esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Ring Exploration with \((H,s)\) View +
## 4 Possibility Result and Upper Bounds of Exploration Time
In this case, since \(\lfloor(S^{\prime\prime}+1-H^{\prime})/H^{\prime}\rfloor\geq 1\), the first part is executed at least once. Consider the first part. Let \(t_{p}\) be the step when \(A\) starts the \(p\)-th \(\textsc{ExpH}(t,v_{i})\). We show by induction that for \(1\leq p\leq\lfloor(S^{\prime\prime}+1-H^{\prime})/H^{\prime}\rfloor\), \(|V^{t_{p}}|=(p-1)H^{\prime}+1\) and \(A\) explores \(H^{\prime}\) nodes by \(\textsc{ExpH}(t_{p},v_{i})\). For the base case, i.e., \(p=1\), \(|V^{t_{1}}|\) is clearly \(1=(p-1)H^{\prime}+1\). This leads to that \(2H^{\prime}+|V^{t}|-1=2H^{\prime}\leq\min(n,S+1)\). Then, by Lemma 2, \(A\) explores \(H^{\prime}\) nodes by \(\textsc{ExpH}(t_{1},v_{i})\). Now, for \(k\leq\lfloor(S^{\prime\prime}+1-H^{\prime})/H^{\prime}\rfloor-1\), assume that \(|V^{t_{k}}|=(k-1)H^{\prime}+1\) and \(A\) explores \(H^{\prime}\) nodes by \(\textsc{ExpH}(t_{k},v_{i})\). Then, clearly \(|V^{t_{k+1}}|=(k-1)H^{\prime}+1+H^{\prime}=kH^{\prime}+1\). Since \(k\leq\lfloor(S^{\prime\prime}+1-H^{\prime})/H^{\prime}\rfloor-1\), \(2H^{\prime}+|V^{t_{k+1}}|-1<n\) and \(2H^{\prime}+|V^{t_{k+1}}|-1<S+1\). Thus, \(A\) explores \(H^{\prime}\) nodes by \(\textsc{ExpH}(t_{k+1},v_{i})\). By Lemma 2, \(S^{\prime\prime}=O(n)\), and \(H^{\prime}=\Theta(H)\), the exploration time of the first part is  
\[\sum_{p=1}^{\lfloor(S^{\prime\prime}+1-H^{\prime})/H^{\prime}\rfloor}(2H^{ \prime}+|V^{t_{p}}|-2)=\sum_{p=1}^{\lfloor(S^{\prime\prime}+1-H^{\prime})/H^{ \prime}\rfloor}((p+1)H^{\prime}-1)=O(n^{2}/H).\]  
We then consider the second part. By Lemma 2, \(A\) exists at the right or left extremity of \(V^{t}\) and \(|\overline{V^{t}}|=n-H^{\prime}\lfloor(S^{\prime\prime}+1-H^{\prime})/H^{ \prime}\rfloor-1=O(H)\) at the start of the second part. Thus, since \(A\) explores one node within \(n\) steps by Lemma 3, the exploration time of the second part is \(O(nH)\). As a result, the exploration time of Algorithm 3 is \(O(n^{2}/H+nH)\) when \(2H^{\prime}-1\leq S\). When \(2H^{\prime}-1>S\), the first part is never executed and then the number of remaining nodes at the start of the second part is \(n-1\). Thus, in this case, the exploration time of Algorithm 3 is \(O(n^{2})\). From Lemma 1 and Theorem 1, the following theorem holds. **Theorem 2**. : _If and only if \(H+S\geq n\) and \(S\geq\lceil n/2\rceil\), a single agent with the \((H,S)\) view can explore of \(1\)-interval connected rings within finite time steps._
## 1 Introduction  
Timely digital geo-referenced fire location maps are essential for tactical fire intelligence and mapping. Fast fire detection and real time monitoring of fire parameters, such as fire spread and location of hot spots, are crucial for the success of the initial attack on and containment of a fire. For this purpose, thermal infrared sensors and airborne infrared line scanners or frame cameras should be regarded as components of such a detection and monitoring system. These can provide fundamental information on fire extent and intensity, as well as rate of progress, which are of vital importance for tactical suppression actions.  
Information is required at different levels, and should be customised to user needs. For instance, at a _strategic_ level, information on the fuel load, the assets and land cover distribution at different regions is needed to allocate national and regional priorities and to support decisions such as the regional distribution of budgets, personnel and equipment as well as to establish and maintain initial attack capabilities, and weather and fire monitoring systems. At the same time, at a _management_ level, information is needed to support fire occurrence monitoring, fire behaviour process models and fire occurrence prediction systems. Finally, at an _operational_ level, especially during a crisis, tactical observations are those that provide detailed, frequent, and repetitive views of specific fire events. Added-value products from these observations include the fire perimeter, fire front, and isolated hot spots and they are used to support decision making for initial attack response, logistical and tactical planning and sustained action operations [1]. However, these different levels have very different requirements in terms of spatial and temporal resolution. Remote sensing systems can provide information at all levels. In the case of tactical emergency response, these systems can be mounted on fixed, airborne or satellite platforms. These are summarized hereafter.
# Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling
## 2 Related Works on Federated Personalization  
FL has emerged as a powerful paradigm for distributed machine learning while respecting data privacy across different clients or organizations. Among the personalized FL frameworks proposed in the literature, FedPer has garnered attention due to its effective handling of non-IID data. FedPer splits the neural network into two distinct parts:  
* A set of shared layers, typically the earlier layers (feature extractors), which are aggregated across all clients to capture global knowledge.
* A set of personal layers, often the latter or task-specific layers, which remain local to each client, thus allowing them to adapt to idiosyncratic features or data distributions.  
During training, the shared layers receive federated updates usually via Federated Averaging(FedAvg) from all clients, while each client's personal layers learn a local representation aligned with its unique environment. In intrusion detection scenarios, such as in industrial vs. campus networks, these local personal layers can capture domain-specific normal behaviors while retaining the globally aggregated attack knowledge in the shared layers. This makes FedPer particularly relevant to security contexts, as it can personalize to local anomalies and normal traffic patterns that differ significantly across organizations.  
Beyond FedPer, other personalized FL frameworks address non-IID data through distinct approaches, each with unique strengths and limitations for intrusion detection. Personalized Federated Meta-Learning (PFedMe) leverages meta-learning to train client-specific models, using a proximal term to regularize deviations from the global model [17]. This approach excels in adapting to diverse attack patterns but incurs high computational costs due to inner optimization loops, which may limit its efficiency in resource-constrained environments like IoT networks. Personalized Federated Averaging (PerFedAvg) integrates Model-Agnostic Meta-Learning (MAML) to enable rapid adaptation to client data through a two-step optimization process involving first- and second-order gradients [25]. While effective for fast personalization, its computational complexity makes it less suitable for single-round scenarios or edge devices in intrusion detection. Local-Global Federated Averaging (LG-FedAvg) blends local and global model features using a weighted average, controlled by a mixing parameter, to balance personalization and generalization [11]. This is useful for capturing varied traffic patterns across network domains, but its performance depends on selecting an optimal mixing ratio, which can be challenging in highly heterogeneous settings. These frameworks provide a foundation for DS-FedIDS, which enhances personalization with dynamic sampling to address class imbalance, offering improved efficiency and detection of minority attack classes in diverse network environments.
# A Long/Short-Term Memory Based Automated Testing Model to Quantitatively Evaluate Game Design
## 3 Proposed Framework: LSTM-Testing  
This study, therefore, proposes a Long/Short-Term Memory based automated testing model (called LSTM-Testing) to provide quantitative testing data. These data help the game designer to objectively evaluate the game design. LSTM-Testing emulates real player learning behaviors and iteratively plays the game through end-to-end functionality (Input: game image frame; Output: game action) without any manual feature extraction and intervention. The architecture of LSTM-Testing shown in Figure 1 is developed based on [9] and involves five logic gates and 2 memory pools including an input gate, convolutional gate, forget gate, training gate, output gate. Furthermore, short-term memory pool, and long-term memory pool are added to emulate gamer behaviors, including (a) how to play a game based on long-term memory and (b) how to relearn it based on short-term memory when a gamer forgets how to play it.  
Figure 1: Long/Short-Term Memory (LSTM)-Testing Framework.  
Four phases of 11 steps of LSTM-Testing, as shown in Figure 1, are adopted: (1) Acquire Sample, (2) Extract Feature by Deep Neural Network, (3) Review by LSTM, and (4) Action and Summarize the Testing. Furthermore, Figure 1 demonstrates three working flow: action decision flow, learning flow, and memorizing flow. The action decision flow (red flow) is an iterative flow consisting of steps 1, 2, 3, 4, 5, 6, 10, 11, and then back to step 1. The action decision flow decides what gameplay has the highest possibility to complete a game. The learning flow (blue flow) is also in iterative flow consisting of step 9.1 and step 9.2. The learning flow is an independent background thread that decides what gameplay decisions are picked up for reviewing periodically. One training cycle means a game session (e.g., playing until win or loss), and a cycle owns several times of reviewing. The memorizing flow is a sequential flow consisting of steps 7 and 8 to decide which sample should be stored in and drop from the short-term memory.  
The detailed operations of each phase are as follows.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 5 Protocol and Interfaces
### Orchestration Interfaces
#### 5.1.3 Provision Transport Links  
The orchestration plane receives a new SFC and calculates all Service Providers that participate in the SFC. If a Transport Link does not exist, the orchestration plane is responsible for setting up this link. If the Transport Link is set up manually, the orchestration layer informs the control plane in order to enable the BGP announcements of the Transport Link. If the link does not exist, an NSD is sent to the other orchestrator. This NSD can contain a full VPN configuration, but in the architecture the message is simplified to only contain the domain (AS number) and a Preshared Key (PSK). Furthermore, each control plane instructs the Tier 1 agent to set up the Transport Link by the use of a KMS server.
## 2 Materials and Methods
### Electrophoretic Mobility Shift Assay (EMSA) of kUrd Oligomers  
To analyze homooligomerization of kUrd **1-K-1\({}^{\prime}\)**, aqueous solution (18 \(\mu\)L) containing its \(\alpha\)- and \(\beta\)-chain RNAs (0.14 \(\mu\)M) was heated at 80 \({}^{\circ}\)C for 2.5 min and then cooled to 37 \({}^{\circ}\)C. The 10\(\times\) concentrated buffer (2 \(\mu\)L) containing 300 mM Tris-Cl (pH 7.5) and 100-200 mM Mg(OAc)\({}_{2}\) was added to the RNA solution. The resulting solution contained 30 mM Tris-Cl (pH 7.5), 10-20 mM Mg(OAc)\({}_{2}\) and 0.125 \(\mu\)M each \(\alpha\)- and \(\beta\)-chain RNA to form 0.125 \(\mu\)M **1-K-1\({}^{\prime}\)**. To analyze heterotrimers, three kUrd solutions (6.67 \(\mu\)L each), each of which contained 0.375 \(\mu\)M each \(\alpha\)- and \(\beta\)-chain RNA, were prepared separately and their mixed solution (20 \(\mu\)L) was then prepared. To analyze heterodimers, two kUrd solutions (10 \(\mu\)L each), each of which contained 0.25 \(\mu\)M each \(\alpha\)- and \(\beta\)-chain RNA, were prepared separately and their mixed solution (20 \(\mu\)L) was then prepared. The resulting solution (20 \(\mu\)L) containing kUrd homo- or heterooligomers was incubated at 37 \({}^{\circ}\)C for 30 min and then 4 \({}^{\circ}\)C for 30 min and 6\(\times\) loading buffer containing 50% glycerol and 0.1% xylene cyanol (4 \(\mu\)L) were added. The samples (24 \(\mu\)L) were loaded onto a 5% non-denaturing polyacrylamide gel (29:1 acrylamide:bisacrylamide) containing 50 mM Tris-acetate (pH 7.5) and 5-25 mM Mg(OAc)\({}_{2}\). Electrophoresis was performed at 4 \({}^{\circ}\)C, 200 V for the initial 5 min, followed by 75 V for 5 or 12 h. Gels were analyzed using a Pharos FX fluoromager (BioRad,Hercules, CA, USA). Each RzM:RzM interface consists of a pair of \(\Delta\)P5/P5 abc interfaces. The binding affinity between \(\Delta\)P5 core module and P5 abc module varied considerably depending on the identity of the \(\Delta\)P5/P5 abc interface [35; 36]. Stability of the interface is improved by increasing Mg\({}^{2+}\) concentration [35; 36]. In each EMSA, we therefore tuned the concentration of Mg\({}^{2+}\).
## 2 System Model
### Minimum Variance Distortionless Response  
The MVDR/SCBwas reported as the optimal design criterion of the beamformer \(\mathbf{\omega}_{k}\). The MVDR criterion obtains \(\mathbf{\omega}_{k}[i]\) by solving the following optimization problem:  
\[\begin{split}&\min\mathcal{J}_{\text{MVDR}}(\mathbf{\omega}_{k}[i])= \mathbf{\omega}_{k}^{H}[i]\mathbf{R}\mathbf{\omega}_{k}[i],\\ &\text{subject to}\qquad\mathbf{\omega}_{k}^{H}[i]\mathbf{a}(\theta_{k})= 1\end{split} \tag{5}\]  
where \(\mathbf{R}=E\big{[}\mathbf{x}[i]\mathbf{x}^{H}[i]\big{]}\in\mathbb{C}^{M\times M}\) is the covariance matrix obtained from the sensor array, and the array response \(\mathbf{a}(\theta)\) can be calculated by employing a DoA estimation procedure. By using the technique of Lagrange multipliers, the solution of (5) is easily derived as:  
\[\mathbf{\omega}_{k}[i]=\frac{\mathbf{R}^{-1}\mathbf{a}_{k}(\theta_{k})}{\mathbf{a}_{k}^{H}( \theta_{k})\mathbf{R}^{-1}\mathbf{a}_{k}(\theta_{k})} \tag{6}\]  
where \(\mathbf{R}\) is the covariance matrix of the received signal. In practical applications, \(\mathbf{R}\) is approximated by the sample covariance matrix \(\hat{\mathbf{R}}\), where:  
\[\hat{\mathbf{R}}=\frac{1}{N}\sum_{i=1}^{N}\mathbf{x}[i]\mathbf{x}[i]^{H} \tag{7}\]  
with \(N\) being the number of snapshots. Larger arrays require longer duration snapshots due to the longer transit time of sound across the array. The computation of a reliable covariance matrix \(\hat{\mathbf{R}}\) requires a higher number of \(N\geqslant M\). More snapshots are needed due to the many elements. Usually, this leads to snapshot-deficient processing [18].
# Use of Foamed Cement Banking for Reducing Expressways Embankment Settlement
## 2 Design Principles and Calculation Method
### Calculation Method
Therefore, the settlement caused by unloading is expressed as:  
\[S_{c}=\frac{[1-(1-\delta)\frac{S_{\infty}}{S_{\infty}^{p+\Delta P}}]}{P_{0}} \cdot P_{c}\cdot S_{\infty}^{p+\Delta P} \tag{3}\]After unloading construction, the actual remaining consolidation settlement \(S_{m}^{1}\) is expressed as:  
\[S_{m}^{1}=S_{m}-S_{c}=S_{m}-\frac{[1-(1-\delta)\frac{S_{m}}{S_{m}^{1}}]}{P_{0}} \cdot P_{c}\cdot S_{\infty}^{P+\Delta P} \tag{4}\]  
Considering the allowable post-construction settlement \(\Delta S\) in design work, by summing the actual remaining settlement \(S_{m}^{1}\) and allowable post-construction settlement \(\Delta S\), the total ultimate settlement \(S_{\infty}^{h}\) induced by the residual embankment load after moving the load \(P_{c}\) is obtained:  
\[S_{m}^{1}+\Delta S=S_{\infty}^{h} \tag{5}\]  
\[P_{c}=\frac{P_{0}\cdot(S_{\infty}^{P+\Delta P}-S_{m}-\Delta S)}{(1-\delta) \cdot S_{m}}=\vartheta\cdot P_{0}\cdot(\frac{S_{r}-\Delta S}{S_{m}}) \tag{6}\]  
where \(S_{r}\) is the residual settlement before improvement with FCB, \(S_{r}=S_{\infty}^{P+\Delta P}-S_{m}\); \(S_{m}\) is the monitoring data during construction; \(S_{\infty}^{P+\Delta P}\) is the computed ultimate settlement under the embankment load \(P_{0}\) function; \(P_{0}\) is the embankment gravity load, and \(\Delta S\) is the design-allowable post-construction settlement for which a local standard is suggested for determining its value. \(\vartheta\) is the rebound correction factor expressed as \(\vartheta=1/(1-\delta)\), where \(\delta\) is the swelling index within the range 0.05-0.3. Under the condition that the embankment design applies overload pre-loading, if the calculated subgrade unloading confirms that \(P_{c}\leq P_{0}-P_{u}\), \(P_{u}\) is the amount of overload, the post-construction settlement requirement can be satisfied after removing the overload pressure. When \(P_{c}>P_{0}-P_{u}\), except for removing overload, it is necessary to apply FCB to process the excess load, \(P_{c}-(P_{0}-P_{u})\). If equivalent load pre-compression is applied in embankment reinforcement, the computation of FCB replacement thickness should take the unloading value \(P_{c}\) into account. If the embankment design condition is under load pre-compression \((P_{0}\leq P_{u})\), computation of the FCB replacement thickness should confirm the expression: \(P_{c}+P_{u}-P_{0}\). In order to use equation (6) to calculate unloading value, parameters \(P_{0}\) and \(S_{m}\) can be acquired by measuring before unloading, and \(\delta\) is determined either by laboratory sample testing or by practical engineering empirical estimation. \(\Delta S\) is allowable post-construction settlement decided by local standard regulations [20]. The ultimate settlement \(S_{\infty}^{P+\Delta P}\) contributing to the pre-loading effect before unloading, it can be obtained by conventional layerwise summation method or by prediction of measured settlement during preloading construction.
## 7 Experiments and Results
### Experiment
According to the result of the experiment and our assumptions, the relationship between the desired position and positioning accuracy of the moving platform can be interpreted easily. Cable tensions at low altitude are smaller than those at high altitude. Furthermore, if the moving platform approaches a pole of robot fixed frame, then the force distribution is very unbalanced among cables. These reasons are also illustrated in Figure 25. On the other hand, the larger the cable tensions, the smaller the positioning error. In Figure 27, \(\mathbf{P}_{2}\), \(\mathbf{P}_{8}\) and \(\mathbf{P}_{6}\) have a low altitude, resulting in the increase in positioning error. Meanwhile, points which have a high altitude (e.g., \(\mathbf{P}_{7}\), \(\mathbf{P}_{5}\) and \(\mathbf{P}_{4}\)), have a better accuracy.
# A Study of Emergency Aircraft Control During Landing
## 3 Proposed Solution
### Lateral Control  
The lateral channel control law consists of two control loops (Figure 2). The structure of the inner loop with the LQRI controller is virtually identical to the loop used for the flight path angle control, except for the measurements corresponding to the lateral aircraft motion (\(\beta\), p, r and \(\phi\)) being used to compute the controller feedback signal. The inner control loop is in control of the roll angle \(\phi\). The role of the aircraft heading control \(\mathrm{HDG}_{\mathrm{set}}\), a superior control parameter, is filled by the outer loop with the proportional controller P. The main control signal here is a deviation from the commanded heading \(\Delta\mathrm{HDG}_{\mathrm{set}}\), determined from \(\mathrm{HDG}_{\mathrm{set}}\) and \(\psi_{\mathrm{mag}}\) by an external module (see Section 6.4). This signal is then transformed to the commanded roll angle \(\mathrm{ROL}_{\mathrm{set}}\) by applying a \(\pm 20^{\circ}\) saturation. The control output signal \(\delta\mathrm{T}_{\mathrm{lat}}\) is a thrust lever deflection of the No. 1 engine. Thrust asymmetry is obtained via inversion of this signal for the second engine.
## 4 Results and Discussion
### Advantages of Our Method  
In our work, we followed up and further developed the ideas described in [7; 13; 15; 16]. In these works, the voxelization of the surface is considered, and the approach is based on the expansion of the indicator function defined on each concentric sphere crossing the surface into a sum of spherical harmonics. The value of the indicator function on the corresponding sphere is \(1\) if there is an intersection with the surface and \(0\) otherwise. For the partial reconstruction of discontinuous function by its spherical coefficients, in addition to computational errors associated with surface voxelization and sampling the function on a grid, the Gibbs effect takes place in [82] (chapter 4). This effect manifested near the discontinuity point of a function [83] as a jump in the sum of its Fourier series, which exceeds the jump of discontinuity of the function itself by \(17.9\%\). In contrast, in our work, we consider the triangular surface of the object, not voxels, and only continuous functions \(f^{(i)}(\theta,\varphi)\) defined on the concentric spheres are used. Their sum \(f(\theta,\varphi)=\sum_{i=1}^{n}f^{(i)}(\theta,\varphi)\) will also be a continuous function as the sum of a finite number of continuous functions.  
Modifications made in our approach allow us to improve the accuracy of the identification. The summation of partial reconstructions at the grid nodes over the concentric spheres allows us to obtain the final grid. After summation over all spheres, the extrema on each sphere become even more prominent among the other sum values on the grid. We find the extrema and saddle points on this grid and build a convex hull on the found points. When we look for the mapping to match both 3D objects, we try to map the essential extrema of the respective objects. The amplification of the extrema causes a reduction in  
Figure 5: Examples when the mapping calculated by the method proposed is substandard. In each row, the (**right**) figure is the target, and the (**left**) is the result of the mapping of rotated green and original red objects. (**top**) Dragon egg—the surface components (spirally arranged scales) are not aligned. (**bottom**) Tablecloth—the holes of the objects are rotationally misaligned.  
the number of mappings to check, so the final result is obtained in a limited (fixed) number of steps without calculation of the gradient.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G
### SON Platforms Risk Assessment and Security Concerns  
Fifth-generation networks are exposed to the threats identified in 4G networks; however, the attack mitigation measures cannot be performed manually in a central manner due to the heterogeneity of the serving environment with M2M, D2D, and multi-equipment environments. Moreover, virtualization deployments raise trust issues between the operator and a Cloud service provider [114].  
As far as SON platforms are concerned, some additional issues must be considered. The fact that a SON platform has full read/write execution rights to the OSS of each RANtechnology needs very careful consideration and monitoring, since the damage that might be created to the network infrastructure due to inappropriate and non-legitimate account usage can be irreversible.  
The same stands for cases where a non-verified SON system manufacturer developers' code/script is imported into the system, since limited SON vendors enable through their APIs such actions and non-certified personnel, or small firms try to benefit from developing their own scripts due to the APIs provided with the system.  
In our opinion, the latter might be acceptable in case of small and non-critical distributed SON architectures, but in case of centralized ones, a misconfiguration might be a complete disaster. Consider the case where wrong scripting is applied centrally in a SON system by non-certified developers by the manufacturer. The outcome shall be a vast number of OSS commands committed to the network which do not lead to the expected KPI advancement and are difficult to revert in real time, manually or by the SON system.  
Finally, since SON systems are attached directly to the OSS, they must be completely isolated and reside behind all possible network security infrastructure such as anti-DDoS (Distributed Denial of Service), and NGFW (Next-Generation Firewalls) systems even if, typically, no web traffic (inbound or outbound) enters or leaves the SON system.