esa-sceva/satcom-chunk-collection huggingface dataset:
# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 3 Experimental Results
### Quantitative Test Results
However, the average sensitivity obtained with the superpixel-based approach is higher than the CNN  
Figure 12: Average image classification results of each fold in the test set using both metrics (BA and Sensitivity) for the hyperparameter optimization of the SVM Linear kernel. results, 91% and 88%, respectively. Moreover, the average specificity is slightly improved using the proposed approach (78%) with respect to the CNN-based approach (77%). Considering the fact that the superpixel approach only exploits spectral information, the results achieved in this preliminary study are competitive with respect to the CNN approach, which uses both spatial and spectral properties. Moreover, it is worth noting that the amount of data generated with the patch-based CNN approach is extremely large (49,565 image patches of \(87\times 87\) pixels with 275 bands, i.e., approx. 768 Gigabytes) compared to the data generated for the superpixel-based approach (426,260 superpixels with 275 bands, i.e., approx. 0.87 Gigabytes). Hence, the CNN approach requires substantial memory and computational requirements to perform the classification of the data, while the superpixel-based approach alleviates these requirements. This is especially important when targeting real-time aid visualization tools for histological screening process during clinical routine practice with the goal of assisting pathologist during diagnostic procedures.
## 3 Methodology for Human Navigation Path Simulation via Inverse Reinforcement Learning
### Apprenticeship Learning  
The apprenticeship learning module in EIRL is grounded in the foundational framework proposed by [PERSON] and [PERSON] [23], which enables inverse reinforcement learning by aligning feature expectations between expert demonstrations and learner policies. This approach is particularly suited for EEG-driven navigation for two reasons. First, unlike imitation learning methods that directly clone expert actions, apprenticeship learning explicitly models the latent reward structure underlying human decisions, providing interpretable safety constraints (e.g., higher weight on obstacle avoidance features). Second, the derived reward function generalizes to novel environments through feature space projection, avoiding overfitting to specific training scenarios--a critical requirement for robotic systems operating in dynamic settings.  
The algorithm iteratively refines reward weights \(\omega\) by maximizing the margin between expert feature expectations \(\mu(\pi_{E})\) and learner expectations \(\mu(\pi)\). Formally, we solve:  
\[\omega^{*}=\arg\max_{\omega}(\mu(\pi_{E})-\mathbb{E}_{\pi\sim\omega}[\mu(\pi) ])^{T}\omega, \tag{2}\]  
where \(\mu(\pi)\) is computed as the discounted sum of state features \(\phi(s)\) along trajectories generated by policy \(\pi\). The optimized weights \(\omega^{*}\) are then embedded into the Q-learning framework by modifying the reward function \(r(s)=\omega^{*T}\phi(s)\). Consequently, the Q-value update rule becomes  
\[Q(s,a)\gets Q(s,a)+\alpha\bigg{[}r(s^{\prime})+\gamma\max_{a^{\prime}}Q(s ^{\prime},a^{\prime})-Q(s,a)\bigg{]}, \tag{3}\]  
where \(r(s^{\prime})\) directly reflects human risk preferences encoded in \(\omega^{*}\). This integration ensures that the final Q-table not only minimizes path length but also emulates expert-level risk aversion.  
Apprenticeship learning (AL) [23] forms the foundation of inverse reinforcement learning (IRL), utilizing linear reward functions to approximate expert behaviors. AL extracts features from observed expert demonstrations and computes feature expectations, which are then treated as components of the reward function to derive optimal policies.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 3 Magnetometer Calibration Based on Least-Squares Fitting Algorithm
### Ellipsoid Fitting Based on Least Squares Algorithm  
If \(n\) sets of data are collected by the magnetometer, the ellipsoid equation can be expressed as:  
\[\begin{bmatrix}1\\ 1\\ \vdots\\ 1\end{bmatrix}=\begin{bmatrix}x_{1}^{2}&y_{1}^{2}&z_{1}^{2}&\cdots&x_{1}&y_{1 }&z_{1}\\ x_{2}^{2}&y_{2}^{2}&z_{2}^{2}&\cdots&x_{2}&y_{2}&z_{1}\\ \vdots&\vdots&\vdots&\ddots&\vdots&\vdots&\vdots\\ x_{n}^{2}&y_{n}^{2}&z_{n}^{2}&\cdots&x_{n}&y_{n}&z_{n}\end{bmatrix}\begin{bmatrix}a _{1}\\ a_{2}\\ \vdots\\ a_{9}\end{bmatrix} \tag{12}\]  
We write,  
\(H=\begin{bmatrix}x_{1}^{2}&y_{1}^{2}&z_{1}^{2}&\cdots&x_{1}&y_{1}&z_{1}\\ x_{2}^{2}&y_{2}^{2}&z_{2}^{2}&\cdots&x_{2}&y_{2}&z_{1}\\ \vdots&\vdots&\vdots&\ddots&\vdots&\vdots&\vdots\\ x_{n}^{2}&y_{n}^{2}&z_{n}^{2}&\cdots&x_{n}&y_{n}&z_{n}\end{bmatrix}\)  
\(k=\begin{bmatrix}a_{1}&a_{2}&\cdots&a_{9}\end{bmatrix}^{T}\)  
Ellipsoid fitting determines a set of ellipsoid parameter values \(K\), with the smallest variance based on the original measurement data and equation. According to the least squares algorithm, the ellipsoid parameters that lead to the smallest measurement error are:  
\[k={{{(H^{T}H)}^{-1}}}{{H^{T}}} \tag{13}\]
# mmS-TCP: Scalable TCP for Improving Throughput and Fairness in 5G mmWave Networks
## 2 Related Work
### AQM in MmWave Network  
The rapid transition from LoS to NLoS due to the presence of various obstacles significantly changes the available capacity of the mmWave band, which greatly affects the performance of the transport layer protocol. If the size of the RLC buffer of the mmWave base station that communicates with the UE is large, the occurrence of buffer overflow is reduced, which thereby reduces the packet loss rate. However, due to the transition from LoS to NLoS, a long queue is quickly created in the buffer, which can lead to a significant delay in end-to-end data delivery [17]. Reducing the buffer size may increase the frequency of buffer overflow and result in frequent packet loss; this may lead to the problem of reduced throughput, as the available bandwidth of the mmWave link cannot be fully utilized.  
To take full advantage of the large available bandwidth of the mmWave link, the TCP sender should increase the cwnd to a very large value. In an LoS with good channel conditions, the sending host, which is transmitting a large amount of data through a large cwnd, cannot immediately respond to the temporary deterioration of the channel condition of the mmWave link and continues to transmit excess data. The number of packets exceeding the available capacity accumulates in the buffer of the base station, which creates a long queue and results in the bufferbloat problem [16]. By applying the AQM scheme to mmWave base stations, it is possible to solve the bufferbloat problem and achieve low latency in 5G mmWave networks where frequent and large transitions between LoS and NLoS occur.  
Through related studies [14; 28] and our previous studies [17; 18], we confirmed that combining TCP with a FIFO queue management scheme can guarantee throughput, but RTT is increased excessively in this case, making it impossible to achieve 5G design goals. CoDel can be applied to the RLC buffer to provide low latency, but the problem of throughput degradation occurs because CUBIC TCP does not reach the maximum bandwidth quickly [18].  
Therefore, this study aims to provide high throughput by redesigning S-TCP with fast cwnd increase characteristics and to ensure low latency by applying the CoDel queue management scheme. This will help achieve the design goals of 5G.
# An IoT-Based Participatory Antitheft System for Public Safety Enhancement in Smart Cities
## 1 Introduction  
Whenever a consumer buys a product, the joy created thanks to this new possession is in danger by the risk of loss or theft of the specific item [1]. Every year millions of motor vehicles are stolen worldwide, causing an enormous loss of capital. For instance, according to the Insurance Information Institute, only in the U.S.A., in 2019, about $6.4 billion was lost to motor vehicle theft [2]. Additionally, according to the International Crime Victim Survey (ICVS) statistics, bicycle theft is four times more likely than automobile theft [3]. Likewise, 70 million smartphones are lost or stolen every year worldwide [4]. In China, 67.2 percent of respondents who participated in a national study, reported themselves as being victims of a bicycle theft within 2002-2007 [5]. This figure is more than double the rate (30 percent) reported from the International Crime Victim Survey in Beijing up to 1997. Generally, vehicle population increases rapidly, while, at the same time, an exponential increase in vehicle theft takes place.  
This article presents a smart IoT-based system, called City.Risks, which was developed in order to use the active participation of people to share information data in order to proactively protect citizens from being victims of thefts of their mobile assets, as well as to reactively provide a more timely and effective response and assistance whenever a theft is indicated. This system utilizes a BLE/Wi-Fi gateway, which can be placed in public locations to enhance and facilitate the processes of detection and alerting of stolen moving assets that exist within the areas that are under investigation.  
The rest of this article is structured as follows: Section 2 refers to related work. Section 3 describes both the structure and the operation of the system developed. The results achieved by the systems are both presented and evaluated in Section 4. Future research is proposed in Section 5. Finally, in Section 6, concluding remarks are drawn.
## 2 Materials and Methods
### Self-Report Measures
#### 2.2.3 Technology Acceptance  
Self-reported perceived usefulness (PU), perceived ease of use (PeU), as well as intention to use (ITU) and predicted usage (PUs) with regard to smartphone use were assessed in light of two purposes: for personal and for business use. In detail, participants filled in 9 items to assess PU (e.g., \"using a smartphone saves time.\"), 9 items for PeU (e.g., \"It is easy to learn how to use a smartphone\"), 2 items for ITU (e.g., \"assuming I had access to a smartphone, I would use it\"), and 4 items for PUs (e.g.,\"In future situations in which I have to fulfill [business/personal] tasks and have the free decision, I would use a smartphone as technology\"), for both personal and business use of the smartphone. The items to assess PU, PeU, and PUs were translated and reformulated for the present research endeavor based on [PERSON], [PERSON], and [PERSON] [38]. The items to assess ITU were translated and reformulated based on [PERSON] and [PERSON] [5]. These instruments constitute established instruments developed and validated by world-wide leading scholars in the field of TA research. Moreover, three additional items were included in the PU and PeU scales. Each item was answered on a 6-point Likert-scale (we opted for a 6-point scale to avoid a tendency toward the middle option; note that the original measures used 7-point scales). For interested researchers, the German items to assess the TA scales as used in the present study are available from the authors upon request. Internal consistency estimates (using [PERSON]'s alpha) for the scales were: smartphone business: 0.88 (PU), 0.86 (PeU), 0.88 (ITU), and 0.95 (PUs); smartphone personal: 0.88 (PU), 0.89 (PeU), 0.89 (ITU), and 0.94 (PUs). Because the associations between personality and TA scales did not differ meaningfully between the personal and business use scales (see Appendix A), we combined both PU, both PeU, both ITU, and both PUs scales (personal and business use) into one scale, respectively. This leads to one total PU, one total PeU, one total ITU, and one total PUs scale. Our strategy makes sense: Without meaningful differences in correlation patterns between personality and TAM for personal and business area, we focus the main body of the paper on general smartphone acceptance in everyday life. Nevertheless, the investigation of individual differences in technology acceptance of the smartphone in both personal and business areas without doubt can provide researchers with additional important insights. Therefore, we also provide information on personality-TAM correlations in the personal and business sectors in the Appendix A. For the interested researchers the data are available upon reasonable request.
# Node-Based QoS-Aware Security Framework for Sinkhole Attacks in Mobile Ad-Hoc Networks
## 4 QoS-Aware Security Framework: Proposed Scheme
### Resource Reservation Scheme  
In MANETs, the bandwidth is shared by nodes among neighboring links, and its usage is managed by the routing protocol implemented. The resource reservation scheme (RRS) can be implemented through QoS routing means or through QoS MAC strategies. QoS routing schemes can be proactive or reactive. These are the known routing schemes such as AODV, DSR, and OLSR. The RRS is responsible for providing an acceptable level of QoS for high-priority sessions during routing by reserving resource usage at all immediate nodes along with the source to destination route. This is done in order for applications, such as video or any form of multimedia, to have enough bandwidth during the transmission phase  
Figure 2: QoS-aware security framework [19].  
to satisfy an acceptable level of QoS requirements. The routing session's QoS is dependent on the bandwidth shared among the neighboring links under the same medium and nodes within the interference range of the route. The reason why an RRS is implemented in this research is that state information on existing transmissions is protected within the route. In OLSR, the available resources are detected in advance as part of its proactive routing process, and all state information is constantly updated at the routing table. A secondary route is selected in an event where the primary route cannot satisfy QoS requirements because of link failure or insufficient resources. RRS is implemented to alleviate the impact of latency that comes with the routing process through the transmission of data from any source node because all the possible routes would have been identified proactively [21].
## 5 Evaluation
### Impact of the Area Subdivision  
The variation of the farm shape and the layout of the subdivisions defines the adjacency constraints and the number of independent plots. Figure 8 provides four layouts with different adjacency constraints. Subdivisions (a), (b), (c), and (d) in Figure 8 present respectively two, three, two, and four independent plots. In subdivisions (b) and (d), we suppose the Inter-Plot Distance (IPD) large enough to eliminate edge effect and inter-plot competition. The associated income to each subdivision is given in Figure 9.  
Figure 8: Various farm subdivisions, with no Inter-Plot Distance (IPD) in (**a**) and (**c**), and acceptable Inter-Plot Distance (IPD) in (**b**) and (**d**).  
Figure 9: Change in incomes based on farm subdivisions in Figure 8.  
Although subdivisions (a) and (c) provide the same income, subdivision (a) may provide a larger income since it contains two sets of two independent plots: the set of blue plots and the set of black plots. From a general observation, the higher the number of independent plots, the higher the income can increase.
## 4 Experimental Results
### Parallel Scenario Experiments  
#### 4.3.1 Experimental Setting  
As mentioned earlier, in the parallel scenario, all collaborating algorithms must satisfy the proposed collaborative method hypothesis. This section offers experiments performed to assess the collaborative approach of the proposed model in the parallel scenario. To this end, the experimental setup is similar to that described in the previous section, with the exception that the number of collaborating algorithms is increased by generating multiple algorithms with different random initialisation in order to achieve diversity in the solutions.  
In this scenario, the experiments are divided according to the algorithm specialisation for collaboration, with the first and second experiments representing the collaboration of algorithms specialised in only quantitative and qualitative data processing, respectively, with each other. Finally, the third experiment represents the collaboration of all the qualitative and quantitative algorithms. The method used to assess the proposed model is identical to that described in the preceding section.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 4 Review
### Beamforming
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Reference** & **Optimization Variable** & **Constraint** \\ \hline
[67] & Spectrum allocation & \\
[68] & Secrecy capacity & Limited power and quality of service \\
[69] & Secrecy outage probability & Total transmission power of satellite and relay \\
[70] & Transmit power & Secrecy rate and communication rate \\
[71] & Secrecy outage probability & Secrecy rate \\
[72] & Power spectral density & \\
[73] & Signal-to-noise ratio & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: References showing the use of resource allocation to improve the PLS of the communication network. ming, with optimal and suboptimal greedy algorithms proposed. Simulations show that the proposed algorithms enhance spectrum efficiency and ensure fairness and demonstrate that the greedy algorithms perform nearly as well as the optimal ones with lower complexity. In Table 3, we summarize the works that have used beamforming to secure the communication network. Overall, beamforming, a signal processing technique, has emerged as a valuable tool for enhancing the security of both terrestrial and satellite communication systems. By focusing the transmission or reception of radio waves in a specific direction, beamforming can reduce the likelihood of signals being intercepted by unauthorized parties. This is achieved by concentrating energy toward the intended receivers, minimizing signal leakage and mitigating interference from other sources. In conjunction with other physical layer security techniques, beamforming can create a more robust and secure communication channel. While beamforming offers significant security benefits, it is not without its challenges. Static beamforming, where the direction remains fixed, can be exploited by attackers who learn the beam pattern and position themselves accordingly. Dynamic beamforming, which changes direction frequently, offers better security but requires more complex implementation and can be susceptible to misalignment due to imperfect channel knowledge or rapid user movement. To address these limitations, researchers have explored various techniques, including machine learning-based beamforming and the use of intelligent reflecting surfaces. These approaches aim to improve the efficiency and adaptability of beamforming, making it a more effective tool for securing communication systems in dynamic and challenging environments.
## 3 Datasets
### Additional Data
#### 3.3.2 Numerical Wave Model  
The German Weather Service (Deutscher Wetterdienst (DWD)) operates an implementation of the numerical wave model (NWM) WAM [20] for sea state modeling. The modeling setup of the so-called CWAM is especially designed to deal with the conditions in the German coastal regions. The model is driven by the COSMO-DE atmospheric model and the BSH-CMOD water level model from the BSH [21]. CWAM is a purely hydrodynamic model, which is nested into other large-scale models. No in situ observations from wave buoys or other sensors are assimilated. The spatial resolution of this coastal model is about 900 m, and each hour, a SWH parameter set with a resolution of 0.01 m is generated. No model accuracy and precision information are provided.  
We validated the quality of the model outputs by comparisons with in situ SWH data from three wave buoys using data from the year 2017. The buoys were located in deep and shallow water areas to study the model performance under different conditions. The validation results are summarized in Table 1. All three validation sites showed no systematic offsets and similar root mean square (RMS) values of up to 0.21 m. Therefore, it was assumed that the model quality was good enough to validate the functional models that will be calculated in Section 4.
## 1 Introduction
### Related Works
[27] proposed a software-defined PLC technology that integrates control functions into edge computing servers to achieve the functionality of edge PLCs. [PERSON] et al. [28] studied the characteristics of data arriving over time in edge PLCs in the industrial internet and applied reinforcement learning methods to optimize memory allocation methods, reducing data loss rates. However, there is a lack of research on how to efficiently allocate memory in task-intensive and memory-limited edge PLCs. For some cost-sensitive and space-limited industrial applications, edge PLCs have limited memory. Existing work has improved the efficiency of edge PLCs from multiple angles but their research focus has not been on memory allocation schemes. Current schemes mostly use static allocation methods or default dynamic allocation methods for memory allocation in edge PLCs. Therefore, for industrial scenarios where edge PLCs' memory is limited, a lightweight and efficient memory allocation method is an emerging need. The main contributions of this paper are as follows:  
* We propose an improved memory allocation strategy where fixed-size blocks are organized in advance into a pool. This strategy ensures real-time memory allocation for real-time tasks while also optimizing memory space utilization. * We introduce a memory allocation strategy based on dynamic programming, which models the memory allocation problem for non-real-time tasks as a knapsack problem. We incorporate a special parameter to adjust the item values, aiming to enhance decision-making results and improve memory allocation efficiency. * We implement the algorithm based on this strategy and conduct comparative testing under identical conditions against the default allocation method and several mature open-source memory allocators. The experimental results validate the real-time performance and efficiency of this strategy in the edge PLC environment. The remainder of this paper is arranged as follows: In Section 2, we provide a detailed introduction to memory allocation strategies. In Section 3, we conduct several comparative experiments and verify the effectiveness of these strategies through the experimental results. Finally, in Section 4, we summarize the paper.
# Scheduling for Media Function Virtualization
## 2 Research Context  
### IP and Virtualization  
Figure 1 shows an overview of MFV architecture. The bottom layer is the MFV infrastructure layer that consists of the hardware and software components required to transport and process media streams in the broadcast studio. Usually, the hardware, i.e., compute, storage, and network, is virtualized. The VMF layer consists of software instances of MFs, referred to as VMFs, running in virtual machines and/or docker containers. VMFs can be chained together to realize complex media services such as the one shown in Figure 1. The topmost layer represents the media service layer.  
Another important architecture component is the control and optimization (CO) layer. The roles of the CO layer include the management of other layers and the distribution of control signals to VMFs. In addition, the layer is responsible for scheduling the media service's VMF-FG to the MFVi. The CO layer is responsible for solving the VMF-FG scheduling problem by taking various input parameters as described in Section 4 into account. An optimization algorithm then generates a solution that describes the mapping of VMF-FG along with the VMF and virtual link schedules, as explained next. The mapping  
Figure 1: Overview of the MFV architecture.  
and the schedule can then be used to configure various MFVi components so that the required guarantees are met.