esa-sceva/satcom-chunk-collection huggingface dataset:
# QuietPlace: An Ultrasound-Based Proof of Location Protocol with Strong Identities
## 5 Technical Challenges
### Selective Information Hiding  
Knowing the identity of the participants and as a result of the users taking part in a PoL protocol session may compromise their privacy. Especially in QuietPlace, where the whole session is recorded and later sent to the verifier, it is important that no identity-related information is leaked during the proof creation phase.  
In the literature, several techniques are proposed to hide the users' identity and signature, so that they do not appear in cleartext. In [5; 21] every user has several pseudonyms that can be used in their messages. The CA is responsible for matching the pseudonyms with the real identity. A widely used technique are cryptographic commitments [29; 30]. A piece of information is temporarily hidden with the purpose of being revealed later. The procedure consists of two stages. In the first stage (commitment) the sender 'locks' a sensitive information and sends it to the receiver. In the second stage (decommitment), the sender sends the key to the receiver, who can now access the locked information.  
A simple commitment scheme uses a hash function h() [20]. In the commitment stage, the sender wants to commit to a message m. The sender produces a random number r and calculates the commitment C = h(m|r). Then, the sender sends C to the receiver. In the decommitment stage, the sender sends m and r to the receiver. The receiver recalculates the hash C' = h(m|r). By comparing C with C', the receiver can verify that the contents of the initial commitment were indeed m and r.  
An even simpler commitment-like technique is including the hash of the identity or the signature in the messages. This information cannot be understood in the first place, but when the sender of the message wants to reveal their identity, they can just send the actual information to the receiver, who can verify it by hashing and comparing with the hash initially received. Group signatures are used in [4; 20] to hide the identity of the witnesses. Each witness signs each of their message with a unique private key, but their signature is verified with a group public key. This way, the signer's identity is hidden, but the fact that they belong to a specific group (in this case the witness group) can be verified.  
In QuietPlace we use simple commitments as described above to protect the identities of the prover and witnesses. However, any other commitment scheme is compatible. For hiding the signatures, we require that they are hashed. Only the CA receives the signatures in encrypted messages to perform verification.
# Predictive Scheduling with Markov Chains and ARIMA Models
## 2 Existing Work on Robust Production Scheduling  
### Essentials of Robust Scheduling  
The purpose of a robust production schedule is predominantly to absorb potential disruptions, by allowing variability to the production system parameters.  
Two phases of scheduling are distinguished [9,12]:  
1. Predictive scheduling-related to the planning stage.
2. Reactive scheduling-related to the production stage.  
A well-executed process of scheduling production jobs must pertain to the first of the phases (also referred to as the offline phase) when the available production data give the foundation for creating [3]:  
* a nominal schedule-based on the current system parameters,
* a robust schedule-based on the assumption of uncertainty and variability of production.  
Reasonable scheduling in this phase requires not only implementing appropriate tools but also suitable methods for determining uncertainty factors [6]. Unfortunately, there is a distinct paucity of solutions that consider the impact of process disruptions [5,6].
## 2 Simulation Model
### Numerical Model  
According to the theory of streamer discharge, streamer channels are formed during the development of streamers, and there are a large number of positive and negative ions in the streamer channels. Because the streamer channel is electrically neutral, it can be approximated as a plasma. The establishment of the fluid model [22] is based on the drift-diffusion equations of electrons, cations, and anions, which describe the generation, dissipation, and transport processes of charged particles. The specific equation [23] is as follows:  
\[\begin{array}{c}\frac{\partial n_{e}}{\partial t}+\
abla(-\mu_{e}n_{e}E-D_{ e}\
abla n_{e})=\\ S_{ph}+\alpha n_{e}|\mu_{e}E|-\eta n_{e}|\mu_{e}E|\\ \end{array} \tag{2}\]  
\[\frac{\partial n_{p}}{\partial t}+\
abla(-\mu_{p}n_{p}E-D_{p}\
abla n_{p})=S_{ ph}+\alpha n_{e}|\mu_{e}E| \tag{3}\]  
\[\frac{\partial n_{n}}{\partial t}+\
abla(-\mu_{n}n_{n}E-D_{n}\
abla n_{n})= \eta n_{e}n_{p} \tag{4}\]  
where \(E\) is the electric field intensity; \(n_{e}\), \(n_{p}\), and \(n_{n}\) are the densities of electrons, cations, and anions; \(\mu_{e}\),\(\mu_{p}\) and \(\mu_{n}\) are electron, cation, and anion mobility; \(D_{e}\), \(D_{p}\) and \(D_{n}\) are electron, cation, and anion diffusivity; \(\alpha\) is the Townsend ionization coefficient; \(\eta\) is the Townsend adsorption coefficient; and \(S_{ph}\) is photoionization source term. The photoionization term in the drift-diffusion equation can be simplified to uniform background ionization [24; 25], and the parameters involved in Equations (2)-(4) are obtained from [26].  
The electric field change in the gas satisfies [PERSON]'s equation [23], namely Equation (5). The dynamics of space charge in the insulator is completely different from that in the gas, and the transport time of the space charge in the solid is much longer than that required by the streamer [13]. Therefore, the insulator interior can be regarded as satisfying the condition of no space charge, namely Equation (6).  
\[\
abla^{2}V=\frac{-e(n_{p}-n_{e}-n_{n})}{\varepsilon_{0}} \tag{5}\]  
Figure 1: Structure of physical model.  
\[\
abla^{2}V=0 \tag{6}\]  
where \(e\) is the element charge, \(\varepsilon_{0}\) is the vacuum permittivity, and \(V\) is the electric potential. Because the charge-mass ratio of electrons is much greater than that of ions, ions can be regarded as stationary for a few nanoseconds [27]. Figure 2 shows the basic flow of the surface discharge.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 4 Results
### Error Analysis  
To further assess model robustness and generalization, we conducted an in-depth error analysis from three perspectives: distributional characteristics of prediction errors, quantitative comparisons across regression metrics, and sensitivity to ecological heterogeneity.
# Efficient Data Exchange between WebAssembly Modules
## 5 Results and Discussion
### Generated Code
#### 5.2.4 Usage  
Karmem is being used in a multiplayer game currently under development. In this game, players take on the role of a programmer, where each player can write their own code to control a character. This use case requires multiple modules to read the same game state, which highlights one of the best uses of shared memory provided by Karmem and the isolation and safety of WASM, allowing for untrusted code to be run without locking into a single programming language.  
Comparing against existent alternatives is difficult. Technologies like waPC do not handle communications between WASM modules, do not provide zero-copy serialization, and do not handle shared memory. The component model is not currently supported by Wazero or WAMR, and few languages support the component model. It also does not offer shared memory.  
Karmem supports multiple languages but has usability challenges like Object-Relational Impedance Mismatch (ORIM) [71]. Although ORIM is typically associated with databases, Karmem encounters comparable issues due to the incompatibility between different programming paradigms. Specifically, it lacks object identity, meaning identical datasets are not distinguished as separate entities, unlike in languages such as Java, Swift, and C#. The absence of encapsulation allows unrestricted access and the modification of shared data across modules, which contrasts with object-oriented.  
Additionally, while Karmem's approach to avoiding data copies enhances performance, it can come at the cost of usability. Developers may need to manually manage data copying and fully understand the implications of this behavior, adding complexity to the development process and potential issues like Use-After-Free (UAF). In some languages, strings, fixed-size arrays, and similar data types are assumed to be immutable. However, the generated code may use type punning from shared memory, which lacks the immutability property, further complicating the handling of data and increasing the potential for errors. Another issue is the potential misuse of set/write operations over read-only shared memory.  
Unlike FlatBuffers and Cap'n Proto, Karmem does not support default custom values, resulting in a default value of zero or its equivalent. Consequently, relying on such default  
Figure 35: Definition of test interface with Karmem.  
values in a meaningful way is not recommended. Checking the existence of a field of dynamic structs is possible but not publicly exposed by the generated code.
## 4 Concepts of Integrating the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)
### The Concepts of PSO and ACO  
Inspired by the social foraging behaviors of some animals, particle swarm optimization was first proposed in 1995. It finds the optimal solution for problems through the movements of a population of candidate solutions, here defined as particles [17]. When applying PSO, it initially comes with a number of particles, continuously calculates the fitness, and updates particles' velocity and location till the stop condition is satisfied. As a popular intelligent algorithm, PSO has advantages of stronger global search ability, quick convergence speed and less controlling parameters. However, it does not have good ability in dealing with the feedback information. It is easy to encounter local optimization and slow convergence in the later stage [43].  
On the other hand, ACO was first developed in 1992. It is simulated with the search food process of ants to solve complex optimization problems [44]. During the foraging process, each virtual ant in the colony would search the path according to the transition probability and leave some pheromones on their trails. The more pheromones left on the path, the more likely ants will use this path. ACO is a simulated evolutionary algorithm based on swarm intelligence, and has some advantages of positive feedback, distributed computation and good search capability. However, due to the lack of information at the initial stage, ACO has disadvantages such as slow convergence speed and easily gets into a local optimum [28].
## 5 Properties of Shape Groups
### Occurrence and Properties  
Figure 3 shows the frequency of occurrence of each shape group. We note that, in our dataset, shape group _(8) Branches_ is the most common, occurring 20% of the time, followed by groups _(14) Irregulars and aggregates_, _(9) Side planes_, and _(1) Needles and thin or long columns_ occurring 17%, 14%, and 11% of the time, respectively. The other shape groups have frequencies below 7.5%. Figure 4a-c shows the particle size, \(D_{\text{max}}\); cross-sectional area, \(A\); and area ratio, \(A_{\text{r}}\), respectively, for each shape group. These quantities are shown by their median, represented as a point. The data spread is given by the percentiles 84.1% and 15.9%, which together correspond to \(\pm 1\sigma\) (standard deviation) if the distribution was normal (or Gaussian). The same data are shown in Table 8.  
In Figure 4a, we note that shape group _(6) Stellar crystals_ has the largest particle size with median \(D_{\text{max}}=1450\) um, followed by shape groups _(2) Crossed needles and crossed columns_, _(7) Bullet rosettes_, _(8) Branches_, _(9) Side planes_, and _(11) Spatial stellar crystals_ with \(D_{\text{max}}>1000\) um. Shape groups _(3) Thick columns and bullets_, _(5) Plates_, _(13) Ice and melting or sublimating particles_, and _(15) Spherical particles_ have the smallest sizes with median \(D_{\text{max}}<500\) um. Shape groups _(11) Spatial stellar crystals_ and _(14) Irregulars and aggregates_ have the widest spread ranging over 1000 um, whereas shape groups _(13) Ice and melting or sublimating particles_ and _(15) Spherical particles_ have the smallest data spread with less than 300 um. Figure 4b illustrates that shape group _(6) Stellar crystals_ with the largest particle size also has the largest cross-sectional area, with median \(A=6.34\)\(\times\)\(10^{-7}\) m\({}^{2}\), followed by shape groups _(7) Bullet rosettes_, _(8) Branches_, _(9) Side planes_, and _(11) Spatial stellar crystals_ with median \(A>3.0\times 10^{-7}\) m\({}^{2}\). In general, groups with large \(D_{\text{max}}\) also have large areas. However, there are exceptions, such as shape group _(2) Crossed needles and crossed columns_. While this shape group is among the groups with the largest \(D_{\text{max}}\), it is also among the shape groups with smaller areas. This is due to its shape leading to a  
Figure 3: Occurrence of shape groups (1–15). minimal area ratio, as can be seen in Figure 4c. We note that shape groups _(1) Needles and thin or long columns_ and _(2) Crossed needles and crossed columns_ have the smallest area ratios with \(A_{\rm r}<0.2\). Shape group _(15) Spherical particles_ has an area ratio close to one, as expected.
# Arabic Hate Speech Detection Using Deep Recurrent Neural Networks
## 3 Materials and Methods
### Preprocessing and Word Embedding Layer for Arabic Hate Speech Dataset  
The cleaning and preprocessing of data is an extremely vital step to increase accuracy. The Arabic hate speech dataset was subjected to a preprocessing stage in which the data were cleaned and irrelevant data were discarded. In addition, diacritics, symbols, special characters, and emojis were removed from the data. It should be noted that Arabic characters depend on the different Unicode representations; thus, a dictionary was created to map the same representation to their fixed characters.  
Every sentence inside a single comment separated by space was taken and a default tokenizer was applied to remove symbols from the string. This was then converted to  
\begin{table}
\begin{tabular}{r c c} \hline \hline
**Class ID \#** & **Class Name** & **Class Data Size** \\ \hline
1 & Comments Against Religion & 407 \\
2 & Racist Comments & 394 \\
3 & Comments Against Gender Equality & 410 \\
4 & Insulting/Bullying Comments & 404 \\
5 & Violent/Offensive Comments & 788 \\
6 & Normal Positive Comments & 900 \\
7 & Normal Negative Comments & 900 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the newly constructed class and data size for hate speech detection.  
Figure 1: Workflow of the proposed system for hate speech detection from Arabic text.  
tensor to represent numerical data, which are suitable for neural networks. The data were then extensively labeled; this was a hard, laborious, and time-consuming task. To ensure that the labeling was performed correctly, randomly selected text was shown to a different set of students who were tasked with labeling it; the two labels were compared to indicate if the labeling task had been carried out accurately. The labels' were converted from an integer to a tensor or float data type so each label contained a vector of the numeral that represented the label. The default batch size was set at 32; however, this, along with other hyperparameters, was changed during each experiment.  
Word2 Vec [27] and global vector (GloVe) [28] were the models chosen for word embedding, which allowed the system to learn word vectors according to their co-occurrence in sentences. This representation of the words in a lower dimension of vector space allowed the deep learning algorithms to map the words based on the similarity of the semantic properties. As shown in Figure 2, two architectures are present for the Word2 Vec: continuous bag of words (CBOW) and skip-gram (SG). The former predicts the target word using its contextual context, i.e., where it lies in the sentence within a window showing the words before and after it. The latter predicts the surrounding words based on the target word. After this extensive preprocessing, a vocabulary set of 25,000 words was created, and its size was changed based on the various experiments. The data were split into two as mentioned earlier, with 80% for training and 20% for validation.
## 3 Proposed Protocol Architecture
### Addressing Scheme  
In our proposed addressing scheme, all the IP-WSN sensor nodes will be identified by the IPv6 global unicast address, but the 128 bits global unicast address will be generated in a hierarchical fashion. At the lowest level, individual RFD IP sensor nodes will be identified by 16 bits short addresses within a specific SMAG domain. These 16 bits short addresses will form the least significant 16 bits of the EUI-64 identifier. Then the SMAG/FFD IP sensor node will hold EUI-64 bits identifier. This EUI-64 bits identifier will be generated by a 48 bits prefix and a 16 bits short address. Finally for the global communication individual IP sensor node will be identified by a unique global unicast address which consists of a 64 bits prefix and a 64 bits EUI-64 identifier. In this scheme all the nodes can be uniquely identified from the outside world. This is a stateless addressing scheme. It also facilitates the routing mechanism since individual IP-WSN nodes need not contain any routing table information which consumes huge memory space and energy for the LoWPAN. Although EUI-64 bits identifier is considered to be globally unique, it is not possible to communicate end to end with the corresponding nodes and individual IP-WSN nodes. For this reason we need to deal with the global unicast address. In this scheme, FFD is regarded as SMAG and RFD is regarded as an end IP-WSN node.
# Thermoresponsive Behavior of Magnetic Nanoparticle Complexed pNIPAm-_co_-AAc Microgels
## Materials and Methods
### MNP Synthesis  
For the current experiments, zinc-doped iron oxide magnetic nanoparticles (MNPs) were synthesized following the previous reports [33; 35; 36]. In brief, Fe(acac)\({}_{3}\) (353 mg), FeCl\({}_{2}\) (40 mg) and ZnCl\({}_{2}\) (30 mg) were dissolved in octyl ether with organic surfactants (oleic acid and oleylamine). The mixture was placed in a 50 mL three-neck round-bottom flask and heated at 300 \({}^{\circ}\)C for 1 h. Then, the reactor was cooled down to room temperature. The products were isolated by centrifugation after adding ethanol and the centrifuged magnetic nanoparticles are re-dispersed in toluene. For the amine functionalization, the MNPs are first coated by silica shell via a standard water-in-oil microemulsion sol-gel approach and the silica surface is further functionalized by aminopropyltrimethoxysilane (APTMS) [36]. The size and shape of amine-modified magnetic nanoparticles (aMNPs) were estimated by imaging using TEM (JEM-2100, JEOL, Tokyo, Japan) (Figure 1). Zeta potential value of aMNPs was measured (19 mV) and the hydrodynamic size by dynamic light scattering (DLS) is 21 nm with polydispersity index (PDI) of 0.1.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 5 Simulation Results and Discussion
### Simulation of Decision-Making Result
#### 5.2.2 Simulation of the Decision-Making Result of Unknown Jamming Patterns
The anti-jamming knowledge graph has strong extensibility that can add anti-jamming communication parameters in later work. When the jamming pattern changes, the reinforcement learning methods need to relearn all the parameters from scratch, which will lead to more computational complexity and slower response. The proposed decision-making algorithm directly extracts the anti-jamming strategy by utilizing the advantage of prior knowledge for known jamming patterns. For unknown jamming patterns, prior knowledge can also be utilized to effectively reduce decision complexity.
# Triple-Band Notched Ultra-Wideband Microstrip MIMO Antenna with Bluetooth Band
## 2 Design of an Ultra-Wideband Antenna Element
### Step 2: UWB Antenna with Single Band Notch at WiMAX Band  
The UWB spectrum signal faces some sort of interference with other narrow unlicensed band signals such as WiMAX since it operates at a small distance, e.g., an office, home, or hospital. Therefore, the WiMAX band extending from 3.3 to 3.8 GHz should be excluded from the obtained FCC-UWB signal. In order to achieve WiMAX band rejection over the aforementioned frequency range, the coupled line technique is introduced. An alpha-shaped coupled line band stop filter was placed along the feeding line before the proposed UWB radiator to obtain a band notch characteristic at the WiMAX frequency band, as shown in Figure 4b. The coupled line length for the alpha-shaped filter is calculated from Equation (4) at the notch frequency of \(f_{notch}=3.3\) GHz. The value of the calculated coupled line length is \(L_{\rm C}=25.02\) mm (i.e., electrical length = 0.5 \(\lambda_{g}\)) while the simulated value is \(L_{\rm C}=28.92\) mm (i.e., electrical length = 0.558 \(\lambda_{g}\)).  
\[L_{coupled}\simeq\frac{\lambda_{g}}{2}=\frac{C}{2f_{notch}\sqrt{e_{eff}}} \tag{4}\]  
where \(\lambda_{g}\) is the waveguide wavelength, \(C\) is the speed of light (C= 3 \(\times 10^{8}\) m/s), \(f_{0}\) is the operating notch frequency at the WiMAX band, and \(e_{eff}\) is the effective relative permittivity.  
Figure 5 shows the simulated reflection coefficient for the design of Figure 4b (i.e., Case 6). It is shown from the figure that the antenna operates over the FCC-UWB spectrum with a WiMAX band notch that extends from 3.3 to 3.8 GHz. However, there is another undesired band notch (i.e., first-order mode) at the frequency of 6.2 GHz. In order to overcome the undesired band notch, a stub-loaded resonator (SLR) is added to the alpha-shaped coupled line BSF, as shown in Figure 4c. Not only does the stub-loaded resonator (SLR) introduce a high selectivity characteristic but it also minimizes the length of the half waveguide coupled line (i.e., alpha-shaped coupled line length). As shown in Figure 4b,c, the length of the coupled alpha shape is reduced from \(L_{\rm C}=28.92\) mm to \(L_{\rm CS}=27.32\) mm. Figure 6 shows the investigation of the influence of \(L_{\rm CS}\). Three values of \(L_{\rm CS}\) are selected, 26.32 mm, 27.32 mm, and 28.92 mm, while the other parameters are kept constant. It is clear from Figure 6 that the optimum curve is at \(L_{\rm CS}=27.32\) mm. The simulated reflection coefficient for the proposed UWB antenna with a WiMAX band notch using coupled line and SLR (i.e., case 7) is presented in Figure 5. It is depicted by the case 7 curve in Figure 5 that the proposed antenna operates from 2.12 to 12.24 GHz with a band notch from 3.3 to 3.8 GHz. Moreover, the antenna supports the Bluetooth frequency band operating from 2.4 to 2.483 GHz.  
Figure 4: UWB antenna with single band notch: design steps. (**a**) Case 5; (**b**) Case 6; (**c**) Case 7.