esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction and Related Work
In this paper, we consider an IoT application that is implemented with Contiki operating system support. The Contiki OS is intended for networked, memory-constrained, and low-power wireless IoT devices. Contiki provides multitasking and has a built-in Internet Protocol Suite (TCP/IP stack) with low memory requirements. The Contiki programming model is based on protothreads [17; 18]. The operating system kernel invokes the protothread in response to an event (timers expired, messages posted from other processes, triggered sensors, incoming packets from a network neighbor node). In Contiki, the protothreads are cooperatively scheduled; therefore, the process must always explicitly yield control back to the kernel at regular intervals by using a special protothread to block waiting for events while yielding control to the kernel between each event occurrence. Contiki supports inter-process communication using messages passing through events.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 3 Modelling
### Deterministic Models
#### 3.4.6 Cort3D  
The CORT3D model helps to assess the impact of site-specific conditions on the effectiveness of chemical oxidation and to make decisions and plan injection (site, oxidant concentration) [297]. This tool is based on the widely used and well-tested code RT3D version 2.5 [20; 384; 664]. CORT3D simulates the dissolution of NAPLs using the stagnant film model [665], equilibrium or rate-limited sorption, second-order kinetic contaminant oxidation, kinetic oxidation of NOD considering a fast and a slow kinetic part, and diffusion simulated using different effective diffusion coefficients for each mobile species. The code simulates three aqueous mobile components (contaminant, aqueous chloride, and aqueous oxidant) and five immobile components (NAPL, sorbed contaminant, manganese oxide, fast NOD, and slow NOD). The CORT3D calculations account for velocity changes caused by time-varying permeability due to the dissolution of NAPL and the precipitation of manganese dioxide due to permanganate consumption. This code has been tested at the Navy Training Centre site in Florida [666].
# Honey Therapy in Diabetic Foot Ulcers: A Promising Strategy for Effective Wound Healing
## 2 Diabetes
### Diabetic Foot Ulcers
#### Infection and Wound Healing
Beyond macrophage dysfunction [43], animal models of diabetes show impaired fibroblast response to growth factor stimulation [46], as well as impaired keratinocyte and fibroblast migration and proliferation [47]. Diabetes might also impair wound healing by decreasing the growth factors responsible for the extracellular matrix formation, such as keratinocyte growth factor (KGF) and fibroblast growth factor (FGF) [48; 49]. Beyond this, the expression of receptors can also be reduced, such as transforming growth factor \(\beta\) receptor type 1 (TGF-\(\beta\)R1), which is responsible for fibroblast-to-myofibroblast differentiation during the granulation tissue formation process, leading to deficient re-epithelialization and wound contraction [50]. In fact, IGF-1 expression in animals with diabetes was also reported to be delayed and decreased during the healing process when compared with non-diabetic animal models [51]. Delayed vascular regeneration was also observed in diabetes due to many factors, such as receptor dysfunction, a reduction in the number of essential ligands or cells, and impaired epithelial-to-mesenchymal transition (EMT) [49]. In fact, a reduced functional capillary density and decreased angiogenesis-positive area were observed in animal models with diabetes, showing impaired vascular regeneration [26]. Moreover, the levels of the transcription factor zinc finger E-box binding homeobox 1 (ZEB1) were observed to be responsive to the hyperglycemia status, and the dysregulation in ZEB1 expression led to a defective EMT towards wound epithelialization and poor angiogenesis [52]. The severity of DFU is classified by the Wagner system into grade I, indicating a superficial uninfected ulcer; grade II, indicating a deep ulcer with exposed tendons; grade III, indicating a deeper ulcer with exposed bone and infection; grade IV, indicating partial gangrene of the foot; and grade V, indicating complete gangrene [7]. In a study performed in 194 patients with DFU grades between I and III, 16% did not heal in 6 months, 15% underwent amputation, and 4% died [53]. Therefore, diabetes is known to compromise wound healing via multifactorial mechanisms, increasing several concerns about the development and prevalence of DFUs in this population.
# Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications
## 5 Conclusions  
Several previous review papers exist on the use of remote sensing for agriculture [101; 102; 103], however, to our knowledge there has not yet been a specific review article on Sentinel-2 features for agriculture. We are convinced that may prove beneficial to researchers and agrarian institutions entering this field to understand the features and applications of Sentinel-2 in crops monitoring alone and as compared to other previous and current satellites. The comparison of the Sentinel-2 A + B constellation with other orbiting satellites with similar features highlights its improved capacities within precision agriculture. In this sense, we detail several applications on the detection andmanagement of biotic and abiotic stresses, crop water requirements, crop type classifications, and the estimation of crop yields and nitrogen status. Furthermore, besides the promising technical features of Sentinel-2, it is worth mentioning the public availability of its imagery. By making this data accessible to all interested institutions, the potential benefits of precision agriculture are greater and may better provide the mechanisms for advising farmers towards a more productive and sustainable management, thus, contribute to economic and ecological sustainability worldwide.  
[PERSON], [PERSON], [PERSON] and [PERSON] conceived the review; [PERSON] and [PERSON] performed the literature research; [PERSON] made the figures; [PERSON] and [PERSON] analyzed the scientific rigor; [PERSON] coordinated the structure and scope of the topic; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish projects AGL2016-76527-R and IRUEC PCIN-2017-063 from the Ministerio de Economia y Competividad (MINECO, Spain) and by the support of Catalan Institution for Research and Advanced Studies (ICREA, Generalitat de Catalunya, Spain), through the ICREA Academia Program.  
This study was supported in part by the Spanish project AGL2016-76527-R from the Ministerio de Economia y Competividad (MINECO, Spain) and by the support of Catalan Institution for Research and Advanced Studies (ICREA, Generalitat de Catalunya, Spain), through the ICREA Academia Program.  
The authors declare no conflict of interest.
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 3 Small-Scale Vapor Compression Refrigeration (VCR) Systems
### Components
#### 3.4.2 Condensers
Based on the available information, it can be appreciated that several types of heat exchangers can be used as condensers; however, microchannel condensers receive the most attention for small-scale applications due to the previously listed advantages: (1) high heat transfer rates, (2) compactness, and (3) reduction in refrigerant mass. It is expected that research on these components will continue due to technological progress leading to extended applicability of the technology.
## 2 System Model
### LEO Satellite Mega-Constellation Network Mobility Calculation Model  
Mobile technology has experienced exponential growth in the number of users, applications, and data traffic, with services ranging from multimedia streaming to online gaming [37; 38]. Unfortunately, 5G technology is unable to meet the requirements of low latency and high bandwidth under IoT services. Fortunately, the satellite mega-constellation helps to meet stringent latency requirements [39; 40].  
We focus on the LEO satellite network and the orbital height in the range of 500\(-\)2000 km, with a low transmission delay. Additionally, as a pioneer of LEO satellite mega-constellation networking, the Iridium constellation utilizes polar orbits to communicate with each other via inter-satellite links (ISLs) [41]. And the geometry structure of the Iridium constellation with six orbital planes (see Figure 2) can be simulated by the software SaVi 1.6.0 [42; 43], the red line denotes the orbit of the satellite, the green dot denotes a satellite. In this paper, we use the Iridium constellation for analyzing the caching policy in LEO-SMC-ICN-IoT.  
The LEO satellite mega-constellation is a complex mobility network. The prediction of the dynamic location of satellite nodes is a key point in caching policy. Our team has completed Section 2.2 of [41]. For ease of analysis, we briefly reiterate the orbit elements (also known as Keplerian elements) and the ISL geometry relationship of the Iridium constellation.  
Figure 1: LEO-SMC-ICN-IoT scenario.  
Figure 2: Iridium constellation.
# Stain Defect Classification by Gabor Filter and Dual-Stream Convolutional Neural Network
## 3 Gabor Image from Stain Defect Image
### Gabor Image Generation  
To extract the stain defect features for various frequency bands and directional, a total of 64 Gabor filters are generated according to the 8 amplitudes \(\lambda_{\alpha}\) and eight phases \(\theta_{\beta}\) as shown in the following equation, respectively. The size of each Gabor filter is \(3\times 3\).  
\[g(x,y;\lambda_{\alpha},\theta_{\beta},\psi,\sigma,\gamma)=\exp(- \frac{x^{\prime 2}+\gamma^{2}y^{\prime 2}}{2\sigma^{2}})\cos(2\pi\frac{x^{\prime}} {\lambda_{\alpha}}+\psi) \tag{9}\] \[\text{where, }x^{\prime}=x\cos\theta_{\beta}+y\sin\theta_{\beta} \text{,}y^{\prime}=-x\sin\theta_{\beta}+y\cos\theta_{\beta}\]  
\[\lambda_{\alpha}=2^{-\frac{4\kappa+2}{2}},\ \theta_{\beta}=\frac{\beta}{8} \pi\ \text{ where, }\alpha=0,1,\cdots,7,and\ \beta=0,1,\cdots,7 \tag{10}\]  
Gabor filter image \(I_{Gabor}\) are created through convolution operation between \(I_{Gray}\) and Gabor filter created.  
\[I_{Gabor}(m,n;\alpha,\beta)=\sum_{x=-1}^{x=1}\sum_{y=-1}^{y=1}g(x,y;\lambda_{ \alpha},\theta_{\beta},\psi,\sigma,\gamma)\ \cdot\ I_{Gray}(m+x,n+y) \tag{11}\]
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 3 5G Antennas as a Candidate for Massive MIMO Technique at Sub-6 GHz
### Single Elements
#### 3.1.2 2D-Model Antennas
Low-profile multi-slot patch antennas for long-term evolution (LTE) and 5 th generation (5G) communication applications consist of a step patch and a ground plane. Three slots were inserted into the patch to achieve the required operating bandwidth. The insertion slot enhanced the capacity effect, whereas the prototype antenna covered the operating frequency band (S11 \(\leq\) -10 dB) ranging from 3.15 to 5.55 GHz, supporting (N77/N78/N79) for the 5G wireless communication sub-6 GHz and LTE (22/42/43/46) bands. The broadband antenna offers an omni-directional and stable radiation pattern, excellent gain, and compact size, making this design suitable for wireless fidelity (Wi-Fi), wireless local area networks (WLAN), LTE, and sub-6 GHz 5G communication applications [98]. A T-slot on a rectangular microstrip patch with defective ground structure (DGS) makes up a slotted ground plane microstrip patch antenna for 5G wireless communications below 6 GHz. The suggested antenna's ground plane is a modified C-shaped slot. To further enhance the performance of the antenna, the modified C-slot has specific cuts on the bottom and top of the modified C-shape. The suggested antenna's gain is increased by integrating a reflector into the design to focus the side lobes and strengthen the main lobe of the radiated signal. The presented antenna uses insertion feeding and is constructed on a FR-4 epoxy substrate. The antenna has a size of 28.03 mm \(\times\) 23.45 mm \(\times\) 5.35 mm and dimensions of 5.49 dB in gain and 7.12 dB in the directivity at their maximums. The bandwidth of the suggested antenna was between 4.775 GHz and 5.049 GHz [99]. With dimensions of 35 mm \(\times\) 40 mm \(\times\) 1.6 mm and a dielectric constant of 2.55, the compact 5G sub-6 GHz wireless patch antenna was placed on a Taconic TLX-8 substrate. The antenna's peak gain was 6.83 dBi, and its frequency of oscillation was 5.6 GHz. With gains of 6.97 dBi and 5.97 dBi, respectively, the addition of a parasitic ring resonator close to the feed-line resulted in a double resonance at 5.6 GHz and 6.6 GHz. Through inserting a ring-shaped resonator in the ground, antenna miniaturization is also possible. As a result, the resonant frequency changed from 5.6 GHz to 3.8 GHz [100].
# Hierarchical Feature Aggregation from Body Parts for Misalignment Robust Person Re-Identification +
###### Abstract  
In this work, we focus on the misalignment problem in person re-identification. Human body parts commonly contain discriminative local representations relevant with identity recognition. However, the representations are easily affected by misalignment that is due to varying poses or poorly detected bounding boxes. We thus present a two-branch Deep Joint Learning (DJL) network, where the local branch generates misalignment robust representations by pooling the features around the body parts, while the global branch generates representations from a holistic view. A Hierarchical Feature Aggregation mechanism is proposed to aggregate different levels of visual patterns within body part regions. Instead of aggregating each pooled body part features from multi-layers with equal weight, we assign each with the learned optimal weight. This strategy also mitigates the scale differences among multi-layers. By optimizing the global and local features jointly, the DJL network further enhances the discriminative capability of the learned hybrid feature. Experimental results on Market-1501 and CUHK03 datasets show that our method could effectively handle the misalignment induced intra-class variations and yield competitive accuracy particularly on poorly aligned pedestrian images.  
Keywords:person re-identification; misalignment; hierarchical feature aggregation +
Footnote †: journal: Applied Sciences
## 3 Block Iteration with Parallelization
### Block Iteration
#### 3.1.1 The Iterative Feasibility of Constructing \(\Phi_{c,c}\)
```
0:\(\mathbf{x}_{b}\), \(\Delta\mathbf{x}_{b}\) and \(\xi_{tol}\)
0:\(\mathbf{x}_{c}\) and \(\Delta\mathbf{x}_{c}\)
1: Equally distribute \(\mathbf{x}_{b}^{(i)}\) to \(P_{(i)}\) and put \(\mathbf{x}_{c}\) in \(P_{main}\)
2:\(\mathbf{x}_{c}=\left[\mathbf{x}_{b_{i}}\right]/\text{Select the initial control point from boundary points randomly}\)
3: Calculate \(\Phi_{c,c}\), \(\Phi_{c,c}^{-1}\) and \(\boldsymbol{\alpha}\), and make \(\[PERSON]_{temp1}=\Phi_{c,c}\), \(\Phi_{temp1}^{-1}=\[PERSON]_{c,c}^{-1}\)
4:\(P_{main}\) sends \(\boldsymbol{\alpha}\) and \(\mathbf{x}_{c}\) to \(P_{(i)}\)
5: Calculate \(\Phi_{b,c}^{(i)}\), and make \(\Phi_{temp2}^{(i)}=\Phi_{b,c}^{(i)}\)
6:\(\Delta\mathbf{x}_{b}^{(i)*}=\Phi_{b,c}^{(i)}\boldsymbol{\alpha}\)
7:\(\boldsymbol{\varepsilon}^{(i)}=\Delta\mathbf{x}_{b}^{(i)}-\Delta\mathbf{x}_{b} ^{(i)}\)
8:\(P_{main}\) receives \(\boldsymbol{\varepsilon}_{max}^{(i)}\) from \(P_{(i)}\)
9:\(\varepsilon_{max}=max(\boldsymbol{\varepsilon}_{max}^{(i)})\)
10:for\(\dfrac{\|\boldsymbol{\varepsilon}_{max}\|_{2}}{\|\Delta\mathbf{x}_{b}\|_{2}}> \xi_{tol}\)do
11:\(\mathbf{x}_{c}=\left[\mathbf{x}_{c},\ \ \mathbf{x}_{b_{\varepsilon_{max}}}\right]\)
12:\(\[PERSON]_{c,c}=\left[ \begin{array}{cc}\[PERSON]_{temp1}&\mathbf{r}\\ \mathbf{r}^{T}&\phi(0)\end{array} \right]\)
13:\(\[PERSON]_{temp1}=\[PERSON]_{c,c}\)
14:\(\beta=-\[PERSON]_{temp1}^{-1}\mathbf{r}\), \(b=\dfrac{1}{\phi(0)-\mathbf{r}^{T}\[PERSON]_{temp1}^{-1}\mathbf{r}}\)
15:\(\[PERSON]_{c,c}^{-1}=\left[ \begin{array}{cc}\[PERSON]_{temp1}^{-1}+b\beta\beta^{T}&b \beta\\ b\beta^{T}&b\end{array} \right]\)
16:\(\[PERSON]_{temp1}^{-1}=\[PERSON]_{c,c}^{-1}\)
17:\(\boldsymbol{\alpha}=\[PERSON]_{c,c}^{-1}\Delta\mathbf{x}_{c}\);
18:\(P_{main}\) sends \(\boldsymbol{\alpha}\) and \(\mathbf{x}_{c}\) to \(P_{(i)}\)
19: Calculate \(\tau^{(i)}\)
20:\(\[PERSON]_{b,c}^{(i)}=\left[ \begin{array}{cc}\[PERSON]_{temp2}^{(i)}&\tau^{(i)}\end{array} \right]\)
21:\(\[PERSON]_{temp2}^{(i)}=\[PERSON]_{b,c}^{(i)}\)
22:\(\Delta\mathbf{x}_{b}^{(i)*}=\Phi_{b,c}^{(i)}\boldsymbol{\alpha}\)
23:\(\boldsymbol{\varepsilon}^{(i)}=\Delta\mathbf{x}_{b}^{(i)*}-\Delta\mathbf{x}_{b} ^{(i)}\)
24:\(P_{main}\) receives \(\boldsymbol{\varepsilon}_{max}^{(i)}\) from \(P_{(i)}\)
25:\(\varepsilon_{max}=max(\boldsymbol{\varepsilon}_{max}^{(i)})\)
26:endfor
27:end Greedy selection
```  
**Algorithm 1** Block iteration and Parallel computing in Greedy selection.
## 4 Optimization Design
### Analysis of Optimization Results  
After the above optimization process, the optimal solution is obtained: \([M_{1}\,M_{2}\,M_{3}\,M_{4}]\) = [0.503 0.402 0.653 0.765]. In order to form a comparison, this paper also compiles the programming of the traditional optimization method. To ensure the fairness of the comparison as much as possible, the traditional optimization methods still use the Pattern Search tool in the MATLAB toolbox. However, due to the fact that each optimization in traditional optimization methods requires a finite element operation, the maximum number of optimizations in traditional optimization methods is set to 2000 to prevent excessive time of computation. The optimal solution obtained by traditional optimization methods is [0.517 0.402 0.653 0.647]. We substitute the calculation results of the two methods into ANSYS for review, and the results are shown in Table 6. In the table, \(\sigma_{y,heel}\) represents the vertical stress at the heel position of the dam body, \(\sigma_{max,appream}\) represents the maximum stress in the upstream of the dam body, \(\sigma_{dmmx}\) represents the maximum value of compressive stress of the dam body, \(\sigma_{heel}\) represents the compressive stress at the heel of the dam, and \(\sigma_{toe}\) represents the compressive stress at the toe of the dam. As can be seen, the verification results all meet the requirements in the specifications.  
Figure 17: Comparison of optimization methods. (**a**) Neural network combination method; (**b**) common method.  
Table 7 lists the characteristic values of the initial, optimized dam size by traditional method and optimized dam size by method based on BP neural network, where \(V_{dm}\) refers to the amount of material used per one meter thickness of the dam body. The operation of both methods is based on the X86 architecture platform, with AMD7945 hx CPU, Nvidia 4060 laptop GPU, 16 GB memory, and Windows 11 operating system. The time consumption of both methods is recorded separately. When using the optimization method based on BP neural network, it takes 4 h and 17 min to obtain the 729 sets of data from ANSYS used for training the network, the training time for BP neural network is 15 s, and the time consumed by Pattern Search tool is 12 min, totaling 4 h and 30 min.  
The effect diagram of section optimization based on the BP neural network optimization design method is shown in Figure 18.
# A Wideband Terahertz Transmissive Polarization Manipulator Based on Metasurfaces
## 4 Conclusions  
In summary, a simple transmissive metasurface-based THz polarization manipulator with dual function is proposed. For incident linear \(x\) or \(y\) polarized waves, it transmits circularly polarized waves and works over 43.9% of bandwidth, which is the widest reported so far using any bilayered structure. For incident slant (xy) polarizations, it behaves as a THz wideband transmit reject filter. Close agreement between HFSS (FEM) and CST (FDTD) results ensures the validity and reliability of our design. Physical mechanisms, electrical circuit representation, and quantitative explanation of the working principle behind the polarization manipulation justify the designed structure. Such a simple and wideband polarization manipulator can be used in potential applications in THz manipulating devices, including biosensors and communication systems.  
Writing-Original Draft Preparation, A.K.F., Writing-Review and editing, A.K.F, C.R. and K.C., methodology, A.K.F., software, A.K.F., validation, K.C., and C.R., supervision, C.R., project administration, C.R.  
This research was funded by the National Natural Science Foundation of China, grant number 61831001, the High-Level Talent Introduction Project of Beihang University, grant number ZG216S1878, and the Youth-Top-Talent Support Project of Beihang University, grant number ZG226S1821.  
The authors declare no conflict of interest.
## 4 RoCoMAR Algorithm
### Adaptive Link Maintenance
is equal to or greater than \(R_{req}\), which is the end-to-end PDR value required by the user application, the RN resigns by sending the _CHG-NEXT-HOP_ message. ```
1:\(R(u,v)\leftarrow\emptyset\); \(\triangleright\)\(u,v\in G\)
2:while\(true\)do
3:whilestate == Stand-bydo
4:if Receive a _MREQ_ from \(v\)then
5: Obtain position information of \(u\) and \(v\);
6: Move to \(mid(u,v)\);
7: Send a _CHG-NEXT-HOP_ to \(u\);
8:\(State\) = Relaying;
9:endif
10:endwhile
11:whileState == Relayingdo
12: Pause for time(\(t_{alm}\));
13: Obtain \(R(u,v)\) from \(v\);
14:if\(R(u,v)<R_{req}\)then
15: Move to \(mid(u,v)\);
16:else
17: Stop at present position;
18: Send a _CHG-NEXT-HOP_ to \(u\);
19:\(State\) = Stand-by;
20:endif
21:endwhile
22:endwhile
```  
**Algorithm 2** Adaptive Link Maintenance and Reinforcement Algorithm-Run at RN  
In summary, for improving the quality of data transfer in the route, RoCoMAR first performs the link reinforcement process to identify the poorest link and to place one RN in the midpoint position of the link. After the RN is successfully placed in the midpoint position, it works as a relay node on the route by continuously performing the adaptive link maintenance in order to adjust its position and movement according to the network topology change.
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 3 Preliminaries  
### Transition Graph  
The transition graph can be denoted as \(G=(V,E)\), which models correlations between base stations. \(V\) represents the base stations corresponding to distinct locations, and \(E\) symbolizes the connections or pathways between these base stations.