esa-sceva/satcom-chunk-collection huggingface dataset:
# An Edge-Based LWM2M Proxy for Device Management to Efficiently Support QoS-Aware IoT Services
## 4 QoS-Oriented Extended Functions
### Caching
#### 4.2.1 Cache-Management Scheme  
Several measures have been introduced and analyzed in order to measure the freshness of the data stored in the cache [23]. For example, the age of a cached item can be defined as the time difference between current time and the time the item became desynchronized with the source; or the age of a cached item can be defined by the Age of Information (AoI, [9]), i.e., the elapsed time between the current time and the time the item was generated at the source. In this case, for example, the LWM2M Object representing the Client sensor can support the LWM2M Resource Timestamp (ResourceID: 5518) that specifies the timestamp of when the measurement was performed.  
To quantify the lifetime of a cached item we choose the AoI metrics. We assume that the AoI is the QoS parameter established by the service level agreement about data freshness between the Server and the Proxy. As an example, during the configuration phase the Server can require that at least a certain fraction of the requests, e.g., 90% of the requests, receive a data item whose AoI is not larger than a given target value [24].  
So, a cached item is valid if its AoI is smaller than a value called refresh window (W). The Proxy responds to the Server request using its cached data item if it is valid; otherwise,  
Figure 8: Service delay CDF for different values of the congestion window size, \(\lambda=1/330\) ms\({}^{-1}\).  
it relays the request message from the Server to the Client, then, it relays the response message back from the Client to the Server and updates the cache.
# Moving Mesh Strategies of Adaptive Methods for Solving Nonlinear Partial Differential Equations
## 2 The Improved Moving Mesh PDEs  
We show the approach through an example, and it can be generalized to various other kinds of the time dependent propagations problems. Consider the non-linear partial differential propagations equation:  
\[u_{t}=L(u)u_{x} \tag{1}\]  
subject to appropriate boundary and initial conditions, where \(L\) represents a differential operator involving only spatial derivatives.  
Without loss of generality one can assume that \(x\in[0,\ 1]\). An one to one coordinate transformation between the computational domain and physical domain is defined as:  
\[x=x(\xi,\ t),\ \ \xi\in[0,\ 1],\]  
and the coordinate transformation satisfies:  
\[x(0,\ t)=0,\ \ x(1,\ t)=1. \tag{2}\]  
The EP proposed by [PERSON] for determine \(x(\xi,\ t)\) is to require equidistribution of a positive monitor function \(M\), so that:  
\[E_{1}(x(\xi,t),t)\doteq\int_{0}^{x(\xi,\ t)}M(x,\ t)dx-\xi s(t)=0, \tag{3}\]  
where \(s(t)=\int_{0}^{1}M(x,\ t)dx\).  
Although the choice of the monitor function is important for the distribution of the mesh, we will utilize the arclength monitor function \(M(x)=\sqrt{1+(u_{x})^{2}}\) in this manuscript.
## 3 Results  
### Seasonality Analysis  
In this section, averaged backscattering coefficients in dB from Sentinel 1 and ALOS-2/PALSAR-2 for sites S1, S6 and S10 as a function of acquisition date are shown in Figure 8 and Figure 13 to the end of this section, respectively. The rest of the sites will be analyzed below. Each time series have been collected with the same acquisition parameters such as wavelength, orbital pass, and incidence angle. Due to the low penetration depth at C-band, as seen in Section 2.5, the annual seasonality in the backscatter accounted for surface roughness changes. Growth of halite through evaporation of standing water or rise of brines by capillarity leads to a high increase of the radar power backscattered [8]. Thus, temporal changes in the time series were the result of salt crust development and their time evolution. We choose three patterns of time signatures to describe this behavior. The backscattered power from 2014 to 2018 has been explained in terms of climate and annual seasonality, and episodic events, each one with their own dynamics. Although an almost exclusively dry environment, wet conditions over extended periods in the salar are due to climate patterns such as La Nina. Summer and Altiplanic winter, where water supply was available, alternate to dry months on an annual basis. Finally, episodic events such as floods and strong winds resulted in eventual, short-term, intense variations in the salar surface and therefore to the corresponding radar backscattered power.  
Figure 8: _Cont._
## 1 Introduction
### Neighborhood Area Network  
The function of Neighborhood Area Network (NAN) is to communicate the information collected by smart meters to central controller [8]. The NANs may contain few hundreds of smart meters deployed in HANs. Smart meters are linked with different gateways through NANs. The coverage region of NANs is around 1-10 square miles. The requirement of data rates for NAN is around 10-1000 Kbps [11; 12; 13; 14].
# An Explainable Deep Semantic Coding for Binary-Classification-Oriented Communication
## 2 System Model and Problem Formulation
### Problem Formulation  
Different from the traditional source coding, the semantic coding needs to balance data compression efficiency, source reconstruction quality, and binary-classification task performance. To this end, we formulate an optimization problem based on the rate-distortion function of mixed source and large deviation theory of binary hypothesis testing as  
\[\min_{P_{Y_{1}},Y_{1},P_{Y_{2}},X_{2}} \max\{I(X_{1};Y_{1}),I(X_{2};Y_{2})\}-\mu C(P_{Y_{1}},P_{Y_{2}})\] (9) subject to \[\mathbb{E}[d(X_{1},Y_{1})]\leq D,\] \[\mathbb{E}[d(X_{2},Y_{2})]\leq D,\]where \(\mu\geq 0\) is a predetermined weight coefficient to balance mixed source compression efficiency and binary-classification performance. Intuitively, the formulated problem (9) can be seen as a weighted sum of the rate-distortion function (5) and the Chernoff information (8). Subject to a given distortion constraint, a larger \(\mu\) prioritizes classification accuracy at the expense of compression efficiency, and vice versa.  
Based on the convex optimization principle, we introduce Lagrange multipliers \(\lambda_{1}\geq 0\) and \(\lambda_{2}\geq 0\) to incorporate the distortion constraints into the objective function and formulate the dual problem as  
\[\begin{split}\max_{\lambda_{1},\lambda_{2}}\min_{P_{Y_{1}}|X_{1},P_{Y_{2}}|X_{2}}&\max\{I(X_{1};Y_{1}),I(X_{2};Y_{2})\}-\mu C(P_{ Y_{1}},P_{Y_{2}})\\ &+\lambda_{1}\big{(}\mathbb{E}[d(X_{1},Y_{1})]-D\big{)}+\lambda_ {2}\big{(}\mathbb{E}[d(X_{2},Y_{2})]-D\big{)}.\end{split} \tag{10}\]  
For the four optimization variables in the dual problem, we employ an iterative optimization process to reduce the complexity. Thus, we further decompose the dual problem into two sub-problems, given by  
\[\begin{split}&\max_{\lambda_{1}}\min_{P_{Y_{1}}|X_{1}}I(X_{1};Y_{1}) -\mu C(P_{Y_{1}},P_{Y_{2}})+\lambda_{1}\big{(}\mathbb{E}[d(X_{1},Y_{1})]-D \big{)},\\ &\max_{\lambda_{2}}\min_{P_{Y_{2}}|X_{2}}I(X_{2};Y_{2})-\mu C(P_{ Y_{1}},P_{Y_{2}})+\lambda_{2}\big{(}\mathbb{E}[d(X_{2},Y_{2})]-D\big{)}.\end{split} \tag{11}\]  
Although we consider semantic coding of source sequence and binary-classification task dependent on reconstructed sequence in the system model, it is worth noting that the assumption of mixed i.i.d. sources leads to single-letter rate-distortion function and Chernoff information. Thus, all subsequent formulations and analysis are expressed in terms of single-letter variables instead of sequences.  
It is worth emphasizing that in this work, the notion of explainability stems from the principled objective-level design of the BCOC model. By explicitly incorporating Chernoff information, which quantifies class separability into the optimization objective, the model structurally preserves features that are critical for binary decision making. Instead of relying on attention mechanisms or saliency maps commonly used in deep neural networks, this formulation offers a task-oriented and theoretically grounded approach to preserving task-critical semantics throughout the coding process.
## 3 Results
### Characteristics of Shear Band
#### 3.2.3 Inclinations of Shear Bands  
The inclination angle of the shear band is defined as the angle between the shear band and the direction of the minor principal stress. To analyze the inclination of the shear band, the following three theories were adopted.  
Mohr-Coulomb theory:  
\[\theta_{MC}=45^{\circ}+\phi/2, \tag{3}\]  
Roscope theory [34]:  
\[\theta_{R}=45^{\circ}+\psi/2,\text{ and } \tag{4}\]  
Figure 10: Global and local stress–strain relationships: (**a**,**c**,**e**) locations of typical regions in T1, T3, and T5 specimens; (**b**,**d**,**f**) global stress–strain curves of T1, T3, and T5 specimens and local stress–strain curves of typical regions in T1, T3, and T5 specimen.  
Arthur solution [35]:  
\[\theta_{A}=45^{\circ}+\left(\phi+\psi\right)/4, \tag{5}\]  
where \(\phi\) is the internal friction angle obtained from the plane compression test, which is 23.8\({}^{\circ}\), and \(\psi\) is the dilation angle. Because the tests were undrained, the dilation angle of the specimen was zero. In fact, the dilation angle is not zero [18]. Since the local displacement can be calculated by the DIC technique, the dilation angle at failure can be obtained using the following equation [4,18]:  
\[\psi=\theta_{M}-\arctan\frac{\Delta u_{z}}{\Delta u_{x}}, \tag{6}\]  
where \(\theta_{M}\) is the measured inclination of the shear band, and \(\Delta u_{z}\) and \(\Delta u_{x}\) are the incremental quantities of the local displacement in the \(z\) and \(x\)-directions outside the shear band, respectively.  
Table 4 summarizes the measured and calculated inclinations of the shear band. The measured inclination (\(\theta_{M}\)) of shear bands in the T1, T3, and T5 tests are smaller than the inclinations calculated based on the three theories mentioned above. Because the shape of the shear band is very complicated, the prediction of the inclination of the shear band is not accurate. The measured inclinations are closer to the inclinations calculated based on Mohr-Coulomb theory, as reported by [PERSON] et al. [36] and [PERSON] et al. [37].
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### AI-Powered Learning Management Systems (LMSs)
#### 3.4.2 Technology Used: MLLMs in AI-Powered LMSs  
The integration of MLLMs into LMS platforms involves several key technologies. MLLMs like GPT-4 [9] and CLIP [14] can process and understand content in various formats, enabling the LMS to handle textual, visual, and auditory data effectively. Advanced language models facilitate interactions between the LMS and users through natural langauge interfaces, allowing for conversational queries, personalized feedback, and adaptive content delivery [8]. AI algorithms analyze learner behaviors and preferences to recommend tailored content, with MLLMs enhancing this by interpreting complex patterns in multimodal data [64]. Furthermore, MLLMs can generate and grade assessments, provide detailed explanations, and offer suggestions for improvement across multiple modalities. Incorporating models like Wav2 Vec 2.0 [65] and Tacotron 2 [66] allows the LMS to support voice interactions and auditory content delivery.
## 5 Federated Learning with Homomorphic Encryption  
### Role of HE in FL  
Homomorphic encryption (HE) enables calculations over an encrypted domain, making it a good candidate for collaborative training of joint models in FL. HE can be applied in various ways within the FL framework, as seen in previous works.  
One application of HE in FL is to hide client updates from the server. Instead of accessing the client's updates directly, the server will perform the aggregations in the encrypted domain and only access the final result. This approach provides an added security layer against eavesdropping and data breaches. By encrypting the updates, even if an unauthorized person intercepts the data, it will not have access to the raw data or the model updates.  
Another way to utilize HE in FL is to collaboratively train the model without the need for intermediate decryption. In this scenario, the server conducts aggregations in the encrypted domain while having no method to decrypt the final result. Only clients having the decryption key can share the model.  
HE can have other applications to counter adversarial attacks that do not deal with privacy, or auxiliary attacks that are facilitating privacy attacks, such as poisoning attacks. These attacks aim to compromise the integrity or reliability of the FL process. From security perspectives, to defend the server from model poisoning attacks, researchers have explored various variational measures. One such measure is CosDetect, proposed by [PERSON] et al. in 2023 Yaldiz et al. (2023), which employs a cosine similarity-based outlier detection algorithm to address fundamental issues more effectively than existing security solutions. The authors observed that the weight of the last layer pertaining to the local model update could be more sensitive to the local data distribution than other layers. This observation is significant, as it suggests that the last layer of local updates from malicious clients should exhibit outlier characteristics compared to updates from honest clients, making it more meaningful to a privacy attack. However, as this paper does not focus on such attacks, we will not delve deeper into them.
# Dependence of the Staphylococcal Volatilome Composition on Microbial Nutrition
## Results and Discussion
### Species-Specific Responses to Medium Nutrition  
We examined the species-specific responses to the four complex media to obtain a fuller understanding of staphylococcal responses to differences in nutrition (Figure 3). We saw that the number of volatiles produced by each species in each medium did not correspond to free glucose availability (Figure 3a), demonstrating that our prior observation of the staph volatiles was not a function of data aggregation (Figure 1a). We also observed that the volatilomes of both species were dominated by high numbers of hydrocarbons and ketones in all media, and in each medium, the ratio of hydrocarbons to ketones was larger in _S. epidermidis_ than in _S. aureus_. When analyzing the volatilomes by the relative abundances of each chemical class (Figure 3c), we found that the relative abundance of _S. aureus_ volatiles correlated to free glucose availability (TSB > BHI > LB > MHB), but the _S. epidermidis_ volatilome did not (BHI > LB > TSB > MHB). We also observed that _S. aureus_ seemed to be more strongly influenced by the presence or absence of free glucose, with half or more of the volatilome represented by acids and esters in the high glucose media and less than one percent in the low glucose media (Figure 3d). _S. epidermidis_, by contrast, produced abundant acids and esters in all four media, but at 78-89% of the total abundance in high-glucose media, and 38-57% in the low glucose media. Because we did not have time course sampling of the volatilomes, we could not determine if these differences were a function of greater relative production of acids and esters by _S. epidermidis_ in all four media or reduced assimilation as an alternate carbon source in nutrient-depleted media. However, these snapshots supported the assertion by [PERSON] and colleagues ([PERSON] and others, 2018) who surmise that attempts to standardize optimal cultivation conditions by the selection of a single medium for all microorganisms--even two species of the same genus, as shown here--will insufficiently reveal the full spectrum of volatile metabolic compounds.  
Figure 2: Media-dependent staph volatilome variation represented as a dissimilarity matrix using Morisita–Horn differences calculated using relative abundances of volatile chemical classes.
## 3 Results and Discussion
### Hardened-State Properties of Mortars
#### 3.2.3 Flexural Strength  
Figure 5a shows the flexural strengths of the mortars between 7 days and 365 days of curing. These results show a similar behaviour pattern with those of compressive strength (Figure 4). Analysing the flexural strength values at 365 days of curing, the binary mortars with RC (5-10%) show a slight decrease, between 2.6% and 5.0%, compared to the conventional mortar, and the mortars with FA (10-20%) show a decrease between 7.2% and 14.7%. Regarding the ternary mortars with RC and FA, these also show slight decreases, between 5.1% (M-5/10) and 11.8% (M-10/20), with respect to the conventional mortar; these reductions of strengths being lower than those shown by the binary mortars with RA and FA. These results also indicate that all mixes produced, regardless of the type of addition, show decreases in this property lower than the percentage of replacement, attesting to the long life of the pozzolanic reactions that compensate for the loss of strength induced by the replacement of the clinker. Likewise, the relationship between flexural strength and curing age underwent an evolution according to a logarithmic-type regression with correlation coefficients R\({}^{2}\) > 0.94, similar to the pattern shown by the compressive strength with curing age (Table 6).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Mechanical Property**} \\ \cline{2-7}
**Mortar** & \multicolumn{2}{c}{**Compressive Strength**} & \multicolumn{3}{c}{**Flexural Strength**} \\ \cline{2-7}  & **A** & **B** & **R\({}^{2}\)** & **A** & **B** & **R\({}^{2}\)** \\ \hline RM & 10.43 & 22.92 & 0.967 & 0.51 & 5.58 & 0.949 \\ M-5/0 & 10.19 & 16.12 & 0.971 & 0.54 & 5.11 & 0.984 \\ M-10/0 & 9.31 & 15.27 & 0.925 & 0.61 & 4.41 & 0.999 \\ M-0/10 & 9.70 & 20.98 & 0.950 & 0.41 & 5.46 & 0.973 \\ M-0/20 & 10.71 & 11.90 & 0.931 & 0.36 & 5.02 & 0.998 \\ M-5/10 & 1055 & 19.42 & 0.946 & 0.40 & 5.12 & 0.971 \\ M-10/20 & 9.9 & 8.43 & 0.985 & 0.47 & 5.30 & 0.968 \\ \hline \hline \end{tabular} Note: \(y=A\) In \(x\) + B; where \(y\) is the mechanical property (compressive or flexural strength) value (MPa), A and B are constants, and \(x\) is mortar age.  
\end{table}
Table 6: Correlation between the mechanical properties and the curing age.  
Finally, it is common to express the flexural strength as a function of the compressive strength. Figure 5b presents the linear relationship (R\({}^{2}\) > 0.82) between the flexural strength and the square root of the compressive strength, highlighting that the incorporation of RC or FA individually, as well as of RC and FA simultaneously, does not affect this relationship that also exists for conventional mortars [44].
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 3 Materials and Methods
### Required Materials  
Considering all the involved parties in this development authentication and authorisation system, the environment used during this development tasks is based in Virtual Machines (VMs). Also, we describe the networking hardware used during validation. Additionally, for working with WebAuth authentication, we also need security keys and a compatible technology stack, namely, the following:  
* Virtualisation software. We used _VirtualBox 6.1_ for setting up the virtual environment for development.
* Three security keys. For the development of the tool and the validation phase, we used the Yubico Yubikey Security Key, which is compatible with discoverable credentials.
* Compatible technology stack. That is, compatible operating systems and browsers. We used Linux (Manjaro and Ubuntu), macOS Monterey, Windows (10 and 11), Android (v8, v12 and v13), and iOS 16.
* Hardware wireless router compatible with OpenWRT. We used ASUS RT-AC1200.
# Research on SWIM Services Dynamic Migration Method +
## 4 SWIM Service Dynamic Migration Method
### Migration Trigger Mechanism  
It can be known from the nature of the pseudo-random sequence that the pseudo-random sequence can guarantee the randomness of '0' or '1' [16]. On the one hand, it can be predetermined, and can be repeatedly produced and reproduced. On the one hand, it has some random properties of a random sequence. A trigger mechanism based on pseudo-random sequence is established in the SWIM network. First, a series of pseudo-random sequences are generated by the shift register, and the pseudo-random sequence output '1' triggers the migration mechanism. However, in order to prevent the output of consecutive '1' or '0' in the pseudo-random sequence, the service drifts too fast, causing the SWIM network to oscillate, or the service drift is too slow, providing the opportunity for the malicious attacker to maliciously detect the SWIM network and reduce the security of the SWIM service. Therefore, a triggering strategy combining random sequence and interval control is proposed. The SWIM service dynamic migration mechanism is trigged only when the pseudo-random sequence outputs '1' within the range specified by the time interval. According to the different types of SWIM services, combined with the knowledge of civil aviation experts, it is possible to adjust the frequency of the dynamic migration of SWIM services by expanding or narrowing the width of the time interval. The dynamic migration trigger mechanism of the SWIM service is shown in Figure 6.  
The dynamic migration mechanism of the SWIM service consists of two parts: a shift registers and a time interval. The shift registers outputs a pseudo-random number bit by bit with a period of \(T\). The flow of the SWIM service dynamic migration trigger mechanism is shown in Figure 7. Among them, \(Tl\) provides the shortest service time for SP, and \(Th\) provides the longest service time for SP, where \(Tl\) and \(Th\) cannot be integer multiples of \(T\), that is, the time interval is [\(Tl\), \(Th\)]. In the time interval, the service migration mechanism is initiated when the pseudo-random number of the pseudo-random sequence output is '1'; or when the SP service time reaches \(Th\), the service migration mechanism is trigged by the time interval.  
Figure 6: The migration trigger mechanism.