esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 4 Performance Evaluation
### Simulations
#### 4.3.4 Performance Evaluation for the TCP Traffic Scenario  
TCP is one of the most widely used transport layer protocols along with UDP, and many applications transmit data through TCP. Therefore, we conducted a simulation study to evaluate GCS-routing performance with TCP. Similar to Sections 4.3.2 and 4.3.3, we evaluated GCS-routing for the mobility and replacement scenario, but employed TCP rather than UDP.  
For the mobility scenario, Drone 4 moved along the arrow and sent TCP traffic to GCS without throughput limitation. We conducted this simulation 10 times for each routing protocol, with the outcomes as shown in Table 8. OLSR was omitted because OLSR in ns-3 does not support TCP data communications. GCS-routing provided better network throughput and stability than DSDV. The standard deviations in Table 8 are much larger than those in Table 4 due to larger TCP throughputs than the previous UDP case.  
For the replacement scenario, the user located in Drone 4's service area sent TCP data traffic without throughput limitation to Drone 1. We also conducted this simulation 10 times for each routing protocol, with outcomes as shown in Tables 9-11. Network disconnected times of GCS-routing are much shorter than those of DSDV, and GCS-routing provides better network throughput than DSDV. GCS-routing standard deviation was also significantly smaller than that of DSDV. Thus, GCS-routing provided more stable network performance than DSDV.  
Thus, the simulations confirmed that GCS-routing provided better network performance than DSDV even when using TCP.
## 2 Materials and Methods
### SPME-GC-MS Volatile Analysis  
Dried, finely powdered seeds (unroasted/roasted) (20 mg) were placed in SPME screw-cap vials (1.5 mL) spiked with 10 \(\upmu\)g (\(Z\))-3-hexenyl acetate with fibers inserted manually above and placed in an oven kept at 50 \({}^{\circ}\)C for 30 min. HS-SPME analysis of the volatile compounds was performed as reported in [10] with slight modifications. The fiber was subsequently withdrawn into the needle and then injected manually into the injection port of a gas chromatography-mass spectrometer (GC-MS). GC-MS analysis was adopted on an Agilent 5977B GC/MSD equipped with a DB-5 column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m film thickness; Supelco) and coupled to a quadrupole mass spectrometer. The interface and the injector temperatures were both set at 220 \({}^{\circ}\)C. Volatile elution was carried out using the following gradient temperature program: oven was set at 40 \({}^{\circ}\)C for 3 min, then increased to 180 \({}^{\circ}\)C at a rate of 12 \({}^{\circ}\)C/min, kept at 180 \({}^{\circ}\)C for 5 min, finally increased at a rate of 40 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C and kept at this temperature for 5 min. Helium was utilized as a carrier gas with a total flow rate of 0.9 mL/min. For ensuring complete elution of volatiles, SPME fiber was prepared for the next analysis by placing it in the injection port at 220 \({}^{\circ}\)C for 2 min. For assessment of biological replicates, three different samples for each _P. mahaleb_ seed accession were analyzed under the same conditions. Blank runs were made during sample analyses. The mass spectrometer was adjusted to EI mode at 70 eV with a scan range set at _m/z_ 40-500.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample Codes** & **Seed Color** & **Location** \\ \hline WG & White & Greece (Athens) \\ WE & White & Egypt (Cairo) \\ WS & White & Sudan (Khartoum) \\ RWS & Roasted white & Sudan (Khartoum) \\ RE & Red & Egypt (Cairo) \\ RRE & Roasted red & Egypt (Cairo) \\ RS & Red & Sudan (Khartoum) \\ RRS & Roasted red & Sudan (Khartoum) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Origins of the different _P. mahaleb_ seed accessions used in analysis.
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 4 Shallow Underwater Acoustic Channel Model
### Ambient Noise  
The noise in a shallow underwater acoustic channel is location specific, frequency dependent, and cannot be modelled as white noise. For the simulations, an ambient noise model combining thermal, shipping, wave, and turbulence noise [41] was incorporated. The power spectral density (PSD) was computed as:  
\[N(f)=10\log\biggl{(}10^{\frac{N_{ shipping}(f)}{10}}+10^{\frac{N_{turbulence}(f)}{10}}+10^{\frac{N_{ \text{super}}(f)}{10}}+10^{\frac{N_{thermal}(f)}{10}}\biggr{)}. \tag{20}\]  
It could be modelled as colored noise and had a high amplitude around the lower and higher end of the acoustic communication spectrum and was the lowest at around 60 kHz [42].
## 4 Classification of Auto-Configuration Protocols
### Stateless Protocols
#### a.2.2 Apac  
_Agent based Passive Auto-configuration_ (APAC) [23] is an auto-configuration protocol based on PDAD. Its main feature is the use of certain nodes which centralize the distribution of addresses. The mechanism by which a node configures its IP address upon entering the network consists of asking if it has some node type _Address Agent_ (AA) within a one hop distance. In that case, the AA node will give it an IP address.  
If it does not receive a response from any AA node, the incoming node is configured to operate in AA mode, and it will be a server of addresses for the next incoming nodes. When it is configured as AA, the node randomly generates an identifier number _agentID_, in order to form a table with the addresses that it will assign to the nodes that arrive. These addresses have the form _agented_\(+\)_hostID_.  
When a node is moved in the network and leaves the radio coverage of AA proportionately to its IP address, this node must ask for another address from another node AA within its new radio coverage. This is complemented by a mechanism preventing communication interruptions in the process. In this situation, the previous AA will mark its IP address as free in order to be allocated to some other node later.  
The detection of duplicated addresses is undertaken in the PDAD process. Once any conflict has been detected, the AA which assigned the conflictive address is informed. This AA node will then generate a new _agentID_ and it will warn all its dependant nodes to change their address from _agented_\(+\)_hostID_ type to the new agentID.  
With regards to the partitioning and merging of networks, the same above mentioned mechanism is utilized. In case of partitioning, the nodes AA will mark addresses which have left the network as free. In the case of the merging of two networks, the mechanism for the detection of duplicate addresses continues working properly.
## 4 Conclusions  
We have followed the TDD paradigm in the development of an in-house code to allow for the finite element analysis of finite periodic type electromagnetic structures (with many potential applications, e.g., analysis of antenna arrays, metamaterials, and so on). We have introduced substructuring techniques based on a unit cell that is virtually repeated on a \(1\times M\) of \(M\times M\) to model the structure under analysis. Specifically, on the so-called one Schur complement approach, we manage to model the problem using only one type of Schur complement in contrast to the conventional use of a number of different Schur complements depending on the situation of the associated domain (unit cell) in the structure (i.e., on one side, corner, interior, etc.). As a result, we have obtained faster computations.  
These substructuring techniques have been introduced in an existing general-purpose FEM code. Along the process of code development and integration of the software, we have written tests for small increments of functionality; then, we have developed the code to pass the tests; and finally, we have used refactoring to increase the performance and readability of the code. Thus, we have obtained a reliable and maintainable code in less time than using a conventional software development paradigm.  
Specifically, we have used unit and integration tests, system tests (where we have applied MMS to isolate single terms within the FEM formulation), and application tests. We have shown convergence tests and the resolution of the code with the approximation of monomials contained in the space of functions that we use. Additionally, we have used smoke tests to have a fast way of debugging all the features of the code. Finally, we have shown a real-life example with an array of pyramidal horns, whereas we have realized a performance analysis of the code.  
Conceptualization, I.M.-F. and L.E.G.-C.; software, I.M.-F.; validation, I.M.-F. and A.A.-M.; investigation, A.A.-M. and L.E.G.-C.; data curation, I.M.-F. and A.A.-M.; writing--original draft preparation, I.M.-F. and A.A.-M.; writing--review and editing, L.E.G.-C.; supervision, A.A.-M. and L.E.G.-C.; resources, L.E.G.-C.; funding acquisition, L.E.G.-C. All authors have read and agreed to the published version of the manuscript.  
This work has been financially supported by TEC2016-80386-P and PID2019-109984 RB-C41.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABC & Absorbing Boundary Condition \\ DDM & Domain Decomposition Method \\ FEM & Finite Element Method \\ MMS & Method of Manufactured Solutions \\ MPI & Message Passing Interface \\ TDD & Test-Driven Development \\ \end{tabular}
# A Research Study on Protocol Stack of Space-Based Optical Backbone Network
## 3 Design of the Protocol Stack
### Design of Transport Plane  
From the design discussed in the architecture of protocol stack, we know that the transport plane should implement functions of control message transmission, data transmission, service adaptation and AOS frame switching.
## 3 System Descriptions and Assumptions
### Cache Chord Mechanism
#### 3.3.3 Identifier Mapping  
At the end of time slot \(t\), each server node in the edge layer executes strategy \(\ae_{d,t}\) to update the cache IP list. Specifically, if server \(d\) stores or deletes an application task in the local cache pool, i.e., \(\rho_{k}^{d,t}=-1\) or \(1\), it requires executing Algorithm 2 to update the cache IP list. This ensures consistency between the data index in the overlay network and the storage status of data in the actual environment.  
```
1:Initialize\(N1D\), \(K1D\), \(\text{Nod}X\).Cache\(PIlist\), \(\text{Nod}X\).FingerTable, cache replacement decision \(\ae_{d,t}\) of \(\text{Nod}X\)
2:for each server \(d\) in cluster \(\Delta\)do
3:for each task \(k\) in server \(d\)do
4:if\(\rho_{k}^{d,t}\
eq 0\)then
5: search Task \(k\) through execute Algorithm 1 and get IP address of target server node \(\text{Nod}T\)
6:if\(\rho_{k}^{d,t}=1\)then
7: add \(\text{Nod}X\) IP address into corresponding \(\text{ServerIP}_{Y}^{t}\) of \(\text{Nod}T\).Cache\(PIlist\)
8:endif
9:endif
10:endfor
11:endfor
12:return\(\text{Key}Y\) IP address to original server node \(\text{Nod}X\)
```  
**Algorithm 1** Application Key Routing  
In this section, we present the proposed algorithm for the proposed algorithm.  
```
1:Initialize\(N1D\), \(K1D\), \(\text{Nod}X\).Cache\(PIlist\), \(\text{Nod}X\).FingerTable, cache replacement decision \(\ae_{d,t}\) of \(\text{Nod}X\)
2:for each server \(d\) in cluster \(\Delta\)do
3:for each task \(k\) in server \(d\)do
4:if\(\rho_{k}^{d,t}\
eq 0\)then
5: search Task \(k\) through execute Algorithm 1 and get IP address of target server node \(\text{Nod}T\)
6:if\(\rho_{k}^{d,t}=1\)then
7: add \(\text{Nod}X\) IP address into corresponding \(\text{ServerIP}_{Y}^{t}\) of \(\text{Nod}T\).Cache\(PIlist\)
8:endif
9:endif
10:endfor
11:endfor
12:return\(\text{Key}Y\) IP address to original server node \(\text{Nod}X\)
```  
**Algorithm 2** Cache IP List Update  
Figure 3: Cache Chord mechanism.
# Mexican Bee Honey Identification Using Sugar Crystals' Image Histograms
###### Abstract
Raman spectroscopy is widely used in conjunction with statistical analysis, using principal component analysis (PCA), linear discriminant analysis (LDA), or correspondence analysis (CA) for botanical origin classification. However, in this technique, the honey peaks observed in the spectrum are often compromised by an intense fluorescence background overlapping the Raman signal [18]. Another method treats honey with macroporous adsorption resins and distinguishes it by high-performance liquid chromatography with an electrochemical detector and chemometrics [19]. The chromatographic fingerprints of the honey samples were established, and the samples were analyzed using principal component analysis and orthogonal projections to the discriminant analysis of latent structures [20]. Furthermore, among all of the known physical-chemical methods used for determining the botanical origin of honey, no method specializes in using crystalline formation to achieve this purpose [7; 21]. This work uses SHG microscopy imaging to acquire detailed images of the honey's crystals [22]. SHG allows for the observation of sugar crystals without the interference of liquid honey, as happens when bright field microscopy is used. The number, shape, and distribution of these crystals are distinctive for each kind of honey, and it is used in this work to identify it regardless of its color, pollen grains, or other chemical parameters. Each SHG image generates a unique gray-level histogram that depends on the intensity distribution of the obtained SHG signal. The illumination source intensity applied to all of the samples under study is the same, employing the microscope's hardware to allow for a comparison. The latter allows for a differentiation of the gray-level histogram for each image retrieved. This method only requires a small amount of honey under study, as it is a microscopical technique. Additionally, it does not require the special preparation of the honey and does not induce damage to it, like some other techniques may [21]. Several honey samples from different regions of Mexico were studied in this proof-of-principle. It has been reported that honey crystallization is different according to the floral origin of the honey [7; 8], a feature observed with SHG. The corresponding gray-level histogram distribution for each sample was analyzed using the PCA multi-variable method [23; 24]. PCA can identify the differences among samples without needing a morphological analysis performed directly on the second harmonic images. The proposed analysis can differentiate among the honey samples using only one second harmonic (SH) image for each one. The recorded SH image has unique information on each honey's sugar crystal shape. The latter contributes to the botanical identification of the honey through a non-destructive imaging technique and statistical analysis.
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 3 System Model
### Stage 2: Task Scheduling  
In the second stage of task scheduling, the tasks in the server cache queue are first dynamically sorted, and then the UQRL algorithm is used for task scheduling from the server to the VMs. The structure is shown in Figure 2.  
Each server has m number of VMs, represented as \(VM_{1},VM_{2},\ldots,VM_{j},\ldots,VM_{m}\), where \(1\leq j\leq m\). Each VM has a task queue, called the virtual machine queue (VMQ), represented as \(VMQ_{1},VMQ_{2},\ldots,VMQ_{j},\ldots,VMQ_{m}\), where \(1\leq j\leq m\). When tasks arrive at the server, they are first cached in the SQ. We have defined a time window to execute the UQRL task scheduling algorithm once it is within a certain time period.  
In existing Q-learning-based cloud task scheduling methods, user requests are usually processed using a first-come-first-served (FCFS) approach. However, actual submitted user tasks may have various requirements for task deadlines (completion times). Intuitively, all other conditions being equal, tasks with shorter deadlines should be scheduled first. Therefore, in this study, tasks in the SQ are dynamically sorted based on their deadline, making it easier to prioritize urgent tasks and improve the system's QoS. Then, the UQRL algorithm is used to schedule tasks and send them to the VMQs of the different VMs, where they wait to be executed. The VMs accept tasks one-by-one from their respective VMQs for execution. A detailed description of the algorithm is provided in Section 4.  
The time a task takes from entering the cloud center to completion can be divided into several parts: waiting time (the time spent in queue), transmission time (the time for a task to be transmitted over the link), and execution time (the time required for the VM to process the task). This article mainly discusses RL scheduling algorithms. To focus on the research problem, the transmission time is temporarily ignored, and only waiting time and execution time are considered. In the first stage of task allocation, when a user task arrives in the GQ it is immediately assigned to the SQ. When the number of servers is sufficient and the SQ capacity is large enough, the waiting time of tasks in the GQ can be ignored. Therefore, we only consider the waiting time of tasks in the SQ and VMQ.  
For user tasks, we make the following assumptions:  
1. When user tasks arrive at the cloud server, they are independent.
2. Each task is complete and cannot be further divided into smaller tasks.
3. There are no dependencies between tasks.
4. A VM can only execute one task at a time.
## 2 Materials and Methods
### The Description of Study Sites
#### 2.1.1 The Sugarcane Plantation Site in Sao Paulo, Brazil (USR)  
The sugarcane plantation EC flux tower site (21.6366\({}^{\circ}\)S, 47.7897\({}^{\circ}\)W) is located at Luiz Antonio municipality, Sao Paulo State, Brazil [18]. Sugarcane varieties (SP81-3250, SP83-2847, and RB86-7515) were planted in this site over years. The site is 552 m high above sea level and its soil type is sandy soil. The soil and sugarcane stalk yields were representative of southern Brazil [48; 49]. The canopy height reached approximately 5 m when sugarcane was at the peak of its growing season. The distance between the sugarcane planting rows was 1.4 m. The sugarcane plantation field (\(>\)400 ha) has a gentle slope of \(<\)2%, and the close-by vegetation includes pasture, citrus fruit orchards, and savanna woodland [49]. Sugarcane is a multi-year ratoon crop, and on average sugarcane fields in the region have a cropping cycle comprising one plant crop and four ratoon (regrowth) crops [18; 49]. At the USR site, the sugarcane plantation was planted in 2003 and replanted in 2007, one year less than the average because a low care stalk yield was observed in the 2007 harvest (day of the year 149). The detailed agricultural practices can be found in the publication [18].
# Development and Application of Intelligent Temperature Control System for Large Aqueduct
## 1 Introduction
[PERSON] [11] developed the digital Guangdong Dam concrete temperature control intelligent monitoring system in combination with Guangdong Dam. The system is integrated by multiple software and multiple subsystems, which can realize the automated analysis of information, evaluation, early warning alarm and unmanned cooling of dam water pipes. Scholars have made different contributions to the intellectualization of mass concrete water-cooling methods based on practical projects. [PERSON] and [PERSON] [6,7], based on temperature monitoring methods and a temperature database, used different methods to analyze temperature data and get more accurate temperature data and control indicators, but they did not more in-depth research on the intelligence of concrete temperature control. [PERSON] and [PERSON] [8,9] developed an intelligent temperature control system for concrete based on the intelligent cooling of concrete after pouring in bridge and underground powerhouse projects, while [PERSON] and [PERSON] [10,11] developed an intelligent temperature control system for concrete based on the information interaction and intelligent control of the whole process from raw materials to thermal insulation in hydro junction and dam projects. However, these temperature control systems are based on mass concrete such as dams and bridge foundations, and the ideal control curves used are also determined before construction based on analytical calculations, and no real-time dynamic adjustment is made during the construction process. In contrast, most of the large aqueducs are thin-walled structures, which are usually cast with higher strength concrete, and their temperature control sensitivity and accuracy requirements are significantly different from those of massive concrete such as dams. Therefore, based on the combination of concrete water pipe cooling  
ture change of concrete feature points with the temperature change control curve of each point, the cooling water flow rate to be adjusted is determined. The simulation calculation module automatically calls the solver through the batch file, loads the command stream after modifying the parameters, calculates the temperature stress field of the aqueduct body and then transmits the temperature stress data of the feature points used to update the concrete temperature variation control curve back to the information processing and decision-making module. Information collection and control module receives information processing and decision module control instructions, automatically adjusting the flow rate and valve switch.
## 2 System Model and Problem Formulation
### Communication Model
Therefore, the expected power of the signal received by TTG \(k\) from satellite \(m\) is denoted as  
\[P_{k}^{t}=P_{m}^{t}\cdot 10^{-\frac{\mathrm{LSF}_{k,m}^{t}}{10}}, \tag{15}\]  
where \(P_{k}^{t}\) and \(P_{m}^{t}\) represent the received and transmitted signal power at TTG \(k\) and satellite \(m\), respectively. Consequently, the signal\(-\)to\(-\)noise ratio (SNR) at the receiver of TTG \(k\) is  
\[\mathrm{PL}_{k,m}^{t,fre}\left[\mathrm{dB}\right]=32.45+20\log_{10}(f_{c})+20 \log_{10}(d_{k,m}^{t}), \tag{7}\]0  
where \(N_{0}\) denotes the power of additive white Gaussian noise (AWGN).