esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Description of the Numerical Technique  
### Separation of Antenna and Radome Domains  
This section describes the electromagnetic and numerical treatment of the problem, considering a combination of MoM, MLFMA, and CBFM. In this paper, the low-level basis and testing functions considered are modified rooftops and razor blades defined on the parametric space of curved quadrilateral elements over NURBS (non-uniform rational B-spline) patches. The use of curved basis functions favors a better fit to the original surface,avoiding facetization errors. Figure 1 shows a scheme containing the described basis and testing functions. However, the approach described here can be applied to any other kind of basis and testing functions.  
As a first step, consider a separation of the scenario into two different domains: \(\mathrm{D_{r}}\) refers to the radome domain and \(\mathrm{D_{a}}\) indicates the antenna domain. Owing to the nature of radome structures, we will consider them non-overlapping in the present work. Figure 2 presents a generic 2D scheme of this decomposition.  
The method of moments produces a linear system of equations that result from the transformation of a set of integro-differential equations defining the physics of the full problem:  
\[[Z][J]=[V] \tag{1}\]  
where \([Z]\) is the impedance matrix, \([J]\) is the current vector, and \([V]\) contains the excitation. Introducing the separation into both domains at this point, it is possible to expand Equation (1) as follows:  
\[[Z]=\left[\begin{array}{cc}[Z_{D_{a},D_{a}}]&[Z_{D_{a},D_{r}}]\\ [Z_{D_{r},D_{a}}]&[Z_{D_{r},D_{r}}]\end{array}\right] \tag{2}\]  
\[[J]=\left[\begin{array}{c}[J_{a}]\\ [J_{r}]\end{array}\right] \tag{3}\]  
\[[V]=\left[\begin{array}{c}[V_{a}]\\ [V_{r}]\end{array}\right] \tag{4}\]  
where \([Z_{D_{1},D_{2}}]\) refers to the impedance sub-matrix where the active basis functions are contained in domain \(D_{2}\) and the testing functions in \(D_{1}\), with \(D_{1}\) and \(D_{2}\) being either \(D_{a}\) or \(D_{r}\). Note that Equations (2)-(4) are derived from Equation (1) and a subdivision of the  
Figure 1: Basis and testing functions defined as curved rooftops and razor blades on a NURBS (non-uniform rational B-spline) patch.  
Figure 2: Separation of the antenna and radome domains.  
matrix coefficients into different sub-matrices corresponding to both domains. The current and excitation vectors \([J]\) and \([V]\) are also expressed as the combination of two column vectors to the antenna and radome domains, as expressed by Equations (3) and (4).
# Designing a Vertical Handover Algorithm for Security-Constrained Applications
###### Abstract  
In heterogeneous networks (HetNets), the vertical handover (VHO) is an essential process for mobile users (MUs) aiming to secure ubiquitous connectivity and maintain the highest quality of service (QoS) across various types of radio access technology (RAT), such as wireless fidelity (Wi-Fi), the global system for mobile communication (GSM), and the universal mobile telecommunications system (UMTS). In the literature, many recent VHO research works have been proposed in which a number of critical issues still arise when performing seamless vertical handover, including VHO packet loss, VHO delay, and VHO throughput. Moreover, the security aspect of triggering VHO has not been carefully considered, particularly when an MU intends to use a payment application. In this paper, we present a comprehensive performance evaluation for a new secure VHO algorithm, taking into account all the above issues, for co-located Wi-Fi and UMTS networks. The simulation results are compared with the media independent handover (MIH) IEEE 802.21 standard, which facilitates the seamless transfer of connection among heterogeneous networks. We show that the proposed algorithm outperforms the MIH standard in terms of VHO delay, VHO packet loss, and VHO throughput in providing uninterrupted connection to security-constrained applications.  
vertical handover; mobile networks; wireless networks; heterogeneous wireless networks; security-constrained applications
## 3 Results and Discussion
### Deep Levels
This band of traps may explain why two different energy levels show up in the dark or under light (Figure 14). This phenomenon can be explained by the band bending occurring at the intermixing layer between (Cd,Zn)S and Sb\({}_{2}\)Se\({}_{3}\). Indeed, because of the extended trap levels and the variable Fermi and quasi-Fermi levels in the dark and after illumination, only energy-related fractions of the traps will be filled. The literature [55] reports numerous levels within the Sb\({}_{2}\)Se\({}_{3}\) energy gap; most of them are categorized as deep donors, with only a few classified as acceptor-like defects. Among these, the antimony vacancy (V\({}_{\text{Sb}}\)) corresponds to an energy level located at 180 meV above the top of the valence band [55; 56], which is consistent with the lower extreme of the trap band detected by admittance measurements (Figures 13 and 14). If V\({}_{\text{Sb}}\)[55; 56] is the actual point defect acting as a trap then one is led to conclude that the trap band is localized in the Sb\({}_{2}\)Se\({}_{3}\) layer. Preventing defects can reduce recombination, leading to a higher open-circuit voltage (V\({}_{\text{oc}}\)) and fill factor (FF), while minimizing the lattice mismatch improves carrier transport and short-circuit current density (J\({}_{\text{sc}}\)). Based on trends in similar thin-film technologies, efficiency gains of 20-30% are expected with effective defect passivation and optimized interfaces.
## 5 Experimental Evaluation and Results  
In this section, we conducted an experimental setup to evaluate our scheme and compare it with state-of-the-art schemes that are designed for dynamic hashing on persistent memory. For this evaluation, we selected CCEH and Dash as the comparison schemes. Both of these schemes utilize the extendible hashing scheme, with CCEH emphasizing cache consciousness and Dash aiming to reduce read and write operations on a 256-byte bucket size.  
The experimental results shows that our scheme:  
* Efficiently utilizes the space at the segment level, ensuring that no empty space is left within a segment before triggering a segment split operation or directory doubling.
* In a multicore environment, our scheme exhibits strong scalability in terms of performance compared to state-of-the-art hashing schemes that employ similar techniques.  
* Our scheme achieves a high load factor without compromising performance and recovery, all while maintaining minimal costs. It demonstrates competitive performance in this regard.
## 3 Overview of the Model
### Named Entity Recognition Model Based on the Combination of Characters, Words, and Pinyin
To calculate the relevance weight of each matching word, we introduce LEBERT's character-to-word attention mechanism as a reference. Specifically, we represent the \(i\)-th character vector as \(h_{i}\) and the set of word embedding vectors corresponding to the \(i\)-th character as \(x_{i}\), where \(x_{ij}\) represents the embedding representation of the \(j\)-th word corresponding to the \(i\)-th character (aligned). Assign all \(x_{i}\) to \(h_{i}\), as \(x_{i}=\{x_{i1},x_{i2}...x_{im}\}\), with a size of \(m\) by \(d_{c}\). The method for calculating the relevance of each word is as follows:  
\[A_{i}=softmax\Big{(}h_{i}W_{attn}x_{ij}^{T}\Big{)}\]  
where \(W_{attn}\) is the weight matrix of bilinear attention, and \(A_{i}=\{\alpha_{i1},\alpha_{i2},\ldots\alpha_{in}\}\) is a vector of weights for each word. Then, we can obtain the weighted sum of the vocabulary features as follows:  
\[h_{i}^{\prime}=\sum_{j=1}^{m}\alpha_{ij}x_{ij}\]  
Weighted word embeddings and pinyin features are injected into the character vectors through the following methods:  
\[h_{i}^{\prime}=h_{i}+h_{i}^{\prime}+p_{i}\]  
The integration of vocabulary, character, and pinyin features is accomplished by utilizing dropout layers and layer normalization. The information obtained is processed through a series of 11 transformer blocks in order to derive the final output of BERT. The aforementioned output is subsequently fed into a BiLSTM to acquire the sequence features of the input vectors. Finally, the ultimate prediction results are obtained through CRF.
# Adaptive Path Planning for UAV-Based Pollution Sampling
## 4 Experiments and Results
### Results of Pollution Sampling with Adaptive Path Planning  
The last step of the verification study focused on assessing the performance of the integrated APP. The GPP and LPP were both assessed in two combined missions designed to simulate real-world scenarios where the APP functions as a path planner for a meteorological UAV.  
In the early stages of the APP research, it became evident that the heuristic criterion of wind influence (see Section 4.3.3) had a negligible impact on the quality of the generated paths. At the same time, the energy expenditure criterion (refer to Section 4.3.4), which completely disregarded wind effects, showed promise for enhancement through the integration of wind data. Consequently, the two criteria were merged to leverage their strengths.  
The updated EEE relies on pre-computed energy data as outlined in Equation (22). Instead of relying on \(v_{g}\), this method estimates flight time using \(v_{g}\), calculated via the wind triangle and incorporating wind velocity data from the wind map. Despite this improvement, the approach remains heuristic. Wind does not alter the direction of \(\vec{v}_{g}\); its effect is limited to modifying the magnitude of \(v_{g}\), either accelerating or decelerating the UAV, which in turn impacts energy consumption. To avoid infinite energy predictions when \(v_{g}\) approaches zero, the value of \(v_{g}\) is constrained to a minimum positive number.
## 2 Materials and Methods
### Mfa_R Processes
#### 2.3.3 Enforcement Process  
Even if the authentication is successful, a procedure is needed to reinforce the system's security regarding device identification. This process is carried out after a specific number of messages or a specific time of authentication of the second step, that is, when there must be an authentication reinforcement (enforcement). This step is intended to mitigate attacks such as spoofing (impersonating another subject, software component, or device, mimicking their physical [71; 72] or network characteristics [73; 74]) and sybil (an attack where a node pretends to be several nodes [75]. This is particularly problematic in [76]) sensor networks, among others. Since the query in the second step will be redone, it is practically impossible for a malicious (real or virtual) device to \"guess\" the answer (CR factor) and remain authenticated to participate in the system.  
The enforcement process starts when the data service (\(dataServ\)) receives data from the authenticated device. \(dataServ\) queries \(authServ\) to see if there is a need for enforcement. If so, a new query is sent as a 'piggyback' when returning data publication. If there is no need for enforcement, the query field will return empty. Once the query is received, \(dev_{\text{reg}}\) must answer it correctly or it will have its authentication expired by the \(authInspector\) and be dropped to the compatibility level.  
Additionally, it is important to mention some aspects of the enforcement algorithm (Algorithm 6). This is a server-side algorithm whose function is to revalidate the access of a device that potentially committed faults, for example. Note that this algorithm starts when the reputation score is below the \(LOWER\_THRESHOLD\) (Equation (4)), line 3. Similar to the authentication process, a query is generated and sent to the device (lines 4 to 8). When this is received back by the server (line 11), there is again a validation of its response, which, if positive, reinserts the device as authenticated in two factors and with an initial reputation (lines 14 to 16). However, if the query answer is not correct, \(authInspector\) will remove the device's access to the system (line 19).  
```
Result: result
1 Functiondo_enforcement(request: Request) : booleanis
2\(result\) = FALSE;
3 ifscore < LIMIAR_INEFIORthen
4 query_resp = query_api.generate(device_data);
5\(auth\_code\) = authInspector.generate_code();
6 authInspector.save(auth_code_resp);
7 authInspector.level(auth_code_AUTH2);
8 resp_step2(query);
9 wait(step2);
10\(step2\) = receive_step2();
11\(auth\_code\) = step2_auth_code;
12\(resp\) = step2_query_response;
13 ifauthInspector.valid(auth_code_resp)then
14 authInspector.level(auth_code_AUTH2);
15 authInspector.reputation_score(auth_code_INITIAL_REPUTATION);
16\(result\) = TRUE;
17
18 else
19 authInspector.remove(auth_code);
20
21 end if
22
23 end if
24
25 returnresult;
26
27 end for
```  
**Algorithm 6**Enforcement Process--server-side
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 4 LPWAN Systems and MG Control  
The fundamental idea of MG is to localize energy production based on consumption distribution while aiming to use as many sustainable energy technologies as possible. A standard definition for MG can be found in [53]. MGs may consist of several DGs and ESSs that inter-operate to create a stable electrical grid capable of fulfilling the demand. These small-scale grids may operate as isolated (islanded) or in interaction with other grids. The details and analysis of MG control structures are well covered in the literature, and out of the scope of this paper [1; 2; 54; 55; 56]. In this work, we are interested in the point of convergence of communication with MG control.  
In general, MGs are controlled by a multi-level hierarchical scheme [57; 58]. In this configuration, the fast primary level does not require information exchange with other units to operate. On the other hand, the secondary and the tertiary level systems include device-to-device communication links. The role of communication in MG control has been explained in detail by several researches conducted on MG secondary control [59; 60; 61]. However, most of the communication-related MG studies mainly focus on the topology of the network and issues, such as limiting the communication between neighboring devices or detecting faults on communication links by analyzing the dynamic performance of the control system and looking for communication failure indicators [59]. To cut a long story short, in the majority of MG studies, the communication system is normally considered as an auxiliary service that either is properly operating or is down. By contrast, in this work, the contribution is on the design and modeling of a LPWAN-based communication system and its integration in MG control structure.  
In order to propose a LoRaWAN communication framework for MG control system, the first step is to explain the feasibility and benefits of using LPWANs for this application in particular.
## 3 Implementation and Evaluation of the Method
### Computational Cost Evaluation  
Since all the processing is done directly on the mobile device, it is important to consider the time it takes to execute the proposed function according to the duration of the input signal. Accordingly, it was decided to register the time it takes for the application to calculate the hash code for each recording. Then, an analysis of runtime versus signal duration was done.
## 3 The Proposed Method
### Generation of the First Constrained Polygonal Space
#### 3.3.1 Free Spaces' Ratio  
To compute a feasible, safe, and smooth \(P\), it is highly enticing that the amount of free spaces must be high in the CPS. To measure the obstacle-free spaces in the CPS, we first determine the size (\(\xi\)) of the \(S_{1}^{3}\), blocked-spaces (\(\xi_{obstacles}\)), and free-spaces (\(\xi_{free}\)). The overall size (\(\xi\)) of the CPS that is in the form of a polygon can be obtained by the Gauss determinant using Equation (1):  
\[\xi=\frac{1}{2}\sum_{i=1}^{|\xi|}(x_{i}y_{i+1}-x_{i+1}y_{i})\times h \tag{1}\]  
where \(x,y\) are the coordinate values of the CPS boundary, \(|g|\) denotes total vertices of the CPS boundary, and \(h\) is the height of the CPS. Out of the \(\xi\)-sized CPS, we find the amount occupied by the obstacles (\(\xi_{obstacles}\)) using Equation (2):  
\[\xi_{obstacles}=\sum_{i=1}^{n}\omega O_{i} \tag{2}\]  
where \(n\) denotes total obstacles' count present in a \(S_{1}^{3}\) and \(\omega O_{i}\) denotes the obstacles' occupancy. The occupancy of an \(O_{i}\) obstacles can be determined using Equation (3):  
\[\omega O_{i}=O_{H}\times O_{L}\times O_{W} \tag{3}\]  
where \(O_{W},O_{H},O_{L}\) denote the width, height, and length of an obstacle, respectively. The free space (\(X_{free}\)) amount, where UAV can fly safely, can be determined using Equation (4):  
\[\xi_{free}=\xi-\xi_{obstacles} \tag{4}\]  
The ratio (\(r_{f}\)) of the free spaces can be calculated using Equation (5):  
\[r_{f}=\frac{\xi_{free}}{\xi} \tag{5}\]  
The value of \(r_{f}\) ranges between 1 and 0. We represent this ratio as (\(1-r_{f}\)) to compute the occupied spaces value in overall CPS \(S_{1}^{3}\) complexity computation.
# Non-Uniform Distribution of Contamination on Composite Insulators in HVDC Transmission Lines
## 4 Simulation Test Results
### Effect of Wind Speed on Contamination Non-Uniformity  
When the test is completed, turn off the power and take the sample out of the tunnel, brush the contamination into a beaker and then filter and weigh the contamination. Then \(\mathrm{K_{T/B}}\), \(\mathrm{K_{W/L}}\), \(\mathrm{K_{H/M}}\) were calculated in Formula (1). When the wind speed is 1 m/s, 3 m/s, 5 m/s and 7 m/s respectively, the diameter of the particles is 250 meshes, relative humidity is 60% in DC energized condition, and the results are shown in Table 4.  
As is shown in Table 4, average NSDD of the Type-A increased with the increase of the wind speed. NSDD increases from 0.12 to 0.32 when the wind speed increases from 1 m/s to 7 m/s, which illustrates that increased wind speed contributes to contamination. Besides this, the \(\mathrm{K_{T/B}}\) value remains almost constant and stable at around 0.5. However, the \(\mathrm{K_{W/L}}\) value presents a downward trend, which means that the contamination growth rate of the leeward side is larger than that of the windward side with the increase of wind speed. It is noteworthy that when the wind speed is small, such as 1 m/s, the amount of contamination accumulated on the windward side is greater than that on the leeward side, but the opposite is true when the wind speed is higher. Besides this, the \(\mathrm{K_{H/M}}\) value also presents a downward trend, which indicates that with the increase of wind speed, the effect of electric field on the growth of contamination at high voltage terminal is weakened.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
## 3 Sensor Localization
### Angle-Based  
In this approach, the direction joining the anchor and target is often referred to as the direction of arrival, usually known as the Angle of Arrival (AoA). It is useful for the measurement node to exploit directional antennas [4] to estimate the Angle of Arrival, which is computed based on an angle system. In Figure 3, an example with three Anchor  
Figure 2: Time difference of arrival—TDoA.  
nodes (A, B, C) is presented. Once a target node has fixed a reference, it can measure the angles (\(\alpha\), \(\beta\), \(\gamma\)) from radio signals coming from each Anchor, so the estimated position is given by the intersection u of the three rays originated by each Anchor. The advantage of Angle-of-Arrival-based methods is that they do not require flight time estimation (and consequently not even synchronizations).
# Bio-Inspired Energy-Efficient Cluster-Based Routing Protocol for the IoT in Disaster Scenarios
## 3 Network Model  
The network model used in this study is shown in Figure 2 and is based on the following assumptions:  
1. The network is considered a homogeneous network. At the time of IoT-based sensor deployment, all the nodes are isomorphic, which means that all the nodes have the same energy at the time of deployment.
2. The sensor nodes are powered by batteries, and no energy-harvesting methods are applied or replenished.
3. The area covered by the IoT-based sensors is randomly distributed. The locations of the sensors are fixed, and each sensor node has a unique network identifier.
4. Each sensor node perceives its own location. The distance to other nodes can be calculated using the Euclidean distance equation.
5. The base station has unlimited energy and computational power.  
The first-order radio model is considered in this study, as presented by the authors of [44]. The ratio of the energy consumption model is based on the distance between the sender and receiver. Figure 3 shows the energy consumption model, where the energy required to send a message is calculated based on the sender-receiver distance and the total length of the message.  
\[E_{Tx}(n,d)=\begin{cases}n*E_{elec}+n*\varepsilon_{fs}*d^{2},d\leq d_{0}\\ n*E_{elec}+n*\varepsilon_{mpf}*d^{4},d>d_{0}\end{cases} \tag{1}\]  
In Equation (1), \(E_{Tx}(n,d)\) is the energy required to transmit \(n\) bits over the distance \(d\), and \(E_{elec}\) is the energy required to transmit a bit. \(\varepsilon_{fs}\) is the amplification factor for the free-space model, and \(\varepsilon_{mpf}\) represents the multipath fading model. \(d_{0}\) is the distance threshold, defined as \(d_{0}=\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mpf}}}\). When the distance is less than the threshold, the free-space model is used, and when the distance is greater than the threshold, the multipath fading model is used. In addition, to receive \(n\) bits of data, the energy required is \(E_{RX}=E_{elec}*n\).
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 2 Background and Related Work
### TinyML  
Tiny Machine Learning (TinyML) has emerged as a growing field in machine learning, characterized by its application in highly constrained Internet of Things (IoT) devices such as microcontrollers (MCUs) [51]. This technology facilitates the use of deep learning models across a multitude of IoT devices, thereby broadening the range of potential applications and enabling ubiquitous computational intelligence. The implementation of TinyML is challenging, primarily due to the limited memory resources of these devices and the necessity for simultaneous algorithm and system stack design. Attracting substantial interest in both research and development areas, numerous studies have been conducted, focusing on the challenges, applications, and advantages of TinyML [52; 53].  
An essential goal of TinyML is to bring machine learning capabilities to battery-powered intelligent devices, allowing them to locally process data without necessitating cloud connectivity. This ability to operate independently from cloud services not only enhances functionality but also provides a more cost-effective solution for IoT applications [3; 54; 55; 56]. The academic community has thoroughly examined TinyML, with systematic reviews, surveys, and research papers delving into aspects such as its hardware requirements, frameworks, datasets, use cases, algorithms/models, and broader applications. Notably, the development of specialized TinyML frameworks and libraries, coupled with its integration with networking technologies, has been explored to facilitate its deployment in various sectors, including healthcare, smart agriculture, environmental monitoring, and anomaly detection. One practical application of TinyML is in the development of soft sensors for economical vehicular emission monitoring, showcasing its real-world applicability [57]. In essence, TinyML marks a significant progression in the domain of machine learning, enabling the execution of machine learning tasks on resource-constrained IoT devices and microcontrollers, thus laying the groundwork for an expansive ecosystem surrounding this technology.