataset:
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## 4 QAOA Mappings: Strings
### Example: MaxIndependentSet
#### 4.2.1 Partial Mixing Operator at Each Vertex  
Given an independent set \(V^{\prime}\), we can add a vertex \(w\
otin V^{\prime}\) to \(V^{\prime}\) while maintaining feasibility only if none of its neighboring vertices \(\mathrm{nbhd}(w)\) are already in \(V^{\prime}\). On the other hand, we can always removeany vertex \(w\in V^{\prime}\) without affecting feasibility. Hence, a bit-flip operation at a vertex, controlled by its neighbors (adjacent vertices), suffices both to remove and add vertices while maintaining the independence property. These classical moves inspire the controlled-bit-flip partial mixing operators.  
In general, for a string \(\mathbf{y}\) and a set of indices \(V\), let \(\mathbf{y}_{V}=(y_{v})_{i=1}^{|V|}\) be the substring of \(\mathbf{y}\) in lexicographical order of the indices. In particular, let \(\mathbf{x}_{\text{nbhd}(v)}=(x_{w})_{w\in\text{nbhd}(v)}\). (The ordering of the characters within the substring is arbitrary, because we only use this as the argument to predicates that are symmetric under permutation of the arguments.) For each vertex, we defined the partial mixer as a multiply-controlled \(X\) operator:  
\[\begin{split} H_{\text{CX},v}&=X_{v}H_{\text{NOR} (x_{\text{nbhd}(v)})}\\ &=2^{-D_{v}}X_{v}\prod_{w\in\text{nbhd}(v)}(I+Z_{w}),\end{split} \tag{23}\]  
with corresponding partial mixing unitary, a multiply-controlled-\(X(\beta)\) single-qubit rotation:  
\[\begin{split} U_{\text{CX},v}(\beta)&=e^{-i\beta H _{\text{CX},v}}\\ &=\Lambda_{\text{NOR}(x_{\text{nbhd}(v)})}\left(e^{-i\beta X_{v }}\right)\\ &=\Lambda_{\text{NOR}(x_{\text{nbhd}(v)})}\left(X_{v}(\beta) \right),\end{split} \tag{24}\]  
where \(X_{v}(\beta)\) is the single-qubit operator \(X_{v}(\beta)=e^{-i\beta X_{v}}\). Since \(X_{v}\) is both Hermitian and unitary, \(e^{-i\beta X_{v}}\) is a linear combination of the identity and \(X_{v}\) for \(0<\beta<\pi/2\).
## 3 Results and Discussion  
### Chemical Composition  
#### Chemical Composition of Apilarnil  
Apilarnil is a product of yellowish color, milky consistency, and sour taste, obtained after triturating 7-day-old drone larvae collected together with the cell contents [38]. The composition of the apilarnil is variable and depends on the age of the larvae, the food sources to which the bees have access, and the beekeeping season.  
The fresh apilarnic we analyzed had a water content of 68.54%, acidity 26.62 mg/g, and pH 5.74 (Table 3). The commercial samples had an impurity percentage of 5.47% and those harvested from the apiary 15.28%, with statistically significant differences (\(p<0.05\)) between the two sources. Large variations in protein content were observed in the samples of fresh commercial apilarnil (22.04 mg/g) and those harvested from the apiary (44.86 mg/g). The ash content of the samples analyzed was 0.88%, with maximum values observed in the commercial samples (1.45 mg/g and 1.89 mg/g respectively). The main macroelement found was phosphorus 1233.00 mg, followed by potassium 787.65 mg, magnesium 369.93 mg, calcium 284.23 mg, and sodium 144.02 mg. The best represented microelement was iron 12.31 mg, followed by zinc 8.66 mg, chromium 3.29 mg, manganese 4.71 mg, and copper 3.71 mg.  
Freeze-dried apilarnil had a water content of 6.78%; protein was represented at 117.25 mg/g and ash 1.89%. The weight of macro- and microelements was close to that observed in fresh apilarnil (Table 3).  
Of the contaminants analyzed, nickel and lead were not detected in any sample of fresh or freeze-dried apilarnil, and cadmium was identified in the range of 1.34 ppm, with higher values in commercial samples (Table 3).  
The results obtained fall within the range of data presented in the specialized literature; the differences may be the result of geographical and meteorological conditions, the beekeeping season in which the harvesting was completed, food sources, etc. The research carried out by [PERSON] et al. (2018) highlighted the following chemical composition for apilarnil: water 73.25%, total protein 9.47%, lipids 8.38%; while the royal jelly analyzed had a water content of 66.03%, total protein 11.14%, and lipids 3.96%.  
[PERSON] et al. (2019) reported the nutritional profile of fresh apilarnil from Apis mellifera pupae drones aged 21-24 days as moisture 74.23 g/100 g, protein 11.05 g/100 g, fat 8.19 g/100 g, ash 0.85 g/100 g, and for dry apilarnil obtained from drone pupae aged 16-20 days ([PERSON] et al., 2019) moisture 1.69 g/100 g, protein 48.52 g/100 g, fat 23.41 g/100 g, and ash 4.05 g/100 g. The protein level of bee brood (pupae and larvae) evaluated by [PERSON] et al. (2018) was 46.4-46.73 g/100 g and the fat level 18.84-20.75 g/100 g.
## 2 Related Work  
### Insertion Loss  
The worst-case insertion loss is the dominant factor in determining the laser source output power to ensure reliable communication in the HONoC. The two major causes of insertion loss due to MR drop (Figure 1a) and waveguide crossings (Figure 1b) were reported to be 0.5-1.5 dB and 0.04-0.12 dB, respectively [9; 10; 11; 12; 13].  
The received signal power should be higher than the sensitivity of the photodetector. Therefore, the relationship between the laser source power (\(P_{laser}\)), worst-case insertion loss (\(P_{worst\_IL}\)) and sensitivity of the photodetector (\(P_{sensitivity}\)) can be expressed as  
Figure 1: Two major insertion losses in optical network-on-chip (ONoC): (**a**) Microring (MR) drop loss; and (**b**) waveguide crossing loss.  
\[P_{laser}\geq P_{worst_{IL}}+P_{sensitivity}, \tag{1}\]  
where \(P_{sensitivity}\) has been reported as approximately \(-20\) dBm [14].  
Other types of optical signal power losses considered at the system-level analysis are as follows:  
1. The waveguide propagation loss is \(-0.274\) dB/cm [15].
2. The MR through loss is smaller than the MR drop loss by two orders of magnitude [16].  
Given that these factors are minor in comparison to major insertion losses, most on-chip network architecture studies focus on reducing the insertion losses caused by MR drops and waveguide crossings.
## 2 Materials and Methods
### Experimental Setup and Measurements
The PEMGRAPH system is a highly sensitive and high-resolution open dual-head system [38] that consists of two planar \(17.5\times 21.3\) cm\({}^{2}\) detector heads, each composed of four modules. There are a total of eight modules implemented in this device. The gap between each module was 0.6 cm. Each module was composed of 20 \(\times\) 64 crystal array of Pr:LuAG with dimensions of 0.21 \(\times\) 0.21 \(\times\) 1.5 cm\({}^{3}\) and was optically isolated by a BaSO\({}_{4}\) reflector with a thickness of 0.1 cm. This adds up to a total of 20 \(\times\) 64 \(\times\) 8 = 10,240 Pr:LuAG crystal segments. Each side of the crystal matrix was optically coupled to three units of a 5.2 \(\times\) 5.2 cm\({}^{2}\) Hamamatsu H8500 position-sensitive photomultiplier tube (Hamamatsu Photonics Co., Ltd., Hamamatsu, Japan). The spatial resolution was 0.2 cm (at full width at half maximum (FWHM)) in the plane parallel to the detectors [38]. The acquired list mode data were reconstructed to 77 dynamic frames using the 3D iterative maximum likelihood-expectation maximization (MLEM) method [43,44]. The field of view (FOV) of each reconstructed image was 20.35 \(\times\) 14.19 \(\times\) 14.6 cm\({}^{3}\), and the matrix size was 185 \(\times\) 129 \(\times\) 73. Therefore, the voxel size of each reconstructed image was 0.11 \(\times\) 0.11 \(\times\) 0.2 cm\({}^{3}\). The experimental data were then analyzed using our previously developed PyBLD software [45] (link: [[http://www.rim.cyric.tohoku.ac.jp/software/pybld/pybld.html](http://www.rim.cyric.tohoku.ac.jp/software/pybld/pybld.html)]([http://www.rim.cyric.tohoku.ac.jp/software/pybld/pybld.html](http://www.rim.cyric.tohoku.ac.jp/software/pybld/pybld.html)) (accessed on: 11 January 2022)). AMIDE (A Medical Image Data Examiner) version 1.0.4 software was used to analyze the images (link: [[http://amide.sourceforge.net](http://amide.sourceforge.net)]([http://amide.sourceforge.net](http://amide.sourceforge.net)) (accessed on: 11 January 2022)) [46]. The one-dimensional (ID), two-dimensional (2D) and three-dimensional (3D) time-course activities for 15, 20, 30 and 55 min after irradiation were obtained, which provided information on the location and distribution of activity.
## 4 Policy Implementation
### Financial Instruments  
Financial policy instruments include subsidies, taxation, and direct interventions. In 2010, \(\copyright\)43 billion will go, for example, to farmers in the European Union as direct income support. Farmers are eligible to these subsidies if they maintain their land in good agricultural and environmental condition, and comply with standards on public, plant and animal health, and animal welfare. Payment eligibility is the responsibility of the EU member states, for which each country needs an Integrated Administration and Control System, including a land Parcel Identification System (LPIS). The LPIS provides a unique identifier to each land parcel and its owner (the claimant), and is used to register applications for the subsidies and payments made. The development, update, and revision of the LPIS, which can be based on existing maps and documents from national land registers, is increasingly based on high resolution aerial photography and satellite imagery.  
The use of EO in beach nourishment [32] is another example of remote sensing supporting the implementation of a direct financial intervention in the Netherlands [33]. Beach nourishment aims to restore the coastal protection offered by dunes and beaches weakened by shore erosion. Airborne laser altimetry is used to estimate the volume of sand required for beach nourishment by measuring theelevation of the dry parts of the beach; sonar is used for the underwater parts (Figure 4). This information is used to direct the companies contracted to nourish the beaches. Maintenance of coastal defence lines is a task of the central government (Ministry of Public Works), which uses a financial instrument to achieve its policy objective of improving coastal defence through beach nourishment. EO contributes to the efficacy and efficiency of achieving this policy, while assessing the volume of sand required.  
Financial instruments also trigger the use of EO in the context of the Kyoto protocol, which requires reliable and transparent approaches for the estimation of carbon stocks and for the emission and removal of greenhouse gases from agriculture, forestry, and other land uses. The use of remote sensing has been advised to support the accounting of carbon credits, while assessing carbon stocks, carbon sequestration by vegetation [34, 35], and the implementation of payments under the REDD+ (Reduction of Emissions from Deforestation and Forest Degradation) policy [36], which is currently under discussion.
# Pilot-Scale Fenton-like System for Wastewater Treatment Using Iron Mud Carbon Catalyst
###### Abstract  
Fenton oxidation can contribute to meeting effluent standards for COD in actual wastewater treatment plant effluents. However, Fenton oxidation is prone to produce iron sludge waste. The application of heterogeneous Fenton-like systems based on Fenton iron mud carbon in wastewater treatment plants is essential for Fenton iron mud reduction and recycling. In this study, a Fenton iron mud carbon catalyst/Ferrate salts/H\({}_{2}\)O\({}_{2}\) (FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\)) system was developed to remove chemical oxygen demand (COD) from secondary effluents at the pilot scale. The results showed that the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system exhibited excellent COD removal performance with a removal rate of 57% under slightly neutral conditions in laboratory experiments. In addition, the effluent COD was stabilized below 40 mg\(\cdot\)L\({}^{-1}\) for 65 days at the pilot scale. Fe(IV) and \({}^{1}\)O\({}_{2}\) were confirmed to be the main active species in the degradation process through electron paramagnetic resonance (EPR) and quenching experiments. C=O, O-C=O, N sites and Fe\({}^{0}\) were responsible for the generation of Fe(IV) and \({}^{1}\)O\({}_{2}\) in the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system. Furthermore, the cost per ton of water treated by the pilot-scale FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system was calculated to be only 0.6209 USD/t, further confirming the application potential of the FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) system. This study promotes the engineering application of heterogeneous Fenton-like systems for water treatment.  
pilot scale; Fenton iron sludge; bis(oxidant); chemical oxygen demand Article
# Comparative Analysis of Skew-Join Strategies for Large-Scale Datasets with MapReduce and Spark
## 3 Data-Skew-Handling Algorithms
### Multi-Dimensional Range Partition
#### 3.4.4 Multi-Dimensional Range Partition  
The MDRP algorithm is shown in Algorithm 5. The \"fragment-replicate\" technique is used for the join operation. Assume that a mapper receives a record \(r3\) with the join key \(r3.k=2\) of dataset \(R\); then, this record will belong to a cell \((R_{2},L_{2})\). However, this cell is divided into three partitions \(P_{2}\), \(P_{3}\), and \(P_{4}\), since \((R_{2},L_{2},4)\in C_{2}\), \((R_{2},L_{2},4)\in C_{3}\), and \((R_{2},L_{2},4)\in C_{4}\). So, we should use a fragment or a replicate for record \(r3\). By counting the number of records in \(R_{2},L_{2}\) with join key \(k=2\), if the number of records of for dataset \(R\) is greater than that for dataset \(L\), then \(r3\) will be fragmented (randomly selected partition), and the records in dataset \(L\) will be replicated (replicated to both partitions).  
```
0: an input tuple \(r\in R\)
0: a partitioning matrix \(M\) Begin
1: listCell = M.listCell(r.k)
2:for each cell \(c\) in listCell do
3: P = c.listPartition(0)
4:if M.fragment(r.k) == true then
5: i = random() % \(|P|\)
6: Output (P[i], r)
7:else
8:for i = 1 to \(|P|\)do
9: Output (P[i], r)
10:endfor
11:endif
12:endfor
```  
**Algorithm 5** Multi-dimensional range partition algorithm
# Accurate Extraction of Cableways Based on the LS-PCA Combination Analysis Method
## 2 Methodology  
### Overview  
There are difficult problems associated with the observation and maintenance of cableways in ski resorts on a daily basis. Considering the above situation, we propose a high-precision segmentation and extraction method that provides convenience for the daily maintenance of the cableway. The raw point cloud is segmented based on an elevation threshold into upper and lower parts (Section 2.2.1).  
Based on the spatial geometric distribution characteristics of the point clouds, the upper part of the point cloud is further segmented to complete the preliminary segmentation of the cables (Section 2.2.2). Finally, we use a combination of least squares and the principal component analysis to complete the final cable extraction (Section 2.3). The full pipeline is illustrated in Figure 2.
# Granular Activated Carbon from Grape Seeds Hydrothermal Char
###### Abstract  
A two-stage conversion process for the production of a valuable product from biomass waste, i.e., grape seeds activated carbon (GSAC) was investigated. Such process involved hydrothermal carbonization (HTC) of grape seeds, followed by chemical activation with potassium hydroxide (KOH). Different HTC temperatures (T\({}_{\rm HTC}\) = 180-250 \({}^{\circ}\)C), as well as different KOH:hydrochar ratios (R = 0.25:1-1:1), were explored. The samples that were obtained from both stages of the biomass conversion process were analyzed in terms of textural characterization (apparent total and micro-pore surface areas, total and micro-pore volumes, pore size distribution), proximate and ultimate compositions, thermal stability, surface morphology (via SEM), and surface chemistry characterization (via FTIR). Overall yields of approximately 35% were achieved, which are comparable to those obtained with the state-of-art one-stage process. In a wide range of operating conditions, the higher T\({}_{\rm HTC}\) and R, the higher was the surface area of the GSAC, which was maximal (above 1000 m\({}^{2}\)/g) for T\({}_{\rm HTC}\) = 250 \({}^{\circ}\)C and R = 0.5. At such optimal conditions, around 90% of the total porosity was due to micro-pores. Such a trend was not fulfilled at the most severe operating conditions (T\({}_{\rm HTC}\) = 250 \({}^{\circ}\)C; R = 1), which resulted in larger pore size, causing surface area reduction. A proper selection of the process parameters of both the process stages gives great opportunities of tuning and optimizing the overall process. The produced GSACs showed a remarkable thermal stability, and their surface appeared rather free of functional groups.  
hydrothermal carbonization; HTC; chemical activation; grape seeds activated carbon (GSAC); biomass; agro-industrial waste valorization +
Footnote †: journal: Applied Physics Letters B
### Background of Cache-Enabled UAV-Assisted Network  
Wireless caching has turned out to be an effective solution to meet the demand for low latency data traffic [6; 7; 8]. Studies have shown that in cellular networks, there is usually some popular content that is repeatedly requested by users [6]. This popular content would be requested and transferred multiple times in a period of time, which would result in a decrease in the data-transferring efficiency. In order to save on network resources, forwarding caching of some popular content at edge nodes is regarded as a promising solution. During idle periods of the network, edge nodes could cache popular content from a macro base station (MBS) via backhaul networks. Once the cached content is requested, it can be delivered directly to the user through edge nodes, and the user would enjoy low communication latency.  
By employing cache-enabled UAVs as edge nodes, a wireless network could provide high-quality, high-efficiency and on-demand wireless coverage [9; 10; 11; 12; 13]. Consider UAV-assisted communication systems with cache-enabled multi-antenna UAVs; there are mainly three key issues in improving the system performance: UAV deployment, beamforming scheme and caching strategy. First, it is easy to understand that the locations of UAVs determine the distances from the served users, thus affecting the channel quality [14; 15; 16; 17; 18]. Second, multiple antennae enable UAVs to utilize beamforming to obtain higher SINRs; accordingly, an appropriate beamforming scheme is required [19; 20; 21; 22]. At the same time, the caching strategy, or content placement, has been extensively researched in traditional cellular networks [6; 7; 8; 19; 20], and it is also important in UAV-assisted networks.
## 3 Results and Discussions
### Effect of Process Parameters on the Characteristics of Microcapsules
#### 3.1.3 Effect of Core-to-Shell Ratio on Shell Structure and Morphology  
The core-shell ratio of microcapsules determines the thickness of the shell, which in turn affects the surface morphology of the microcapsules, such as sphericity and surface smoothness. An appropriate core-shell ratio can ensure that the microcapsule shell forms uniformly, has good sphericity, and has a smooth surface. When the core-shell ratio is too high, the shell material is insufficient, resulting in a thin shell wall that is not stable enough, making it prone to damage or deformation. Some microcapsules may fail to form a complete structure due to insufficient shell material. When the core-shell ratio is too low,  
Figure 3: Particle size distribution of microcapsules at the optimal process parameters (**a**) oil-to-water ratio 7.5:200, (**b**) emulsifier dosage 1 wt%, (**c**) emulsification speed 2500 r/min.  
there is too much shell material, leading to a thick shell wall that may develop wrinkles due to accumulation, resulting in a rough appearance and affecting its performance.  
Through observations using scanning electron microscopy and optical microscopy, it can be found that when the core-shell ratio is 8:1, as shown in Figure 5a, although most microcapsules have formed a complete structure, their sphericity is generally poor, the shell is relatively thin, and its light shielding capability is insufficient. The core material inside the microcapsules can be seen through the light source of the optical microscope, indicating that the core-shell ratio is too large at this time. When the core-to-shell ratio was increased to 8:2, as shown in Figure 5b, all microcapsules had formed complete structures, the shell material thickness increased significantly, and the optical microscope's light source could no longer penetrate the microcapsules. The microcapsules exhibited high sphericity and smooth surfaces, indicating that the core-to-shell ratio was more appropriate. However, when the core-to-shell ratio was further increased to 8:3, as shown in Figure 5c, the sphericity of the microcapsules decreased, their surfaces became rough, and the shell material was unevenly distributed, causing wrinkles due to excessive shell material accumulation. Therefore, the microcapsules with the best shell morphology were obtained at a core-to-shell ratio of 8:2  
Figure 4: Effect of initiator dosage on the shell structure of microcapsules, (**a**) 2 g, (**b**) 2.5 g, (**c**) 3 g.
## 2 Related Work
### Class Imbalances
[68], a solution to class imbalance was proposed using the ADASYN algorithm for oversampling the minority class, and class weights for undersampling the majority class, ensuring a more balanced dataset for training a multi-stage CNN1D deep learning model. In ref. [69], the proposed system was compared with other techniques for addressing class imbalance, such as random over-sampling (ROS), SMOTE, and ADASYN. Benchmarking on the NSL-KDD and BoT-IoT datasets showed that the proposed system performed effectively in detecting minority class instances in binary classification tasks. In ref. [70], SMOTE was applied to address the class imbalance in the UNSW-NB15 dataset, enhancing the classification performance by balancing the distribution of classes. In ref. [71], an ensemble method wasused to tackle class imbalance across multiple datasets, including CICIDS2017, KDD99, and UNSW-NB15, improving the classification performance by effectively handling the under-representation of minority classes. In ref. [72], the performance of DT and RF was enhanced by applying CatBoost alongside random oversampling and undersampling techniques on the CIC-IDS-2018 dataset. In ref. [73], a novel feature selection algorithm, improved non-dominated sorting genetic algorithm III (I-NSGA-III), was proposed to address the imbalance issue, resulting in a better detection rate, though it did not lead to higher accuracy. In ref. [74], random oversampling of the minority classes and random undersampling of the majority class were applied to improve intrusion detection performance. However, random oversampling is known to induce overfitting [75], and only accuracy was reported in this study. A study in ref. [76] evaluated two tree-based classifiers and one deep learning-based classifier under various sampling rates, showing that sampling techniques improve the detection of both majority and minority classes. In ref. [77], [PERSON] et al. proposed a combination of SMOTE with ENN (SMOTE-ENN) and a DNN for the NSL-KDD dataset. In ref. [78], a cost-sensitive deep learning model combined with ensemble techniques was used to tackle class imbalance in intrusion detection. The model enhanced detection of both majority and minority attacks, but required high computational resources and time. In ref. [79], to address class imbalance, the study utilized SMOTE for oversampling the minority class and Tomek Links for undersampling the majority class. This combination of techniques helped balance the dataset before applying an LSTM model for classification, improving the model's ability to detect both majority and minority classes effectively.