esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Discussion  
The dataset contains 70,720 tweets, split into training and testing sets following the 70-30 rule, with 70% of data allocated for training and the remaining 30% for testing. To assess the effectiveness of our chosen methodologies, we employed a set of performance metrics, including accuracy, precision, recall, F1-score, specificity (negative recall), and ROC_AUC. Below are the equations for the discussed performance metrics. The performance measures in percentage for all the sentiment classifications are shown in Table 1.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{5}\]  
The accuracy measure shows how many data points are correctly predicted.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\]  
Figure 8: Sample of the employed dataset after preprocessing.  
Figure 9: Positive and negative word cloud.  
The precision measure calculates the number of actually positive samples among all the predicted positive class samples.  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
Recall (or sensitivity) calculates how many test case samples are predicted correctly among all the positive classes.  
\[\text{F1-Score}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{8}\]  
The F1-score is the harmonic mean of precision and recall.  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{9}\]  
Negative recall (or specificity) computes how many test case samples are predicted correctly among all the negative classes.
## 5 Challenges and Future Research Direction
### Intelligent Flow Table Management  
In large scale networks, a large number of traffic flows are generated more often, which in turn require corresponding flow rules to be installed, and thus, consumed large storage space. Most of the existing solutions to address the storage concern have been tested on small scale networks, for example between (7-28 nodes and 7-43 links) [15]. Thus, these numbers are incomparable to a large number of devices in a large-scale network environment like Telco, Internet of a Thing (IoT). Machine learning (ML) techniques are quite effective in resource management (i.e., network components like switches and controller(s)). However, most of the ML techniques focus on flow classification, flow monitoring. Little research work focuses on predicting traffic flow for real-time application and best-effort traffic and decides on which traffic flow to be installed in advance. OpenFlow has built-in data collection, which stored information about flows such as packet count, byte count. This statistical information indicates the frequency of traffic flows. Therefore, it will be interesting to devise a scheme that may leverage on this build in data collection to devise a scheme that will further reduce the flowtable memory space usage and speed up flow matching rate possibly using fuzzy theory in the selection of frequently used flow rules to be placed in the flowtable.
# Functional Characterization of _Saccharomyces_ Yeasts from Cider Produced in Hardanger
## 2 Materials and Methods
### HPLC-UV Determination of Amino Acids and Biogenic Amines  
2.5.1. Reagents, Materials, and Standards for HPLC-UV Analyses  
Amino acids: L-tyrosine disodium salt hydrate (98%) (Sigma, Steinheim, Germany), L-asparatic acid (99,5%) (AppliChem, Darmstadt, Germany), L-serine (99%) (Sigma, Steinheim, Germany), L-leucine (98%) (Sigma, Steinheim, Germany), L-cysteine (99%) (Merck, Darmstadt, Germany), isoleucine (99.5%) (AppliChem, Darmstadt, Germany), L-phenylalanine (99.5%) (Sigma, Steinheim, Germany), L-asparagine (98%) (Sigma, Steinheim, Germany), L-lysine monohydrochloride (98%) (Sigma, Steinheim, Germany), L-glycine (99%) (Sigma, Steinheim, Germany), L-glutamine (99%) (Sigma, Steinheim, Germany), L-thyptophan (98%) (Sigma, Steinheim, Germany), L-arginine monohydrochloride (99.5%) (Sigma, Steinheim, Germany), L-alanine (99.5%) (AppliChem, Darmstadt, Germany), L-lysine monohydrochloride (99%) (Acros Organics, NJ, USA), L-proline (99.5%) (AppliChem, Darmstadt, Germany), L-glutamic acid (99%) (Sigma, Steinheim, Germany), L-valine (98%) (Sigma, St. Louis, MO, USA), L-methionine (99.5%) (Fisher Scientific, Geel, Belgium), threonine (99%) (Fisher Scientific, Geel, Belgium), L-hytidine monohydrochloride (99%) (VWR Chemicals, Leuven, Belgium).  
Biogenic amines: putrescine dihydrochloride (98%) (AppliChem, Darmstadt, Germany), cadaverine dihydrochloride (98%) (Sigma, Steinheim, Germany), histamine dihydrochloride (98%) (Alfa Aesar, Karlsruhe, Germany), tyramine hydrochloride (98%) (Alfa Aesar, Karlsruhe, Germany).  
Other reagents and chemicals: hypochloric acid (Gram-Mol, Zagreb, Croatia), sodium hydroxide, sodium hydrogen carbonate and ammonia were purchased from (Sigma, Steinheim, Germany), ethanol, acetonitrile (HPLC grade) (J.T. Baker, Gliwice, Poland), sodium acetate (Carl Roth, Karlsruhe, Germany). Derivatization reagent dansyl chloride was supplied from Sigma (Steinheim, Germany).  
Chemicals were prepared with ultrapure water, which was prepared using a Milli-Q water purification system Purelab Option-Q system (ELGA LabWater, High Wycombe, UK) to a specific resistance of \(>\)18.0 M\(\Omega\) cm\({}^{-1}\) at 25 \({}^{\circ}\)C.  
A mix stock standard solution of 19 amino acids and a mix stock solution of 4 biogenic amines (1000 mg/L) were prepared in 0.1 M HCl. The solutions were stirred in an ultrasonic bath for 5 min.
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs
### Architecture Overview
#### 3.1.1 Vehicle and Network Layer  
This layer encompasses two primary elements, as shown in Figure 4:  
Figure 3: Blockchain subsystem for the proposed security solution.  
* Vehicle Node: Vehicles are furnished with an onboard unit (OBU) containing advanced communication devices, wireless transmission modules, and a trusted execution environment (TEE). These vehicles have adequate computational power to perform rudimentary calculations, such as road condition monitoring and trust evaluation based on received data [74; 75]. Furthermore, they can partake in blockchain consensus mechanisms and execute queries on the blockchain.
* RSU: RSUs facilitate communication among vehicle nodes within their operational domain. They are endowed with significant computational power, networking capabilities, and ample storage, which are all bolstered by a TEE.
## 1 Introduction  
Metasurfaces play an important role in the manipulation of amplitude [1, 2] phase [3, 4] and polarization [5, 6] of electromagnetic wave. Reflectarray and transmitarray antennas [7, 8, 9, 10] based on metasurfaces possess the advantages of high gain, low mass and simple feed. Compared with the reflectarray antenna, transmitarray antenna eliminates the shortcoming of feed blockage. However, the focal length of transmitarray antenna increases its own volume.  
Folded transmitarray antenna (FTA) [11, 12, 13] based on path multiple reflection provides a feasible solution to obtain low profile. Nevertheless, it's a challenge for FTA to achieve wideband operation. Moreover, the choice of feed antenna is also a critical factor for FTA design. Undoubtedly, choosing a wideband horn antenna will inevitably increase the volume of FTA. Alternatively, the co-planar integrated design of feed antenna can realize compact structure and reduce the profile of FTA. However, the bandwidth of planar antenna is usually limited. Therefore, there are few compact FTAs in the published literature. It is pivotal to design an efficient FTA with integrated wideband planar feed antenna.  
Circularly polarized electromagnetic wave has good characteristics of anti-Faraday rotation and anti-interference [14, 15], and is widely used in satellite communication system. Circularly polarized folded transmitarray antenna (CPFTA) [16, 17] can meet the requirements of high gain, light weight and compact structure. It has great potential applications in satellite communication and point-to-point communication. Compared with FTA, the design difficulty of CPFTA increases significantly.  
Stealth technology has attracted the attention of many countries. Radar cross section (RCS) is an important criterion to evaluate stealth performance. RCS reduction of antenna has always been a research hotspot [18, 19, 20]. Furthermore, it is meaningful to improve the radiation and scattering performance of the antenna simultaneously [21, 22, 23]. CPFTA has great potential applications in military fields, so it is valuable to design it with the integrated control of radiation and scattering. To the author's knowledge, there is no published literature on the integrated radiation and scattering performance of CPFTA. We aim to design a high-gain, low-profile, and low-RCS transmitarray antenna, which has application in mobile satellite communication.  
In this paper, the novel circularly polarized folded transmitarray antenna (NCPFTA) with integrated radiation and scattering performance is proposed and successfully proved by experiments. Compared with general circularly polarized folded transmitarray antenna (GCPFTA), the novel design can improve the axis ratio (AR) bandwidth significantly and reduce RCS simultaneously without adding any additional structure. The NCPFTA has the advantages of broadband, high gain, planar structure, low profile, convenience in installation and low RCS.
## 2 System Model
Therefore, the signal of \(m\)th cluster which is transmitted from every antenna of the BS, is described as follow in Equation (5):  
\[\mathbf{x}_{S,m}=\mathbf{w}_{m}\sum_{n=1}^{N}\sqrt{a_{m,n}P_{\text{S}}}x_{m,n}. \tag{5}\]  
Let \(n_{m,n}\sim\mathcal{CN}(0,\sigma_{m,n}^{2})\) be an i.i.d additive white Gaussian noise (AWGN) at the \((m,n)\)th user. In the case of perfect precoding, the received signal of the \((m,n)\)th user includes the signal of all users in the \(m\)th cluster in Equation (6):  
\[y_{m,n} =\mathbf{h}_{m,n}\mathbf{w}_{m}\sum_{i=1}^{N}\sqrt{a_{m,i}P_{ \text{S}}}x_{m,i}+n_{m,n}\] \[=\underbrace{\mathbf{h}_{m,n}\mathbf{w}_{m}\sqrt{a_{m,n}P_{\text{ S}}}x_{m,n}}_{\text{desired signal of }(m,n)\text{th user}}\ \ \underbrace{+\mathbf{h}_{m,n}\mathbf{w}_{m}\sum_{i=n+1}^{N}\sqrt{a_{m,i}P_{ \text{S}}}x_{m,i}}_{\text{interference from the other users}}+\underbrace{\mathbf{h}_{m,n} \mathbf{w}_{m}\sum_{k=1}^{n-1}\sqrt{a_{m,k}P_{\text{S}}}x_{m,k}}_{\text{ interference due to imperfect SIC}}+n_{m,n}. \tag{6}\]  
The term \(\mathbf{h}_{m,n}\mathbf{w}_{m}\sum_{k=1}^{n-1}\sqrt{a_{m,k}P_{\text{S}}}x_{m,k}\) is equal to zero in the case of perfect SIC. As mentioned above, although the ZFBP can remove the inter-cluster interference, the intra-cluster interference meaning the interference between the users which are located in the same cluster, also called IUI, is still remained. Hence, we propose to apply the SIC technique to cancel the IUI. Owing to SIC principle, at receiver site, the interference of users whose channel gain is worse is deleted. According to above assumption of order, the \((m,n)\)th user should remove the interference of the \((m,1)\), \(\ \cdots\,\ (m,n-1)\)th users. Let the signal to interference plus noise ratio (SINR) be denoted by \(\gamma\), and the SINR of \((m,n)\)th user is \(\gamma_{m,n}\), from Equation (6) we have Equation (7):  
\[\gamma_{m,n}=\frac{P_{\text{S}}a_{m,n}|\mathbf{h}_{m,n}\mathbf{w}_{m}|^{2}}{ \sum\limits_{i=n+1}^{N}P_{\text{S}}a_{m,i}|\mathbf{h}_{m,n}\mathbf{w}_{m}|^{2 }+\sigma_{m,n}^{2}}. \tag{7}\]  
Because the \((m,N)\)th user apply the SIC processing to remove the interference signals of all users in the same cluster, the SNR of \((m,N)\)th user is represented by Equation (8):  
\[\gamma_{m,N}=\frac{P_{\text{S}}a_{m,N}|\mathbf{h}_{m,N}\mathbf{w}_{m}|^{2}}{ \sigma_{m,N}^{2}}. \tag{8}\]
## 2 Overview of Blockchain Technology
### Different Forms of Blockchain
#### 2.6.3 Consortium Blockchain  
A hybrid strategy that brings together the advantages of both private and public blockchains is referred to as the consortium blockchain. Thus, it is partially centralized and permissioned for consensus. They have lower integrity compared to public blockchains, while the data access can be public or restricted. Consortium blockchains also have high scalability and efficiency as they are controlled by an organization [151].  
Table 1 summarizes protocols/models/languages for achieving different functions in each plane of the blockchain framework.  
\begin{table}
\begin{tabular}{c c c} \hline
**Plane** & **Function/Purpose** & **Protocols/Models/Approaches/Frameworks/Examples** \\ \hline \multirow{8}{*}{Network} & Proof-based consensus & PoW [96], PoS [97], PoA [98], PoSp [99], PoB [100], PoAu [101], PoET [102] \\ \cline{2-3}  & Vote-based consensus & PBFT [103], DBFT [105], Raft [106] \\ \cline{2-3}  & Hybrid consensus & DPoS [107] \\ \cline{2-3}  & Scripts & TBAC [114] \\ \cline{2-3}  & P2P communication & Bitcoin protocol [111], Ethereum wire protocol [112], IPFS [113] \\ \cline{2-3}  & Network attacks & DoS attacks, routing attacks, domain name service attacks, eclipse attacks [142] \\ \hline \multirow{8}{*}{Data} & Architectures & Linear [3], Directed acyclic graph [4] \\ \cline{2-3}  & Types & Private [149], Public [150], Consortium [151] \\ \cline{2-3}  & Cryptography & Hashing [82], Public key cryptography [80], Zero-knowledge proofs [84], Post-quantum cryptography [87,88,90], Hybrid cryptography [86,89], fault-tolerant ciphers [91,92,93,94,95] \\ \cline{2-3}  & Endpoint attacks & 51\% vulnerability, cryptojacking [143] \\ \hline \multirow{8}{*}{Application} & Smart contracts & Contractual automation [116], access control [117] \\ \cline{2-3}  & Cryptocurrency & Bitcoin [2], Ethereum [81] \\ \cline{2-3}  & Supply chain & Cold chain management [121] \\ \cline{2-3}  & Networking & TRUCON [123], IoTChain [124], UASTrustChain [125] \\ \cline{2-3}  & Healthcare & Drug counterfeiting [127], Medblock [128], MedShare [129] \\ \cline{2-3}  & Insurance & ClaimChain [130], Insurance fraud protection [131], Decentralized reinsurance [132] \\ \cline{2-3}  & Voting & PriScore [133], Auditable voting [134] \\ \cline{2-3}  & Intellectual property & IP protection [135], IP rights management [136] \\ \hline \end{tabular}
\end{table}
Table 1: Summary of protocols/models/approaches/frameworks/examples in each plane of the blockchain framework.
# Feasibility of LoRa for Smart Home Indoor Localization
## 2 Literature Review
### Ultra-Wideband (UIWB)  
The UWB technology has seen rapid development in the communication sector [6]. The UWB has gradually received attentions as a solution for indoor localization because of its immunity to interference from other signals and its excellent penetration of numerous materials [62]. UWB was tested in a corridor with a width of 2.5 m to mimic the location-awareness during museum visiting [63]. UWB tags are attached to each painting, and users carry a phone with UWB for positioning. The results have presented UWB has a sub-meter accuracy in the LOS scenario. Yet, the research group mentioned that NLOS conditions would seriously deteriorate the measurable range and accuracy. Furthermore, [PERSON] et al. [26; 64] proposed an NLOS identification system using multipath channel statistics in UWB packets. This study reports NLOS bias less than 2 m when applied in a rectangular room of size 30 m \(\times\) 20 m. However, information about obstacles causing the NLOS in this experiment are thoroughly explained. It is important to know that the accuracy and applicable range of UWB and other sensing technologies can be greatly impacted by metallic or thick objects in smart homes that interfere with UWB signals.  
Since UWB localization systems have high accuracy, some home-entertainment applications use this technology for precise positionings, such as the smart audio system that adopts UWB to adjust the sound according to users' location and provide an optimum listening experience [6]. The researchers used one omnidirectional transmitting and four directional receiving antennas to detect the small movements of listeners' chest in real-time. Moreover, the experiment validated the precise UWB localization system needs a very stable implementation environment with low jitter and high signal to noise ratios. As discussed above, although UWB has distinctive advantages of accurately localizing objects, there are several defects that block the implementation of UWB in the smart home localization [17]. First, the infrastructure cost of UWB is higher than other systems [62]. Furthermore, when the metal objects, such as common home appliances, hinder the propagation path, UWB has a poor reception range and accuracy.
## 2 Materials and Methods
### Empirical Temperature-Based Models
#### 2.3.1 [PERSON]'s Model  
In 1982, [PERSON] and [PERSON] (HS) proposed a linear relationship between the square root of the temperature difference and the fraction between the extraterrestrial and terrestrial global solar insolation for different periods, as follows:  
\[\frac{H}{H_{0}}=a(T_{max}-T_{min})^{0.5}\]  
where \(a\) is the empirical coefficient, \(H\) is the global solar insolation on a horizontal surface, and \(H_{0}\) is the daily extraterrestrial solar insolation calculated as follows.  
\[H_{0}\Big{(}\frac{Wh}{m^{2}day}\Big{)}=\frac{24}{\pi}\times I_{c}\times\Big{(} 1+0,033*cos\Big{(}\frac{360*D}{365}\Big{)}\Big{)}\times(cos(\varphi)\ \times\ cos(\delta)\ \times\ sin(\omega_{s})\ +\ \frac{\pi}{180}\times\omega_{s}\times sin(\varphi)\ \times\ sin(\delta)).\]  
This model, however, did not consider the effects of cloudiness, humidity, latitude, elevation, or topography, among others, in the specific location for which the model is used [33,34]. [PERSON] stated that the HS model demonstrates better behavior in a monthly time frame than in a daily time frame because the variables follow a mean trend, resulting in a consistent relationship between \(T_{max}-T_{min}\) and \(H/H_{0}\)[35].  
The empirical coefficient represents the rate of change from the maximum and minimum temperature difference with the ratio between the extraterrestrial and terrestrial solar insolation. Initially, the HS model proposed an empirical coefficient calibrated with an eight-year time series from Central Valley in Davis County, California (see Table 3) [4,9]. They found that a relative humidity above 54% affects the empirical coefficient, and that the maximum and minimum temperature difference is smaller when it is nearly this percentage. As shown in Table 3, in humid regions, the empirical coefficient has a value less than 0.10 while other regions have values higher than 0.16 [9]. A thorough process uses empirical coefficients according to the region under study. Accordingly, [PERSON] et al. pointed out that using a fixed empirical coefficient for a large area with different regions may result in significant errors because topography influences temperature, the advective environment, and vegetation [15].
## 4 Decision Engine for Single Link
The probability \(p\) in Equation (19) denotes the conditional probability that a codeword fails in state \(s_{1}\) given that it failed in \(s_{0}\), and can be computed using Equation (1) as  
\[p=\frac{P_{e}(R_{1},\text{SNR}_{1})}{P_{e}(R_{0},\text{SNR}_{0})}. \tag{20}\]  
For any state \(s\) and \(\text{SNR}_{1R}\), the total expected future cost \(V\) and the optimal action \(A\) can be expressed recursively as follows:  
\[V(s,\text{SNR}_{1R}) =E[\text{Total cost}|s,\alpha,\beta]\] \[=b\alpha+\beta+f_{C}+c_{R}+\sum_{s^{\prime}}P(s^{\prime}|s,\alpha,\beta)V(s^{\prime},\text{SNR}_{1R}) \tag{21}\] \[A(s,\text{SNR}_{1R}) =\arg\min_{(\alpha,\beta)}V(s,\text{SNR}_{1R}), \tag{22}\]  
where the summation is taken over all possible states \(s^{\prime}\) to which \(s\) can transition according to Equations (17)-(19) given that \((\alpha,\beta)\) IR bits are sent. \(P(s^{\prime}|s,\alpha,\beta)\) denotes the state transition probability. If we discretize the states and actions to take values from a finite set, the value iteration algorithm [49] can then be used to numerically find \(V(s,\text{SNR}_{1R})\) and \(A(s,\text{SNR}_{1R})\) for all \(s\) and \(\text{SNR}_{1R}\). Essentially, value iteration initializes \(V\) with random values, and alternates between finding the optimal actions \(A\) according to Equation (22) and updating the value \(V\) according to Equation (21), until convergence. At that point \(A(s,\text{SNR}_{1R})\) stores the optimal policy to follow when the HARQ process is at state \(s\) expecting \(\text{SNR}_{1R}\) for the IR, while \(V(s,\text{SNR}_{1R})\) stores the total expected future cost until successfully decoding all codewords in the bundle at the receiver. The single link scenario decision engine is specified by the policy \(A\), and it can be readily extended to individual links in a multi-hop scenario as well. The receiver can estimate its state by computing the bundle's relevant statistics when decoding failures occur, and it then follows \(A\) to request a combination of \((\alpha,\beta)\) IR bits from the transmitter.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 2 Open Technological Issues  
### Vision Sensors and Computer Vision Algorithms  
Three-dimensional visual data acquisition is the first challenge of the bin-picking problem chain. For this purpose, various technologies can be used: the most common are 3D laser scanners [11], structured light vision systems, consisting of a projector and one or two cameras [12] and stereo vision systems [13]. In bin-picking applications, the choice of the applied sensor for object registration is strongly correlated with failures, as discussed in [14]. When designing a robotic bin-picking workcell, a choice has to be made not only on the sensor's technology, but also on the vision sensor placement: it can be attached directly to the robot end effector or placed on a separate fixed camera stand.  
Once the 3D point cloud map has been acquired, sophisticated computer vision algorithms are required for object detection and pose estimation, taking into account factors such as sensor noise, changing lightning conditions, shadows, and reflectance properties.  
Most research on computer vision algorithms for bin-picking tasks has focused on Deep Learning Algorithms, in particular Convolutional Neural Networks and Deep Reinforcement Learning [15]. These approaches generally require 3D models of objects during testing or physical objects during training. Furthermore, they are very often supervised networks, and labeling is an indispensable stage for training the network and teaching where to grab an object based on its 3D model or its geometry. Therefore, these techniques do not scale easily for applications that frequently present objects that have never been seen before [16].  
In the last decade, several manufacturers, such as Fanuc, Keyence, Cognex or Mech-Mind, developed their own ready-to-use 3D vision sensors with integrated computer vision algorithms for bin-picking. These systems are usually easy to install and set up, but it is not possible to customize the computer vision algorithms or access directly to the acquired 3D map.