esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sample Colorimeter  
BA is used instead of SF with the aim to lighten the porous material, as shown in Figure 1. Colorimetric analysis allows for the definition of a color with CIELAB coordinates: L (luminance) and two colors channel (a and b). L values are presented between 0 (dark) and 100 (white), while a and b are presented between negative and positive values. Negative values represent green and blue colors, while positive values are red and yellow for a and b, respectively. Table 2 shows the results of colorimetric analysis of all samples. It is evident that the colors are very different. Indeed, samples synthetized with SF (samples 1, 5 and 6) have lower L values (38.83 \(<\) L \(<\) 47.46) compared with samples obtained starting from BA (samples 2, 3 and 4) (81.97\(<\) L \(<\) 86.01).  
Figure 2: Sustainability analysis of all six samples in terms of the Embodied Energy (EE) and Carbon Footprint (CF).
## 2 Materials and Methods
### Molecular Representation and Geometry Optimization  
The chemical name of each of the compounds [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102] reported in Table S1 was used to retrieve the PubChem CID and the CAS registry number in the PubChem open library [11]. In addition, the.sdf file format of each compound was also obtained for molecule visualization and optimization. Initially, the alvaMolecule software [12] was used for molecule standardization and curation. These processes include the verification of each query through several filters, which performed the standardization of benzene rings into aromatic form, converted unusual covalent bonds to ionic forms, added charge to quaternary nitrogen atoms, removed/added excessive/missing hydrogens, and standardized nitro,azide and diazo groups. The canonical SMILES (simplified molecular-input line-entry system) notation of each compound was also generated in alvaMolecule. Then, an initial optimization of all molecules was developed with the Avogadro program using the UFF force field and the steepest descent algorithm [13]. The optimized conformations were then used to perform quantum chemical calculations in the ground state (gas phase) of the compounds with the program package Gaussian 09, Rev D.01 [14]. The final optimization was performed with the semi-empirical method PM6/ZDO. In addition, the frontier orbital energies (HOMO and LUMO) and global reactivity parameters associated with chemical reactivity were calculated as 3D molecular descriptors. In this way, the gap in energy between HOMO and LUMO (\(\Delta\)E) allowed the estimation of the stability of the molecules. The electron affinity (EA) and ionization potential (IP) can be defined as EA = \(-\)E\({}_{\text{LUMO}}\) and IP = \(-\)E\({}_{\text{HOMO}}\), respectively. Moreover, descriptors associated with the electronic structure and chemical reactivity such as the electronegativity (x = (I + A)/2), chemical potential (\(\mu\) = \(-\)\(\chi\)), chemical hardness (\(\eta\) = \(\Delta\)E/2), chemical softness (\(\sigma\) = 1/2\(\eta\)), electrophilicity index (\(\omega\) = \(\mu\)2/2\(\eta\)) and nucleophilicity index (N = 1/\(\omega\)) were also calculated [15; 16].
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 3 Results
### Spatial and Temporal Changes in China's EEQ
Notably, regulations such as the \"Opinions of the State Environmental Protection Administration on Further Strengthening Ecological Protection\" and \"Opinions on Strengthening Environmental Protection in Rural Areas\" were introduced during this time. Local governments actively organized environmental protection efforts and reinforced measures to combat ecological and environmental violations. Additionally, in Heilongjiang Province, the enforcement of straw burning regulations was intensified, resulting in a significant reduction in traffic congestion, air pollution, and forest fires caused by straw burning and other incidents [45]. Conversely, the years 2001 and 2015 exhibited the lowest EEQ in China. In 2001, despite rapid economic growth during the country's golden period of economic and social development, several EEQ issues emerged. Southeastern China experienced severe acid rain pollution, affecting over 90% of the 188 cities falling within the acid rain control area [46]. Furthermore, the 2015 report released by the National People's Congress highlighted the prominent challenges of heavy environmental pollution, high environmental risks, and ecological damage, reflecting the severity of the ecological and environmental situation in China during that year.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Discussion
### Multiple Animal Detection and LoRa Transmission Conflict  
The proposed system, designed for single animal detection, needs enhancements to handle real-world scenarios with multiple animals invading simultaneously. To address this, advanced computer vision techniques and additional sensors, like Radio-frequency identification (RFID) or acoustic sensors, can be integrated for accurate identification and tracking. Moreover, LoRa transmission conflicts can be resolved by implementing time-division or frequency-division multiple access techniques, collision detection, and retransmission mechanisms, ensuring reliable data transfer and enabling the system to effectively handle multiple animal detections and simultaneous LoRa transmissions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Duration** & **All-Camera Strategy** & **Our Strategy** & **Savings** \\ \hline
1 day & 11.472 & 9.149 & 2.323 \\ \hline
1 month (30 days) & 344.16 & 274.47 & 69.69 \\ \hline
1 year (365 days) & 4187.28 & 3339.385 & 847.895 \\ \hline
5 years (1825 days) & 20,936.4 & 16,696.925 & 4239.475 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of energy consumption (KWh).
# Using Statistical Shape Models to Optimize TKA Implant Design
## 1 Introduction
The SSM is built by aligning all bone models, computing an average geometry, and performing a principal component analysis on the deviations between the individual models and the average geometry. The resulting principal components are called modes of (geometric) variation. In a decreasing order of importance, these modes show the most important changes in geometry witnessed in the studied population. Modes can then be tuned to generate different sizes and geometries. The SSM thus corresponds to a statistical representation of the anatomy of the input population. Hence, it is a powerful tool to reduce the number of bone models to be analyzed in order to obtain statistically relevant measurements. Once the SSM is built, the question arises of how to best use it to validate that the size and shape of TKA components are well adapted to each patient's anatomy. To the authors' knowledge, there is no literature describing how to use SSMs to evaluate the anatomical fit of TKA implant design. The aims of this study were (1) to evaluate how to generate a valid SSM for implant design and (2) provide examples on how to use the SSMs to evaluate the anatomic fit of TKA components.
# Swarm Robotics: A Perspective on the Latest Reviewed Concepts and Applications
## 4 Swarm Robotic Projects
### Architectures and Frameworks  
Another important aspect of SR appliances is the architectures and frameworks. It is essential to understand the tools created to design and develop applications. From these aspects, we also need to understand the dataflow inside a proposed solution. These issues help to assess the real-time aspects, data processing, and data analysis payload processes within any project. In the context of SR, it is feasible to analyze the data in the Edge [82; 83], Fog [84; 85; 86], Cloud [87; 88; 89], or even combine these strategies [90]. Some examples of frameworks and architectures are:  
* **Aerostack** is an example of architecture and software framework developed for UAV/UAS SR applications [91]. The onboard application has modules to control real-time and non-real-time tasks. The collective architecture considers modules that target the most relevant tasks in the context of each SR unit.
* **ARCog** is a cognitive-based architecture designed to surface inspections in large scale [92]. The decision process happens through a supervising agent that attends solicitations from each unit throughout the execution time.
* **ALLIANCE** is a software architecture designed to facilitate the control of heterogeneous SR applications [93]. Internally, each unit has a set of high-level functions to perform designed tasks, using information from internal states, other robots, and environmental conditions.
* **CoMPACT** presented a hierarchical architecture to control UAV swarms [94]. This proposal combines mission-planning tasks with dynamic reassignment, motion planning, and swarm behaviors.
# Automated Conditional Statements Checking for Complete Natural Language Requirements Specification
## 4 Empirical Evaluation
### Descriptions of the Studies
#### 4.3.1 Study I: Selection of Sentence Embedding Methods  
The proposed approach clusters the conditional statements to group the similar statements. For this purpose, it utilizes the sentence embedding methods to obtain the distributed representations of each statement. There are several candidates for the sentence embedding methods. This study is to compare the performance of these candidates and select the better one for the conditional statements clustering and answer the research question RQ1. We observe the performance of these methods through clustering the conditional statements with the K-Means [43] clustering algorithm. The selected candidates for comparison are as follows:  
1. Word2 vec+TF-IDF: Word2 Vec [44] maps each word in the dataset into a vector, while TF-IDF [45] is a method which can generate a score for each word. In this case, the weighted representations of the conditional statements can be generated by combining the approaches of Word2 Vec and TF-IDF. The implementations of Word2 Vec and TF-IDF in Gensim library are used. The Word2 Vec is a pre-trained model from Google [46].
2. Doc2 Vec: Doc2 Vec [47] is an unsupervised algorithm that can learn a fixed-length feature representation from sentences. The implementation of Doc2 Vec in Gensim library is used.
3. Bert: Bert [48] is an unsupervised algorithm, and a deeply bidirectional system for pre-training NLP. The implementation of Bert in Anaconda is used.
4. Skip-Thought: Skip-Thought is an unsupervised algorithm which provides a general distributed sentence encoder. We downloaded the source code of Skip-Thought from GitHub [49], and used the encoder of the pre-trained model to generate the representations of conditional statements. When using skip-thought, the required python version is 2.7.  
To evaluate the performance of above sentence embedding methods, we take the measures of rand index (\(RI\)) [50] and mutual information (\(MI\)) which are often used for the evaluation of the clustering algorithms. They are defined as follows:  
\[RI=\frac{2(a+b)}{n(n-1)} \tag{4}\]  
\[MI(U,V)=\sum_{i=1}^{|U|}\sum_{j=1}^{|V|}\frac{\left|U_{i}\bigcap V_{j}\right|} {N}log\frac{N\left|U_{i}\bigcap V_{j}\right|}{\left|U_{i}\right|\left|V_{j} \right|} \tag{5}\]  
For rand index, the number of \(a\) in Equation (4) is the number of pairs of elements that belong to the same cluster in both the predicted results and the manually grouped results. In the meanwhile, the number of \(b\) is the number of pairs of elements that do not belong to a cluster in both the predicted results and the manually grouped results. The number of \(n\) is the number of elements to be clustered. The mutual information measures the mutual dependence between two sets. For the definition of Equation (5), \(U_{i}\) is the \(i\)-thcluster generated by the clustering algorithm, and \(V_{j}\) is the \(j\)-th cluster of the manually grouped results. The values of both measures are in the range of [0, 1]. The larger, the better.
## 3 Methods
### Physical Layer of OFDM Communication System
#### 3.1.3 M-QAM Modulation
The potential capacity of a system utilizing higher order M-QAM can be calculated by using [PERSON]'s channel capacity formula [60] as  
\[C=Blog_{2}\bigg{(}1+\frac{S}{N}\bigg{)}\text{ bits}/\text{s}, \tag{18}\]  
where \(C\) is the channel capacity in bits per second, \(B\) is the bandwidth of the channel in Hertz, \(S\) is the signal power, and \(N\) is the noise power. Using Equation (18) to express Shannon bound as bandwidth efficiency, \(\eta=\frac{C}{B}\), it can be expressed as:  
\[\eta=log_{2}\bigg{(}1+\frac{S}{N}\bigg{)}\text{bits}/\text{s}/\text{Hz}. \tag{19}\]  
In IEEE 802.11 ax (Wi-Fi 6) standard, 160 MHz is the maximum allocated channel width [61]. Considering that, we can calculate the bandwidth efficiency using Equation (19) for higher order M-QAM, as given in Table 2. It shows that with higher order M-QAM, the data rate increases significantly. Additionally, the table also shows PAPR and GP values for higher order M-QAM calculated in [55]. From the table, we can observe that XQAM exhibits higher GP compared to SQAM due to its irregular pattern; however, SQAM has higher PAPR compared to XQAM, due to its full lattice structure [55]. For the experiment we have considered higher order M-QAM (16-QAM to 1024-QAM) modulation with both SQAM and XQAM constellation design for the OFDM-based image communication system.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 6 Security Assurances and IoT Threat Model
### IoT Threat Model
#### 6.2.4 Threat and Attacks on Authentication and Authorization  
Password cracking is a typical attack that compromises authentic access to a network or a device such as IoT sensors. Software packages such as _aircrack-ng_ (a suite of packet monitoring and injection tools available in Kali Linux oS) could be utilized to monitor and capture WiFi-protected access (WPA) handshake packets to cause dictionary attack further to crack a WiFi password. WiFi attack occurs when a malicious user tries to take control of the network by intercepting the data packets over WiFi or ZigBee channels [12]. It is a type of MIM attack that compromises the integrity of the network. An example of such an attack is Skylet [12]. [PERSON] et al. [23] explain that other common attacks that compromise a WiFi network are evil twin access points, key reinstallation attacks, Address Resolution Protocol (ARP) spoofing attacks, and DNS spoofing. An untrusted sensor node in a network may request sensitive data from the coordinator, and vice versa. Network nodes may enforce access control lists to manage the access of resources by participating nodes in a network.
## 3 Research Methodology
### Correlation Parameters for Interorganizational Workflow
#### 3.1.2 Determination of The Correlation of Interorganizational Workflows  
It is difficult to determine the two parameters \(s_{ij}\) and \(c_{ij}\). To achieve this task, the Fuzzy Analytic Hierarchy Process (FAHP) is used in this paper. The BDSM for the interorganizational workflow  
Figure 4: Correlation of interorganizational workflow.  
Figure 3: The Binary Design Structure Matrix used to determine the correlations between the workflows of different organizations.  
correlation shown in Figure 3 is transformed into a numerical Design Structure Matrix (NDSM), as follows.  
Step 1: A pairwise comparative Fuzzy Consistent Matrix of downstream sensitivity is constructed for the baseline workflow processing of the organization. The baseline workflow is the part of the intraorganizational workflow of each organization, which is associated with other organizations.  
In this method, every element in the rows of the BDSM except the diagonal elements is used as a baseline workflow, and the other non-zero elements of the rows are pairwise compared to determine the importance of the elements to the baseline workflow. In Figure 5a, Xr denotes the baseline element of the \(r\)th row (\(\forall\)r = 1, 2,\(\cdots\),n). \(\{X_{i}\cdots X_{j}\}\) are the non-zero elements of the \(r\)th row (\(\forall\)i, j = 1, 2, \(\cdots\),n) and xij is Xi compared with Xj considering the importance of Xr, where xij = 1- xij. The principal eigenvectors of each Fuzzy Consistent Matrix, i.e., the n-dimensional weight vectors, are calculated. Then, the NDSM of the downstream sensitivity is constructed.  
Step 2: The pairwise comparative Fuzzy Consistent Matrix of the probability of change is constructed for the baseline workflow processing of the organization. This method is similar to that in Step 1, except that column comparisons are performed instead of row comparisons, as shown in Figure 5b. Lastly, the NDSM of the probability of change is constructed.  
Step 3: The final NDSM matrix, R, for the interorganizational workflow correlation is obtained by multiplying the elements corresponding to the NDSM of downstream sensitivity and the NDSM of the probability of change.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Analytical Comparison of the Models
#### 5.2.2 Cybersecurity Features  
Cybersecurity is another critical area. Table 4 highlights how security is built into each HSALA layer, while the three-layered model often relies on ad hoc or reactive measures.  
HSALA's integrated security approach ensures that cybersecurity is a foundational element of the application, reducing vulnerabilities and aiding in the compliance with regulations. The three-layered model's lack of integrated security can result in reactive measures that may not be sufficient to protect against modern threats.
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 3 Bottom-Up Fabrication of Periodic Structures on OFs
### Other Self-Assembly Methods
#### 3.2.1 Breath Figures
The metallo-dielectric pattern obtained by thermal evaporation of a thin gold layer on the top of the honeycomb film allowed the authors to develop an OF probe capable of detecting refracting index unit (RIU) variations with a sensitivity of up to 2300 nm/RIU, surpassing the performances of most analogue sensing probes fabricated by using more technically demanding techniques. The possibility of modulating the lattice period by controlling the casting conditions is also demonstrated. Figure 3: Breath figure approach to periodic pattern nanofabrication on the optical fiber. (**a**) SEM top view of the optical fiber decorated with the photonic–plasmonic pattern obtained by breath figure approach; (**b**) magnified SEM image; (**c**) AFM topography; (**d**) z profile along S line in (**c**); (**e**) schematic overview of the fabrication process on the optical fiber; (**f**) fluorescence microscopy image of the honeycomb film realized on the optical fiber, showing the highly ordered pattern on an extensive area (the darker round area delimitates the fiber facet). Reproduced from Ref. [75] with permission from the 2014 American Chemical Society.
## 1 Introduction  
Metallic buried pipelines sharing the same corridor as AC power lines are subjected to AC interference, which is potentially harmful to personnel and equipment [1, 2, 3]. Some of the most critical conditions for the pipelines' integrity are caused by transient phenomena, such as lightning or short circuits of the power lines [4, 5]. In particular, lightning strikes may cause discharge currents up to hundreds of kA for a duration of several microseconds [6]. In the last few decades, considerable efforts have been devoted to the numerical simulation of interference problems involving power lines and earth-return metallic conductors. The existing methodologies for the assessment of such phenomena are generally based either on transmission line theory [7, 8, 9, 10, 11] or a combination of finite element methodologies (FEMs) and circuital analysis [12, 13, 14, 15, 16]. In either case, however, fundamental requirements for such modeling tools include the capability of taking into account variations in the soil resistivity [17, 18], non-parallel pipeline-power line routings [19], and the presence of multiple metallic conductors, such as mitigation wires [20]. Finally, the majority of the currently employed numerical techniques are developed under the assumption of a sinusoidal steady-state [21, 22, 23]. Indeed, since the 2D FEM simulations of this kind of physical configuration require meshes that are both large and detailed [19], the adoption of time-matching procedures would result in substantial computational loads. In this work, a new methodology was developed for the FEM simulation of interference problems in transient conditions. The technique is based on a finite element analysis of the Laplace-transformed problem. Once the problem was solved in the Laplace domain for a set of complex frequencies, a numerical inversion technique was adopted to compute the time-response at any required time-instant. Unlike time-marching procedures, whose accuracy at a given instant depends on the previous ones, the Laplace transformation of the mathematical problem allows avoiding the dependence on the previous time steps [24]. Moreover, since the solution corresponding to each complex frequency in the Laplace domain is independent of the others, the proposed method easily lends itself to parallel implementations in high-performance computing facilities.