esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Collection and IoT
#### 3.1.2 Internet of Things and Wireless Sensor Networks  
Solutions based on IoT technologies and WSNs can provide useful for PA. [PERSON] et al. propose a solution for optimized irrigation, which utilizes various sensors (including soil moisture probes, soil temperature sensors, ambient temperature sensors, humidity sensors, CO\({}_{2}\) sensors, and light dependent resistors) to acquire real-time measurements. Based on these measurements, the precise management of a water valve is achieved using neural network-based prediction of the soil water requirements [21]. [PERSON] et al. propose an IoT-based low cost network platform for sensors and actuators. This platform integrates machine to machine (M2M) and human-machine interface (HMI) protocols. Edge computing technologies based on the aforementioned multi-protocol approach are also used to develop control processes for several PA scenarios. The experimental results  
Figure 3: Stages of the present literature review.  
regarding this platform indicated several benefits (e.g., reduction in costs and energy requirements, increased acceptance by agricultural specialists) [22]. [PERSON] et al. propose a cloud IoT-based (further information about cloud computing can be found in the following subsection) solution for disease prevention in PA. In this solution, sets of temperature and humidity sensors send their measurements to a cloud platform through a gateway. These measurements are stored and analyzed in the cloud platform, and a decision-support system notifies the farmer through short message service (SMS) when the first attack of the plant disease takes place. Some of the benefits of this solution include increased efficiency, cost reduction, and minimization of the environmental impact by estimating the exact fungicide quantities required [23]. [PERSON] et al. present an autonomous leaf-mounted radio-frequency identification (RFID) wireless temperature sensor for PA. With a weight less than 3 grammars, this sensor can be installed on a leaf to measure leaf-to-air differential temperature. This measurement is used to monitor the water stress levels of plants. The energy requirements of the sensor are covered by a flexible solar panel, and the wireless read range is approximately 3 m. A field evaluation of the proposed sensor proved their capability of discriminating water level variations in plants and thus their suitability for PA [24].
# Prediction of Walnut Deterioration Using Kernel Oxidative Stability
## Materials and Methods
### Oxidative Stability Index  
The oxidative stability index (OSI) was measured using a Rancimat apparatus 617 (Metrohm AG, Herisau, Switzerland). Samples were placed into test tubes, and air was bubbled inside at a controlled flow in block heated at a controlled temperature. Effluent air containing volatile organic acids from the samples was collected in a measuring vessel containing double distilled water (50 mL). The conductivity of the water was automatically measured as oxidation proceeded. For kernel oxidative stability, 0.5 kg of walnuts were cracked and grounded using a Cuisinart Mini-Prep Plus(r) processor (East Windsor, NJ, USA), model DLC-2A. A sample from those ground walnuts kernels (0.5 g) was weighed into test tubes and oxidized under the testing conditions. Filtered, cleaned, and dried air bubble through the kernel samples at the rates of 15, 20, and 25 L h\({}^{-1}\). OSI is the time (in hours) taken until a sharp increase in conductivity occurs, which is determined by the intersection of the baseline with the tangent to the conductivity curve [17]. The OSIs of the samples were recorded at 110, 120, and 130 \({}^{\circ}\)C. In each run, eight samples were randomized on their position in the heating block and analyzed simultaneously. For oil oxidative stability, oil samples (3 g) were weighed into test tubes and oxidized under the testing conditions of 110 \({}^{\circ}\)C with 20 L h\({}^{-1}\) of airflow following the instrument manufacture recommendations.
## 4 Results
### RQ5: Network Security Attack in IoT and WSN Layers
#### 4.5.4 Black Hole Attacks  
This is a network layer attack known as a packet drop attack. In this attack, a node sends an RREQ packet to all its neighbors in the network, and the router is supposed to forward the packet instead of discarding it. The nature of this attack is similar to a DDoS attack. Attackers are used to attack routers by sending many false requests to prevent legitimate routers from forwarding packets. This is also called a first-come, first-served attack because the attacker can also use a malicious router or reprogram it to block packets instead of sending correct information [78]. These attacks reduce the average throughput. When combined with a sinkhole attack, this attack affects performance and stops all traffic around the black hole. When combined with the sinkhole attack, this attack severely degrades traffic and modifies or discards content during transmission.
# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 5 Hybrid User-Based Methods
### Behavioural Collaborative Filtering  
Some researchers have assumed that _if the users shared similar hashtags/topics in the past_, _most likely they will share similar hashtags/topics in the future_. Following this assumption, [PERSON] et al. [60] and [PERSON] et al. [10] proposed personalized hashtag recommendation models in the following manner: they created a user-hashtag matrix and assigned 1 if a user has adopted a specific hashtag in the past. Using an optimization method, they generated a hashtag adoption score of every user to every possible hashtag. To address the data sparseness problem, which happens when the numbers of hashtags and users increase, they updated the rates of using hashtags from random tweets sampled at certain times. They, however, used different sampling techniques. The previously mentioned methods suffer from the cold-start problem.  
Content-based methods can remedy the cold-start problem that arose from the collaborative filtering methods. [PERSON] et al. [61] proposed a personalized hashtag recommendation model that involved integrating content similarity and collaborative filtering to extract candidate hashtags. This method profiles every user according to his/her historical hashtag usage using TFIDF. Given a user, hashtags extracted from like-minded users and similar tweets were retrieved, ranked, and recommended. [PERSON] et al. [66] recommended hashtags based on the weight of combining three features: content similarity, collaborative filtering of users with similar hashtag usage and topical interests.  
To overcome the cold-start problem, in [PERSON] et al.'s paper [63], the recommended hashtag for a given tweet was extracted from the tweets of users sharing similar topics. They generated the topic representation of a tweet by training the LDA model on aggregated tweets containing the same hashtag. [PERSON] et al. [64] adopted a similar approach and combined it with users using similar historical hashtags if present in the database. They found that a smaller number of users is required to get a high hashtag recommendation performance.
###### Abstract  
This study introduces a new design for an ultra-compact shared-aperture antenna utilizing a quarter-mode substrate integrated waveguide (QMSIW) cavity. The proposed antenna operates as a 4 \(\times\) 4 multi-input multi-output (MIMO) system in three 5G/6G millimeter-wave (MMw) bands, while functioning as a single element antenna for a 5.5 GHz wireless fidelity Microwave (Mw) band. The antenna comprises four QMSIW cavity resonators; each QMSIW is loaded with dual slots to produce tri-band MMw operation at 28 GHz, 38 GHz, and 0.13 THz. The four cavities are arranged to reuse the entire aperture by creating a conventional open-loop antenna that operates at a frequency of 5.5 GHz. Simulation, measurement, and co-simulation results show that the proposed antenna has a quad-band operation and exhibits favorable characteristics. The measured scattering parameters validate the simulated ones over the four bands under consideration. The lowest values of the measured total radiation efficiencies are 80%, 73%, 62%, and 72% (co-simulation) within the four covered bands, respectively. The antenna peak gains are 1.8 to 1.85 dBi, 4.0 to 4.5 dBi, 4.3 to 4.5 dBi, and 6.5 to 6.6 dBi within the covered bands. Furthermore, the design satisfies MIMO and diversity conditions (envelope correlation coefficient and branch power ratio) over frequency bands of operation. All excellent results are achieved from an ultra-compact size in terms of footprint area (\(0.018\lambda_{0}^{2}\)), where \(\lambda_{0}\) represents the free space wavelength at 5.5 GHz. The antenna boasts an excellent reuse aperture utilization efficiency (RAU) of 92% and a large ratio frequency of 23, making it an ideal candidate for compact devices. With its superior performance, the proposed design is well-suited for a range ofs wireless communication systems, including mobile devices and the Internet of Things.  
5G antennas; 6G antennas; shared-aperture antennas; SIW-based antennas; dual-mode composite antennas; sub-mode cavity antennas; MIMO 2023
# A Forward\(-\)Backward Splitting Equivalent Source Method Based on S\(-\)Difference
## 2 Forward-Backward Equivalent Source Method Based on S\(-\)Difference
### Forward-Backward Splitting (FBS) Algorithm
The specific steps are as follows:  
(1) Input transfer matrix \(\mathrm{G}\) and \(\mathrm{A}\) of equivalent source model;  
(2) Measure the sound pressure column vector \(\mathrm{p}(M\times 1)\), and the equivalent source intensity column vector \(\mathrm{q}(N\times 1)\);  
(3) Through the \(\mathrm{L}-\)curve method [22], select appropriate regularization parameters, use Tikhonov to regularize the optimal solution formula: \(\mathrm{q}_{Tik}=\left[\mathrm{G}^{H}\mathrm{G}+\lambda\mathrm{I}\right]^{-1} \mathrm{G}^{H}\mathrm{p}\), calculate its equivalent source strength, recorded as \(\mathrm{q}_{Tik}\), and WBH calculates the equivalent source intensity, which is recorded as \(\mathrm{q}_{\mathrm{tr}}\);  
(4) Set up \(\mathrm{q}_{k}=\mathrm{q}_{0}=\mathrm{q}_{Tik}\), \(\Delta D=10\), \(\varepsilon=10^{-6}\), \(\rho=[0.05\times\max(|\mathrm{q}_{\mathrm{tr}}|)\), \(0.5\times\max(|\mathrm{q}_{\mathrm{tr}}|)]\);  
(5) Initial truncation condition: \(trun\_x=\max(|\mathrm{q}_{Tik}|)/\Delta D\); if it is greater than the truncation value, \(\mathrm{q}_{k}^{s}=\mathrm{q}_{k}\), and if it is less than the truncation value, \(\mathrm{q}_{k}^{s}=0\);  
(6) Initialization: \(k=1\), negative gradient vector \(w_{k}=\mathrm{G}^{T}(\mathrm{G}\mathrm{q}_{k}-\mathrm{p})\), step \(\tau=w_{k}^{T}w_{k}/\left((\mathrm{G}w_{k})^{T}(\mathrm{G}w_{k})\right);y_{k}= \mathrm{q}_{k}-\beta w_{k}\);  
(7) Calculate the median cutoff value \(trun\_y=size(find(|y_{k}|\geq\min(trun\_x,\delta)))\); if it is greater than the truncation value, \(\mathrm{q}_{k+1}=y_{k}\), and if it is less than the truncation value, \(\mathrm{q}_{k+1}=sign(y_{k})\centerdot\max(|y_{k}|-\rho\beta,0)\);(8) Calculate output truncation value \(trun\_z=size(find(|\alpha_{k}|\geq\min(trun\_y,\delta)))\); if it is greater than the truncation value, \(\alpha_{k+1}^{s}=\alpha_{k+1}\), and if it is less than the truncation value, \(\alpha_{k+1}^{s}=0\);  
(9) If \(|F(q_{k+1})-F(q_{k})|/F(q_{k})\geq\varepsilon\) or \(k>k_{\max}\), finish;  
(10) \(\alpha_{k}=\alpha_{k+1}\), \(\alpha_{k}^{s}=\alpha_{k+1}^{s}\), \(trun\_x=trun\_z\), \(k=k+1\); output the best equivalent source intensity value and end the cycle;  
(11) Output reconstructed surface sound pressure: P\({}_{R}=\) Aq\({}_{k+1}\);  
Input: transfer matrix G and A of equivalent source model. Output: the best equivalent source intensity q value.
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture
### High-\(k\) Dielectric and Metal Gate (HKMG)
#### 3.6.1 Evolution of HKMG  
New high-\(k\) dielectric and metal gate were initially introduced into 45 nm technology node MOSFET by Intel Corporation at 2007 [65]. Table 3 summarizes the evolution of high-\(k\) dielectric and metal gate from 45 nm to 14 nm nodes. It can be seen that the HfO\({}_{2}\) is used as gate dielectric for both nMOS and pMOS. The HfO\({}_{2}\) high-\(k\) dielectric has high permittivity (a dielectric constant about 25 and a relatively large bandgap, 5.7 eV), large heat of formation (\(-271\) kcal/mol, higher than that of SiO\({}_{2}\): \(-218\) kcal/mol), good thermal and chemical stability on silicon, large barrier heights at interfaces with Si, and adequate compatibility with \(n+\) poly-silicon gate electrodes. At an operation voltage of 1-1.5 V, the leakage current through HfO\({}_{2}\) dielectric films was reported to be several orders of magnitude lower than that of SiO\({}_{2}\) with the same EOT (0.9-2 nm) [39; 65]. One of the main challenges of HfO\({}_{2}\) application in CMOS technology is the thermal instability of HfO\({}_{2}\)/Si contact. There is an inevitable interlayer such as SiO\({}_{x}\) between HfO\({}_{2}\) and Si substrate [65; 66; 67], even though the HfO\({}_{2}\)/Si is theoretically found to be thermodynamically stable [68]. Table 3 summarizes information about different materials as high-\(k\) dielectric and metal gate from 45 nm to 14 nm technology node. The table also shows that TiAlN and TiN are applied as metal gates for nMOS and pMOS, respectively.  
Figure 10: HRRLM at (\(-115\)) reflection from 14 nm FinFETs arrays with of SiGe in S/D areas. Reproduced with permission from [50]. Copyright Solid-State Electron, 2016.
## 2 Related Work
In [48], the authors focused on detecting DDoS attacks on SIP-based VoIP networks by using a deep packet inspection (DPI)-based method. The proposed approach was based on analyzing packets to extract attack signatures and implement new detection rules. That approach was different from ours in that our approach specifically targets the SIP-based DRDoS attack. Furthermore, the response method was not mentioned for the proposed approach, and it was not mentioned whether it had the real-time capability to respond. Our approach has specialized modules for the statistical analysis and packet inspection to detect attacks, and it has the real-time capability to drop attack packets. Additionally, our approach focuses specifically on DRDoS attacks while the related work targeted general DDoS attacks. In [49], deep forest models and service differentiation were used to filter out general DRDoS attack flows. Unlike that work, which targeted general DRDoS attacks, our approach focuses specifically on SIP-based DRDoS attacks and utilizes a combination of statistical analysis, packet inspection, and real-time action to detect and defend against these attacks. Our proposed method is able to effectively identify DRDoS attacks with a higher detection rate and a lower false alarm rate, while also effectively eliminating attack flows and mitigating the damage caused by these attacks. A survey of countermeasures against DoS/DDoS attacks on SIP-based VoIP networks was given with [50]. Recently introduced approaches for detecting such attacks were classified by analyzing their strengths and weaknesses. While an overview was given on various techniques for countering general DoS/DDoS attacks on SIP-based networks, the SIP-based DRDoS attack was not specifically investigated. In our previous studies, we investigated defense mechanisms against SIP-reflection-based DDoS and DRDoS attacks and their impact on an SIP network [14; 15]. In [15], we proposed an effective defense mechanism to combat SIP-reflection-based DDoS attacks. In [14], we showed how an SIP network could be attacked successfully by using the DRDoS attack. Furthermore, we suggested a defense approach to alleviate the effect of the proposed SIP-based DRDoS attack. In this current study, we further improve upon our previously suggested defense approach and provide a more detailed implementation of our defense mechanism. We demonstrate the effectiveness of our proposed defense mechanism by conducting an SIP-based DRDoS attack on an actual VoIP network and show its ability to successfully mitigate the attack. In Table 1, we compare our study against the existing approaches in the literature, in terms of their attack types, detection methods, response methods, and real-time capabilities.
## 2 Problem Statement
### UAV Model  
We consider a solar-powered UAV that flies in an urban environment. We denote \(p(t)=[x(t),y(t),z(t)]\) as the coordinates of the UAV at time \(t\). The following model is used to describe the UAV's motion (This 3D UAV motion model is extended from a 2D model [27] where a forward speed and an angular speed are the control inputs. Beyond these two control inputs, we consider the vertical speed such that the UAV can adjust its altitude):  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Meaning** \\ \hline \(p(t)\) & Position of the UAV \\ \hline \(v(t)\) & Linear speed on \(xy\)-plane \\ \(w(t)\) & Angular speed on \(xy\)-plane \\ \(u(t)\) & Vertical speed \\ \(\theta(t)\) & Heading of the UAV on \(xy\)-plane \\ \(D(t)\) & Drag of the UAV \\ \(f(t)\) & Thrust of the UAV \\ m & The mass of the UAV \\ \(p^{sun}\) & Energy harvesting power of the UAV \\ \(P(t)\) & Energy consuming power of the UAV \\ \(Q(t)\) & Residual energy of the UAV \\ \(\mathcal{T}\) & The random tree \\ \(\mathcal{R}\) & The set of paths in \(\mathcal{T}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Symbols and meanings.  
\[\left\{\begin{array}{ll}\dot{x}(t)=v(t)\cos(\theta(t)),\\ \dot{y}(t)=v(t)\sin(\theta(t)),\\ \dot{\theta}(t)=w(t),\\ \dot{z}(t)=u(t),\\ \dot{u}(t)=\frac{F(t)-D(t)}{m}.\end{array}\right. \tag{1}\]  
where \(\theta(t)\) is the heading of the UAV with respect to the \(x\)-axis; \(v(t)\), \(\omega(t)\) and \(u(t)\) are its linear horizontal, angular and vertical speeds, respectively; m is the mass of the UAV; and \(D(t)\) and \(F(t)\) are the drag and thrust forces, respectively. The model (1) and its slight modifications have been widely used to describe the motion of aircraft, wheeled robots, and missiles, see, e.g., [27; 28].  
The drag force is computed as follows [29]:  
\[D(t)=\frac{1}{2}\rho C_{D}Av^{2}(t), \tag{2}\]  
where \(\rho\) is the air density, \(A\) is the area of the solar cells of the UAV, \(C_{D}=C_{D0}+\frac{C_{D}^{2}}{\varepsilon\pi R_{a}}\) is the coefficient of drag, \(C_{D0}\) is the parasitic drag coefficient, \(C_{L}=\frac{2 mg}{\rho Av^{2}(t)}\) is the coefficient of lift, \(R_{a}\) is the aspect ratio of the wing, and \(\varepsilon\) is the Oswald efficiency factor.  
Moreover, the following constraints are enforced on the UAV's movement at any time \(t\):  
\[\left\{\begin{array}{ll}&Z^{min}\leq z(t)\leq Z^{max},\\ &0<v(t)\leq V^{\max},\\ &-\Omega^{\max}\leq w(t)\leq\Omega^{\max},\\ &-U^{\max}\leq u(t)\leq U^{\max},\\ &0\leq F(t)\leq F^{\max},\end{array}\right. \tag{3}\]  
where the constants \(0<Z^{min}<Z^{max}\) specify the allowed deployment altitude, the constants \(V^{\max}\), \(\Omega^{\max}\) and \(U^{\max}\) specify the bounds of the corresponding speeds of the UAV, and the constant \(F^{\max}\) is the maximum allowed thrust force.
# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 2 Cloud Computing Paradigm, Scheduling and Literature Review
### Resource Scheduling in Cloud Computing
Due to the dynamic nature and inconsistencies in the cloud computing environment and the number of requests and resources, the static algorithms are not adequate for this type of system, because they are not able to adjust the distribution of workload between the VMs well. In most cloud computing scenarios, static scheduling algorithms establish lower than average performance. Moreover, practice has shown that the performance issues with static scheduling algorithms affect the QoS indicators: makespan, reliability, availability, etc. Some examples of static scheduling algorithm instances include heuristics, like round robin (RR), first in first out (FIFO), and shortest job first (SJF). With the above mentioned in mind, dynamic scheduling algorithms are more appropriate to for implementation in cloud computing infrastructure, as, instead of focusing on detailed information regarding the tasks and resources, their activities are based towards the nodes (VMs) monitoring activities. Dynamic scheduling algorithms are constantly monitoring changes in the cloud environment and shifting the workload from one to different nodes depending on the overloaded condition. Dynamic scheduling algorithms are able to establish good load balancing in cloud systems with a lower degree of imbalance between VMs (nodes). Some examples of dynamic resource scheduling algorithms are heterogeneous earliest finish time (HEFT), clustering based heterogeneous with duplication (CBHD), and weighted least connection (WLC). Also, as already stated in Section 1, metaheuristic approaches, particularly swarm intelligence, have proven to be efficient dynamic resource scheduling techniques. For more information regarding swarm algorithm applications to the cloud computing domain, please refer to Section 2.3. The need for sophisticated resource scheduling algorithms rises with the constant growth in number--as well as in the complexity--of submitted tasks. Existing scheduling algorithms and techniques should be improved, and new ones should be devised, accordingly. The scheduling of resources must be efficient in order to satisfy end-users' requirements with no negative impact on the service level agreement (SLA).
## 3 Results and Discussion
### Electrical Characterization  
Before wire-bonding the MODFETs were characterized on-wafer using Cascade Summit 11000B-AP probe station and an Agilent B1500A Semiconductor Device Parameter Analyzer (Agilent Technologies, Inc, Santa Clara, CA, USA). The transfer and output characteristics of the transistors are presented in Figure 4a,b, respectively. As previously reported [22; 23], transistors under study are normally-on devices, i.e., a negative value of \(V_{gs}<0\) is necessary to cut-off the channel [19]. The value of the extracted threshold voltage was \(V_{th}=-0.85\) V. The transfer characteristics show that when the drain voltage is moderately raised from 20 mV to 200 mV the current is greatly enhanced due to the high current throughput of the double deck electron channel. The high electron mobility in the channel induces high sensitivities of plasma waves Si/SiGe MODFETs THz sensors.