esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Implementation Specific Details of the Main Radar Blocks  
### Transmitter Chain Details  
The Gunn oscillator generates the radar waveform, which is transmitted to the environment through the transmitter antenna. The oscillator was recycled from the Cosmic Background Imager (CBI) radio-telescope [30]. The frequency of the oscillator is controlled with electronic resonators composed by a tunable microwave cavity and a YIG sphere. The cavity is set to select the frequency of the carrier wave, and the YIG sphere is used to modulate the output frequency with a voltage input. Adjusting the cavity, 38.6 GHz was the closest stable frequency to the desired 35 GHz.  
A direct digital frequency synthesizer modulates the Gunn oscillator output frequency using a 150 kHz rising sawtooth voltage signal, yielding a non-ambiguous range of 500 m (see Equation (1), with \(F_{d}=B\)). The amplitude of the sawtooth is set to obtain a modulation bandwidth of 10 MHz, and thus producing, at best, a range resolution of 15 m (see Equation (2)).  
The directional coupler after the Gunn oscillator allows the use of the transmitted waveform signal also as a local oscillator (LO) for the receiver mixer. The microwave insulator after the coupler avoids reflections entering back the transmitter antenna.
# EC-Kad: An Efficient Data Redundancy Scheme for Cloud Storage
## 3 Cloud Storage System Model Based on Erasure Code
### Efficient Retrieval of Data Blocks
file is reconstructed by obtaining the minimum required number of data blocks from the same file. If a new node is added during the search process, then the new node generates a new NodeID through the hash function. The new node contacts the known node, obtains its K-Bucket information, and updates its own K-Bucket. The new node takes over the data block closest to its NodeID. If a node failure is detected, then the data block responsible for the failed node is taken over by the node closest to its NodeID, and other nodes update their K-Buckets and remove the failed node. As the elasticity of nodes changes, when adding or reducing storage nodes, only nodes to the order of \(O\) (\(1/n\)) are required to change information. The resource search and localization algorithm for storing a node's encoded data blocks is provided in Algorithm 1. First the \(\alpha\) (default value of three) nearest nodes from the local K-Bucket are selected, and FIND_NODE requests are sent to these nodes to obtain nodes closer to the target key. The above process is repeated until the target node is found or the maximum hop count is reached, and then the search is exited. From the K-Bucket routing structure and search algorithm, it can be seen that each time the nearest node was found, and the distance between the newly found node and the target data block node was usually reduced by half compared with the original. Usually, the nearest node can be searched up to log\(N\) times for successful location, which can accelerate the data block location of storage nodes. ```
Input: file_id, \(k\) (the number of raw data blocks), key Output: data_blocks set (at least \(k\) data blocks for subsequent decoding and recovery of a file) candidates [0] (the node closest to the key)
1. function find_data_blocks(file_id, k)
2. data_blocks \(\leftarrow\) empty set
3. for i from 1 to k + m do
4. key \(\leftarrow\) hash(file_id + i) // Generate data block key
5. node \(\leftarrow\) find_closest_node(key) // Find the node closest to the key
6. if there exists datablock key in the node then
7. data block\(\leftarrow\) Retrieve data block from the node
8. data_blocks.add(data block) // Add the data block in data_blocks set
9. if data_blocks.size >= k then
10. break
11. end if
12. end for
13. return data_blocks // Used for subsequent merging of data blocks to decode and restore the original file operation
14. end function
```  
**Algorithm 1**The Kademlia-based erasure code data block search algorithm.
# Algorithms in Low-Code-No-Code for Research Applications: A Practical Review
## 5 LCNC Used in Existing Research
### Applying NLP Algorithms
#### 5.2.3 NER Algorithm  
Here, NER is an NLP-based information extraction task that seeks to locate and classify named entities mentioned in unstructured text into pre-defined categories, such as locations, person names, organizations, date/time expressions, monetary values, quantities, numerical values, and percentages. Indeed, NER has been applied in almost all domains of research for extracting crucial information from unstructured texts [69; 76]. Previous research has extracted three different categories of entities (i.e., \"Disease or Syndrome\", \"Sign or Symptom\", and \"Pharmacologic Substance\") from health-related tweet messages [76] for discovering public health information and developing just-in-time disease outbreak prediction systems and drug interactions systems. A study in [69] applied basic NLP-based methodologies to extract entities and relationships, as well as to identify sentiment. The keywords investigated within [69] were drug abuse--cannabinoids, buprenorphine, opioids, sedatives, and stimulants. Research in [70] qualitatively evaluated posts about methylphenidate from five French patient web-forums including an analysis of information about misuse or abuse. Data were accumulated from French social networks that cited methylphenidate keywords. Text mining methods, such as NER and topic modeling, were used to analyze the chatter, including the identification of adverse reactions. Previous research in [69; 70; 76], did not use NER as a pre-processor for AI-based algorithms. The NER could be invoked with C# using the Microsoft Cognitive Services Text Analytics application programming interface (API) [75]. The NER algorithms could also be implemented with the drag-and-drop features of Microsoft Power Automate [57], as demonstrated in [15; 18; 19; 20; 21; 22; 23; 24].
# Architectural Design for Digital Twin Networks
## 5 Design and Implementation Strategies
### Interfaces
#### 5.4.2 Application-Facing Interfaces  
These interfaces link the network twin with various applications that interact with the DTN. A lightweight and adaptable interface like Representational State Transfer (REST) is commonly used as the northbound interface.  
Note that user feedback can be collected through the application-facing interfaces. This would enable iterative refinement of the visualization layer based on real-world interaction and evolving user needs.
## 2 Issues in Topology Design
### Architectural Related Considerations  
The architecture of wireless sensor networks needs to accommodate the following three characteristics:  
_Scalability._ Large-scale WSNs rely on thousands of tiny sensors to observe and influence the real world [33]. These sensors do not necessarily need to be active at all times, so sensors can be dynamically added to or removed from the network [34]. A durable and scalable architecture would allow responses to changes in the topology with minimum update messages being transmitted. Another important feature of chain oriented WSNs that affects scalability is the number of nodes in a chain. If there is a single chain in the whole network, the topology is subject to poor scalability. On the other hand, multiple chains in the network can solve the scalability problem. However, all the chains in the network should be of similar length. Therefore, in designing the proposed topology, we keep the lengths of multiple chains similar.  
_Task Orientation._ The tasks of wireless sensor networks range from the simplest data capturing and static-nodes to the most difficult data collecting, mobile-node sensor network [33, 35]. The sensor networks for different tasks may behave very differently. The software structure should be optimised and tailored toward a predefined task-set for each node. Thus, the proposed topology divides all the deployed nodes in the hierarchical structure, assigns specific tasks to each node, and provides a communication abstraction, through which other protocols can be designed.  
_Light Weighting._ The computing and storage capabilities of sensor nodes are very limited. Lightweight operations, such as data aggregation, reduced message size, and a piggyback acknowledgment mechanism, must be applied to the architecture. In designing the communication abstraction of the proposed logical topology, we consider this notion.
# Adaptive Computation Offloading with Task Scheduling Minimizing Reallocation in VANETs
## 5 Vehicular Adaptive Offloading (VAO)
### Operation Overview  
Figure 2 shows the operation of VAO. First, an RV \(v_{r}\) searches for nearby vehicles and collects their information, such as computing power and route, by broadcasting a message. Then \(v_{r}\) assigns computational tasks to nearby vehicles according to the task schedule it makes, based on neighbor vehicle information. After that, it periodically checks whether SVs will be out of its communication range soon, and in that case, unfinished tasks on the SVs are reallocated to other vehicles. This procedure is depicted in Algorithm 1. Before delving into the algorithm, see Table 2 for the notations.  
Figure 2: The operation of the VAO mechanism.  
Algorithm 1 describes the operation of VAO in an RV \(v_{r}\). After initialization, \(v_{r}\) conducts a search broadcasting a vehicle information request (VIRQ) message, and each vehicle receiving this message sends a vehicle information reply (VIRP) with the information on its route and computing resources (Line 6). The searched vehicle information is stored into \(V\) (Line 7), then \(v_{r}\) calls up the task scheduling algorithm to determine \(\chi\) based on the information on the set of the tasks \(T\) and the computing power of vehicles in \(V\) (Line 8). This task scheduling algorithm is described in Algorithm 2 in the next section. Then, \(v_{r}\) transfers tasks in \(T\) to the vehicles in \(V^{\delta}\) according to \(\chi\) (Line 9 to 12). Then, in Line 13, \(v_{r}\) starts to execute the application by transferring input data to the SVs that have entry tasks.  
After completing its allocated tasks, an SV transmits the result to the SVs that have successor tasks. However, in a VANET where vehicles move fast, SVs may frequently leave the communication range of the RV before completing their work, and this can cause a big problem in the CO system because all successor tasks cannot be executed until they receive the results of predecessor tasks. Therefore, we need to reallocate the tasks associated with original SVs that have gone far away without completing the task. If any SV not finishing its assigned tasks has gone too far apart from \(v_{r}\), those tasks are reallocated to other nearby vehicles. This check and reallocation are periodically done until all offloaded tasks are complete. The detailed procedure will be explained in Section 5.3.
# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 4 Ethical Considerations
### Autonomy: The Power to Decide (Whether to Decide)
#### 4.3.2 Misinformation  
Syndromic surveillance systems often generate false detections because it is sometimes difficult to distinguish natural data variations resulting from outlier and dynamic system changes, e.g., with time or space variables, from real outbreaks [73]. Human reviews and follow-up investigations are necessary for verification, which can be costly in time and labour. The prompt identification of novel microbes is often not matched with equally prompt epidemiological assessments [37]. The scarcity of such data, and the lack of transparency in algorithms could compound challenges of misinformation or false alarms that could potentially occur with AI-enabled platforms, especially if they rely on social media or non-authoritative data sources, for example. False detections can erode public trust in these surveillance systems, and such misinformation could weaken an agency's or individual's power to effectively make decisions [74; 75; 76]. The role of social media in trending public attitudes can also be put to effective use to disseminate risk communication in real time [26; 42], but could be a double-edged sword increasingly prone to misinformation and the spread of fake news [74; 75; 76].
## 1 Introduction
The rheological tests can give valuable information about the interactions between grape peels and wheat flour components and gluten matrices, and could differentiate the rheological behavior of supplemented flour dough during mixing, modelling and drying [17]. Frequency sweep tests can provide information about the differences of dough structures between samples with different ingredients added [18]. For this purpose, the deformation frequency is increased progressively at a constant amplitude of the strain. The results obtained at low frequencies led to the behavior of dough at slow changes of stress, while at high frequencies information of dough response to fast load is given [18]. Dough rheology and final product texture could be negatively affected by the addition of fiber-rich ingredients, depending on the amount and particle size. In order to minimize the negative effects of grape peels addition on dough rheological and pasta texture properties due to the gluten dilution, a small particle size could be used since a smaller impact was observed in previous studies [19]. According to the literature, wheat dough with grape peels added presented higher hardness compared to the control [20], with the elastic and viscous moduli being higher compared to the control [17]. The data presented by [PERSON] et al. [14] showed an increase in durum wheat pasta firmness of 30% and an increase in adhesiveness caused by the addition of grape pomace, while uncooked pasta luminosity decreased from 65.95 to 43.55 [14]. In Europe, pasta is usually made from durum semolina, while for Asian noodle manufacturing soft wheat flour is used [21]. Soft wheat pasta is usually processed by sheeting and cutting, while pasta from durum wheat is cold-extruded. Thus, compared to durum wheat pasta, soft wheat pasta has a softer and more elastic structure, with a color that ranges from \"white to creamy white to moderately yellow\". Durum wheat products are characterized as having a harder structure, intense yellow nuance, nutty flavor, stability to overcooking, and a particular eating quality [22]. The intrinsic properties of wheat flour and the processing conditions are the most important factors that influence pasta cooking quality [23]. There are a few researches presenting the impact of HMT applied to wheat. Furthermore, even if there are some papers that showed the influence of grape peels on dough and final product properties, to our knowledge, no studies have been published regarding the combined effect of HMT and grape peels addition on common wheat dough and pastaquality. Thus, the aim of this investigation was to evaluate the synergistic effects of HMT regime in terms of temperature, time, and moisture content and grape peels addition level on dough rheology and texture and on pasta's functional, physical, and textural properties in order to optimize the production process.
## 1 Introduction
In order to address the above-mentioned challenges, an appropriate charging strategy for the drone-based delivery systems is necessary. Therefore, some studies formulated joint charging and routing strategies to meet this demand [12]. It is crucial to obtain the optimal number of CSs with suitable locations according to the distribution of delivery points to extend the flight time of drones. Diverse strategies have been attempted to meet this demand. Generally, for long-distance delivery missions, CSs enabled with wireless power transfer are considered. In contrast, in truck-drone collaborative delivery, drones are considered last-mile delivery vehicles, and battery replacement is considered a convenient solution to reduce charging time and minimize delivery time. Thus, in a typical truck-drone-based delivery systems, trucks provide the charging facility. Some approaches consider the use of public transport to provide charging to the drones [18] and as a carrier to bring the drones to a location that is closer to the customers in remote places [11]. To keep the customer integrity in terms of delivery data and trajectory data, appropriate security measures must be implemented. Moreover, due to the recent booming of the e-commerce platform, drone-based delivery systems are being promoted as an urgency to serve as a faster delivery system. Thus, hacking of a drone containing sensitive information related to both propriator and customer puts a huge risk for both parties involved [19]. Additionally, the leakage of monetary information, transaction record, and other information of both parties would jeopardize the whole drone-based system. Many blockchain based techniques, where the information is stored in blocks, and encryption-decryption approaches are being implemented to solve this issue [20]. The different aspects of the routing problem are detailed in the upcoming sections. Thus, in this review, we aim to identify the main design problems that arise when designing drone routing techniques in drone-based delivery systems. We achieve this by comprehensively reviewing the existing drone routing algorithms. We categorize the existing drone routing techniques in terms of three aspects: trajectory planning, charging, and security. We then detail their key features and operational characteristics. Subsequently, we compare the drone routing techniques in terms of main idea, advantages, limitations, performance aspects, and outstanding features. Moreover, we provide future research and development challenges that may have crucial impact on the future drone-based logistics systems.
## 3 Results and Discussion
### The Effect of Essential oils on the Viability of Bifidobacterium in In Vitro Conditions
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**K1**} & \multicolumn{4}{c}{**Content (\%) 2**} & \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**KI**} & \multicolumn{4}{c}{**Content (\%)**} \\ \cline{5-10}  & & & **Lemon Peel** & & & **Clove Bud** & & **Juniper Berry** & & **Lemon Peel** & & **Clove Bud** & **Juniper Berry** \\ \hline
1. & 2.3-Hexanedione & 793 & -3 & - & 0.42 & 61. & (E,E)-2.4-Decadien & 1314 & - & - & 0.22 \\ \hline
2. & 2-Heptanone & 889 & - & 0.62 & - & 62. & Methyl geranate & 1323 & - & - & 0.49 \\ \hline
3. & Heptanal & 896 & 0.05 & - & - & 63. & Chavioc acetate & 1329 & - & 0.13 & - \\ \hline
4. & \(\alpha\)-Trylence & 926 & - & - & 0.31 & 64. & Circnell acetate & 1350 & 0.25 & - & \\ \hline
5. & \(\alpha\)-Trylence & 926 & 1.34 & - & 3.12 & 65. & \(\alpha\)-Tepriyal acetate & 1351 & - & - & 0.39 \\ \hline
6. & \(\alpha\)-Pinne & 934 & 2.68 & - & 5.71 & 66. & \(\alpha\)-Cubeene & 1351 & - & 0.04 & 1.28 \\ \hline
7. & Camphene & 945 & 0.17 & - & 0.63 & 67. & Neryl acetate & 1354 & 1.81 & - & - \\ \hline
8. & Subinene & 969 & 3.06 & - & 6.19 & 68. & Eugenol & 1356 & - & 62.74 & - \\ \hline
9. & \(\beta\)-Pinne & 972 & 7.14 & - & 3.59 & 69. & Geranyl acetate & 1373 & 1.84 & - & - \\ \hline
10. & \(\beta\)-Myrcne & 985 & 4.49 & - & 6.45 & 70. & \(\alpha\)-Copane & 1376 & - & 0.35 & - \\ \hline
11. & Decane & 996 & - & 0.08 & - & 71. & Tetradecene & 1390 & 0.02 & - & - \\ \hline
12. & Ethyl hexanedate & 998 & - & 0.05 & - & 72. & Tetradecane & 1398 & 0.08 & - & - \\ \hline
13. & Octanal & 999 & 0.66 & - & - & 73. & \(\beta\)-Elemene & 1391 & - & - & 2.01 \\ \hline
14. & \(\alpha\)-Tbellandrene & 1002 & 0.28 & - & 0.67 & 74. & Dodecanal & 1399 & 0.14 & - & - \\ \hline
15. & \(\delta\)-3-Carene & 1007 & 0.02 & - & 0.73 & 75. & \(\alpha\)-Gurjunene & 1407 & - & - & 0.49 \\ \hline
16. & \(\alpha\)-Terpinene & 1019 & 0.99 & - & 1.61 & 76. & Longfiolene & 1408 & & & 0.13 \\ \hline
17. & p-Cymene & 1026 & 4.06 & - & 3.98 & 77. & Caryophyllene & 1408 & 0.45 & 6.24 & 1.54 \\ \hline
18. & Eucalyptus1031 & - & 0.02 & - & 78. & \(\beta\)-Copane & 1416 & - & - & 0.27 \\ \hline
19. & Limnene & 1033 & 31.51 & 0.04 & 4.78 & 79. & \(\alpha\)-Bergometene & 1430 & 1.21 & - & - \\ \hline
20. & 2-Heptanol acetate & 1044 & - & 0.71 & - & 80. & Aromadenne & 1439 & - & - & 0.13 \\ \hline
21. & Oximene & 1047 & 0.26 & 0.11 & - & 81. & Cadina-3.5-diene & 1437 & - & - & 0.14 \\ \hline
22. & \(\gamma\)-Terpinene & 1059 & 12.53 & - & 1.81 & 82. & \(\alpha\)-Famesene & 1508 & 0.23 & - & 1.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical compositions of essential oils from lemon peel (C. flavor L. [PERSON]), clove bud (S. _annotation L.) and juniper berry (_J. commans_ L.) determined by GC-MS.
# From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
## Appendix C Elementary Operators
### SWAP and XY Operators  
We examined the relationship between the quantum SWAP operator SWAP\({}_{i,j}\), which swaps the state of qubit \(i\) and \(j\), and the XY operator, \(\frac{1}{2}(X_{i}X_{j}+Y_{i}Y_{j})\). In many of our mapping constructions, these operators can be used interchangeably; an exception is given in the last item below.  
Recall Equation (10) and the discussion of SWAP preceeding it. The XY operator acting on qubits \(i,j\) can be similarly expressed as:  
\[XY_{i,j}:=\frac{1}{2}(X_{i}X_{j}+Y_{i}Y_{j})=|1_{i}0_{j}\rangle\langle 0_{i}1_{ j}|+|0_{i}1_{j}\rangle\langle 1_{i}0_{j}|\,. \tag{12}\]  
We observed the following connections between the two operators:  
* In the subspace spanned by \(\{\ket{01},\ket{10}\}\), \(XY_{i,j}\) and SWAP\({}_{i,j}\) behave identically.
* In the subspace \(\{\ket{11},\ket{00}\}\), \(XY_{i,j}\) acts as null while SWAP\({}_{i,j}\) acts as an identity.
* The operators \(XY_{i,j}\) and SWAP\({}_{i,j}\) are both Hermitian. SWAP\({}_{i,j}\) is unitary.
* Applied to a multiqubit system, Hamiltonians of the form \(H_{\text{SWAP}}=\sum_{i,j}\text{SWAP}_{i,j}\) or \(H_{\text{XY}}=\sum_{i,j}XY_{i,j}\), where either sum may be taken over arbitrary subsets of indices, each preservethe Hamming weight of computational basis states; hence, so do the corresponding unitaries \(\exp[-i\beta H_{\text{XY}}]\) and \(\exp[-i\beta H_{\text{SWAP}}]\). Although the two operators do not behave identically on the full Hilbert space, they can both serve as mixers in situations in which Hamming weight is the relevant constraint.
* To enforce simultaneous swaps of multiple qubit pairs, in Hamiltonians such as \(H=\prod_{\{i,j\}}\text{SWAP}_{i,j}\), each \(\text{SWAP}_{i,j}\) cannot in general be directly replaced by \(XY_{i,j}\) due to the second item above. See the TSP problem in Section 5.1 as an example.