esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 System Evaluation
### Evaluation Methodology  
In order to address the advantages and limitations of the proposed iAPR system, the evaluation scheme comprises of usability and effectiveness stages, as depicted in Figure 5.  
Following the evaluation steps, the educators disseminated the hazard investigation lessons to 40 learners through lecture-based learning. This is a traditional learning approach where educators provide lectures using a whiteboard and slides. After that, a before-exam was carried out to evaluate the learning outcome of learners before they used the iAPR platform in the subsequent steps. Thereafter, the learners were required to investigate and obtain hazard knowledge and skill using the iAPR platform. After new iAPR-based learning approach, these learners were examined by conducting an after-exam to evaluate the effectiveness of the proposed iAPR system.  
Moreover, to validate the system usability, ten educators, ten construction professionals, and 40 students were asked to experience the iAPR platform. After that, all the participants took part in a survey through questionnaires and interviews, which evaluated the proposed system according to the following criteria [11; 19; 55; 56]: (1) Sense of being in the construction jobsite; (2) Ease of hazard investigation and inspection; (3) Real-world visibility of construction jobsite; (4) Support of 3D object anatomization; (5) Interactiveness with virtual environment; and (6) Learning motivation and engagement of users. Finally, all their feedbacks were scored and analyzed using a five-point Likert scale, which range from 1 point for strongly disagree to 5 points for strongly agree.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 4 Simulation Results  
In this section, six questions are investigated:  
* Where are the user locations that have a high impact on the total transmit power?
* With the optimized AP placement, what is the relation between the total number of APs and the performance of the D-mMIMO system?
* Is it important to take the radiation pattern of the arrays into account when optimizing the AP placement?
* Is it essential to take more environment information into account when optimizing the AP placement?
* Does the angular penalty-based model perform better than the Euclidean distance-based model?  
Our analysis considers five distinctive maps, maps 1-5, which are discussed in Section 4.1, and two scenarios with different antenna elements.  
* isotropic antennas are used in each AP.
* patch antennas are used in each AP.  
For each scenario, 11 signal coverage levels are considered. The parameter setup is summarized in Table 2.  
Figure 7 illustrates the procedure for executing the experiments. For a given channel model, we first use it to generate the average channel gain via Equations (11), (14) or (15). Then the optimal AP placement is generated by solving Formulations (6)-(9) with the generated \(\widetilde{\beta}_{il}\). Noticeably, we always optimally solve Formulations (6)-(9) regarding the considered solutions space, the set of potential AP locations. In this way, we eliminate the impact of the efficiency of the solver on the results. The approach that is used to solve the formulation is detailed in Section 4.2. For each generated optimal AP placement, except the ones generated via the RTS-based channel model, we use the RTS-based model to compute the channels, which are regarded as the ground truth. Then the total transmit power (\(T\).\(p_{\tt T}\)) for the AP placement is calculated based on the channels generated via the RTS-based model. The experiments are executed on a machine with 20 Intel Xeon(R)W-2155 CPU @ 3.30 GHz processors, one NVIDIA Quadro P6000 GPU, and Windows Server 2016 system.
## 3 Results  
Figure 5 presents the calcein-AM and SD-OCT M-scan evaluation based on Explant No. 6 as an example. The experimental results of the Probit-fit analysis for the different applied laser pulse durations are reported, together with the SD-OCT M-Scan evaluation. The quantity of laser lesions applied is demonstrated to be sufficient for Probit-fitting. Out of 13 RPE-choroid-sclera explants that received a total of 1300 SRT laser spots, 10 explants and 1000 treatment spots were included in the evaluation (three explants were excluded due to damage to the RPE during the preparation for staining). Finally, an overall statistical evaluation is presented, with 200 measurements per pulse duration.  
Figure 4: Confusion matrix used to compare fringe washouts in SD-OCT M-scans (Predictive Class) with the treatment outcome of the viability tests with calcein-AM and the binary evaluation of the porcine RPE-choroid-sclera explants (Actual Class).  
Figure 5: (**a**) Calcein-AM image of porcine RPE-choroid-sclera explant No. 6. The second treatment line within the probe region is emphasized (white-dashed rectangles 1–10), in which laser pulses of 6 μs pulse duration were applied; (**b**) exemplary explant classification according to the confusion matrix with 69 True Positive (TP = Blue), 29 True Negative TN = Cyan), 1 False Positive (FP = Red), 1 False Negative (FN = Yellow) for each individual spot evaluated by the algorithm (\(\kappa\)-value of 10). In this case, a sensitivity of 99%, a specificity of 97% and an accuracy of 98% was achieved; (**c**) Line two in, which laser pulses of 6 μs pulse duration were applied, is shown in more detail and with the corresponding SD-OCT M-scans for each lesion: no fringe washout and no RPE lesion is present for lesions No. 1 to lesion No. 3. (80 mJ/cm\({}^{2}\)\(\cong\) 15 μs), 118 mJ/cm\({}^{2}\)\(\cong\) 22 mJ, 155 mJ/cm\({}^{2}\)\(\cong\) 29 mJ). Washouts and RPE lesions start to occur in this sample for a radiant exposure of 198 mJ/cm\({}^{2}\)\(\cong\) 37 mJ at 6 μs pulse duration; (**d**) Complete SD-OCT M-scan record for lesion No. 10 (427 mJ/cm\({}^{2}\)\(\cong\) 80 mJ) showing a fringe washout around A-scan number 800. For each lesion, an M-scan of 54 ms duration was recorded, containing 1792 A-scans (SD-OCT scan rate = 33 kHz); (**e**) Corresponding evaluation by the algorithm with detected washout.
###### Abstract  
In this paper, we present a design of the linear polarization conversion metasurface (MS) for the broadband radar cross section (RCS) reduction based on split-ring resonator (SRR) structure in microwave region. The corresponding phase gradient can be obtained through the stable phase difference of basic units of polarization conversion MS. The designed polarization conversion MS is applied in coded electromagnetic (EM) matrix by defining two basic units \"0\" and \"1\", respectively. Based on the principle of planar array theory, a new random coding method named by matrix-type coding is proposed. Correlative RCS reduction mechanism is discussed and verified, which can be used to explore the RCS reduction characteristic. The simulated linear polarization conversion rate of the designed structure is up to 90% in the frequency range of 6-15 GHz, and the RCS reduction results verify the theoretical assumptions. Two kinds of matrix-type coding MS samples are prepared and measured. The experimental results indicate that the reflectance of MS is less than -10 dB on average under normal incidence in frequency range of 5.8-15.5 GHz. The average RCS reduction is essentially more than 10 dB in frequency range of 5.5-15 GHz and the corresponding relative bandwidth is 92.7%, which reasonably agrees with simulation. In addition, excellent RCS reduction characteristic of the designed MS can also be achieved over a wide incident angle.  
polarization conversion; matrix-type coding; energy scattering; RCS reduction +
Footnote †: journal: Applied Physics Letters
## 2 Setup and Simulation Environment
### Simulation Settings
It is worth noting here that all of the remaining system parameters that were not specifically listed in the tables above were set to the default values recommended by the simulator developers and clearly described/specified in the corresponding documentation.
## 3 Cloud-Agnostic Models
### Challenges  
Described below are several challenges that are faced by developers when adopting FaaS solutions. These challenges need to be taken into account when designing a uniform model for FaaS applications as well:  
* _Custom function signatures._ Every cloud provider imposes its own function signature depending on the programming language and trigger selected by the developer. The function's implementation can become deeply dependent on the provider's specific requirements [15], resulting in portability-related issues.
* _Unique libraries._ There are no common libraries shared between cloud providers that could attenuate portability issues when developing solutions to multiple platforms. Library dependencies are introduced not only for processing custom data types, but also for interacting with provider-specific APIs [15]. Switching between cloud providers requires the developer to adapt, making him less productive.
* _Provider-specific deployment environments._ Each cloud provider decides where and how service deployments can be performed. For instance, up until this date, the cloud provider MsAzure does not support the deployment of a serverless function written in Java or Python directly on the Azure portal [36], while Google Cloud Platform does. The workaround requires the installation of the provider-specific tool named Azure CLI. The variety of tooling is not ideal for solutions that require multi-cloud usage.
* _Discrepancy in deployment configurations._ During a service deployment configuration stage, developers are confronted with different payment plans and detailed hardware specifications to set up [37]. The amount of providers and their intrinsic variability results in a discrepancy between configurations that do not facilitate multi-cloud usage [38].  
To evidence the differences between function signatures, exemplified below, in Listings 1 and 2, are two simple use cases of serverless functions written in Java. Both functions are triggered by HTTP requests and can be deployed to MsAzure and Google Cloud Platform, respectively. The end result will be the same for both functions.  
Figure 1: Abstract ERM of the cloud-agnostic solution.  
```
1 publicclassFunction{
2@FunctionName(\"httpExample\")
3 publicHttpResponseMessagerun(
4@HttpTrigger(name=\"req\",methods=(HttpMethod.GET),
5 authevel=AuthorizationLevel.ANOYMHOUS)
6 HttpRequestMessage<String>request,
7 ExecutionContentcontext){
8 returnrequest
9.createResponseBuilder(HttpStatus.OK)
10.body(\"Helloworld!\").build();
11}
12}
```  
Listing 1: Java \"Hello world!\"--Cloud function, GCP.  
By introducing such wrapping around the actual business logic, functions can become deeply dependent on the provider's requirements.
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Cognitive-Driven Gain Controller System
#### 2.5.1 Real-Time Auditory Attention Decoding (RAAD)  
The RAAD consists of three blocks (see Figure 4), i.e., pre-processing of the EEG responses and speech signals, correlation coefficient generation and AAD using either GLM or SSM.
# Transverse Spin Hall Effect and Twisted Polarization Ribbons at the Sharp Focus
## 6 Numerical Modeling
Thus, in the focal spot plane, we observe alternating regions of upwards- and downwards-directed transverse spin vectors, which is a manifestation of the transverse spin Hall effect. When moving along the intensity ellipse contour in the focal plane, we find that the polarization arrows are partly directed with and partly against the motion direction. Meanwhile, for same-color arrows, this suggests that the spin vector \(\mathbf{S}\) is alternatively directed inwards and outwards of the ellipse. If two same-direction arrows are of different colors, the spin vector \(\mathbf{S}\) is directed inwards for one and outwards for the other (different-color) circle. Such a behavior pattern of the spin vector can be called a transverse (radial) spin Hall effect. When moving along an axis-centered circle, we encounter arrows perpendicular to the circle. If two given arrows are different colors, the spin vector \(\mathbf{S}\) is tangentially directed along the motion in one of them, being directed oppositely in the other. For two same-color, oppositely directed arrows, their respective spin vectors are directed with and against the motion-handedness. This effect may be called a transverse (azimuthal) spin Hall effect. Figure 3 depicts intensity patterns across the focal spot generated by the initial field (21) at \(n=1\), with white-yellow areas marking the maximum intensity and violet-black areas showing the minimum intensity. In view of Equation (22) and putting \(n=1\), field (21) is seen to produce an on-axis C-point of right-handed circular polarization, with its polarization singularity index being equal to \(\mathit{Ic}=2\). Upon moving anticlockwise around the optical axis, the major axis vector of the polarization ellipse in Figure 3 makes four half-turns, or two full turns. When compared to the polarization singula  
Figure 2: Intensity pattern (squeezed white-yellow-red ring) across the tight focus from the original cylindrical second-order vector beam in (17) at \(n=2\) and polarization ellipse projections onto the focal plane. In-ellipse arrows show the direction of the polarization ellipse major axis. Note that with the polarization ellipses being perpendicular to the focal plane, their projections on the plane coincide with the arrows. Blue arrows mark left-handed circular polarization, and red ones mark right-handed circular polarization. The axes are plotted in microns. in the original plane (\(\eta=1\)), a larger singularity index value at the focus for the C-point (\(I_{C}=2\)) is due to the fact that while in the original plane, the optical vortex topological charge (\(n=1\)) has no effect on the singularity index of field (21), in the focal plane, the optical vortex changes the polarization pattern topology, increasing the polarization index value by 1 thanks to extra phase jumps by \(\pi\). Moving away from the optical axis along an arbitrary radius, the major axis vector of the polarization ellipse in Figure 3 will form a non-closed strip in 3D space with half-turns found at distances of linear polarization.
## 4 Experiments and Discussion
### Evaluation Metrics  
The performance of our proposed MSCSA-Net with these two datasets was evaluated by using the mean intersection over union (_mloU_) and F1-score (\(F1\)). The mIoU and F1-score are commonly used in evaluating performance in segmentation tasks, and they can help us understand the differences between our segmentation results and the ground-truth pixels.  
\[mIoU=\frac{1}{n}\sum_{i=1}^{n}\frac{TP_{i}}{TP_{i}+FP_{i}+FN_{i}} \tag{7}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{8}\]  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
where \(TP_{i}\), \(FP_{i}\), and \(FN_{i}\) indicate the true positives, false positives, and false negatives for an object indexed with class \(i\), respectively. The F1-score and mIoU were calculated for all categories.
## 3 Improved YOLOv8s Network Model
### DyHead Module  
Driving in open-pit mines involves many obstacles, such as engineering vehicles (loaders, excavators, mine cars, etc.). The sizes of these obstacles are very different from those of pedestrians, falling rocks and other obstacles. In addition, at different angles, when the camera captures obstacles, they may present significantly different shapes and positions. In response to the above challenges, the head network of the obstacle detection model needs to have a certain spatial perception ability. The original detection head of YOLOv8 adopts a single-scale prediction structure, which cannot handle multi-scale targets well. At the same time, it lacks a global vision. In addition, the prediction head has a small number of parameters, which makes it difficult to deeply mine the spatial structure information in the features, thus bringing certain limitations to multi-scale obstacle detection. The DyHead (Dynamic Head) module [24] can perceive size, task, and space and is designed to improve the model's detection performance for targets of different scales, shapes, and small objects in complex environments. This module can dynamically adjust the weights of each feature layer to effectively extract multi-scale features, enhance the fusion of contextual information, and thus better identify multi-scale obstacle targets.  
Therefore, the DyHead module is presented to enhance feature representation learning and optimize the connection between features at various levels and goal sizes. Specifically, in the detection layer, given a three-dimensional feature tensor \(F\in R^{L\times S\times C}\), its attention calculation Formula (1) is as follows:  
\[W(F)=\pi_{\text{C}}(\pi_{S}(\pi_{L}(F)\cdot F)\cdot F) \tag{1}\]  
In the formula, the input three-dimensional tensor \(L\times S\times C\) is represented by F. The scale-aware attention module, space-aware attention module, and task-aware attention module are represented by \(\pi_{L}\), \(\pi_{S}\), and \(\pi_{C}\), respectively.  
\(\pi_{L}\), \(\pi_{S}\) and \(\pi_{C}\) act on \(L\), \(S\) and \(C\) dimensions, respectively, to expand the receptive field. As shown in Figure 7, these three modules are applied to the head detection network. Multiple DyHeads can be stacked and used. Considering factors such as model performance and computational efficiency, only one DyHead module is used. Experimental  
Figure 6: Comparison between SE module and ESE module: (**a**) SE module; (**b**) ESE module.  
comparisons have shown that using one Dyhead module can enable the detection head to perform better.
## 3 Results and Discussion
### Catalytic Activity
#### 3.8.1 Effect of PdAS Loading
Interestingly, the PdAS(5)-MA catalyst exhibited a similar catalytic performance to the homogeneous Pd-SAOPHEN system. In this context, it is tempting to speculate that the PdAS(5)-MA catalyst, at this Pd-complex content, follows a similar reaction pathway during the Heck reaction as its Pd-SAOPHEN counterpart. Recently, [PERSON] et al. reported the use of immobilized Pd-SAOPHEN on highly porous hyper-crosslinked polymers as catalysts for the Suzuki-Miyaura coupling reaction [31], and they found that the fresh catalysts suffered from Pd-metallization after continuous use. TEM analysis was therefore carried out for the used PdAS(5)-MA catalyst (Figure 8e), where the formation of small Pd-NPs with a narrow particle size distribution was observed. Furthermore, the PdAS(10)-MA catalyst exhibited an enhanced catalytic performance compared to Pd-SAOPHEN and the other PdAS(x)-MA heterogenous catalysts where x = 1, 2, or 5. To confirm the formation of Pd NPs on the catalyst surface, post reaction TEM was therefore carried out as shown in Figure 8f, and as expected, the formation of Pd-NPs on the PdAS(10)-MA surface was confirmed after the first reaction cycle. As expected, an increase in the PdAS content of the resins altered the physicochemical properties and the catalytic activity of the immobilized Pd-complexes. Indeed, dispersion of the PdAS centers had a systematic influence on the catalytic performance, suggesting that in the case of PdAS(x)-MA where x = 5 or 10, the catalytically active species are Pd NPs. We therefore propose that with an increased PdAS incorporation, a decreased spacer content in the Pd(II)-(L4) species could be expected. When the PdAS(x)-MA with x \(\geq\) 5 catalysts are submitted to the reaction conditions, the immobilized Pd(II)-(L4) pre-catalyst forms the Pd(0)-(L2) centers on the catalyst's surface, the vicinal Pd(0)-(L2) species could coalescence by Ostwald ripening [5; 40] to produce Pd-NPs which provoke a change in the catalytic performance in comparison with homogenous Pd-SAOPHEN, PdAS(2)-MA and PdAS(1)-MA resins catalysts.
# Coastal Sargassum Level Estimation from Smartphone Pictures
## 4 Experiments and Discussion
### Classification with Deep Convolutional Neural Networks  
Recently, deep neural networks have proven to effectively solve several complex computer vision tasks. One advantage of such discoveries is the fact that the knowledge acquired in some context can be moved to a similar domain by using transfer learning. Transfer learning reuse the learned network weights for initializing a custom network that is intended to solve the target task. In this work, the target task is the classification of Sargassum level while the source task is the Imagenet classification [28].  
There are at least two ways for using transfer learning: feature extraction and fine tuning. Feature extraction keeps the feature extraction capabilities of an already trained network by freezing the convolutional layer weights while a new classifier is attached to the feature extraction; then the new classifier is trained according to the target task. On the other hand, fine tuning replaces the classifier according to the target task and then the full network is fitted to the target task. Our hypothesis is that transfer learning can provide a good approximation of the Sargassum classification. In the next section, we present several experiments in order to validate our proposal.
# A Survey on Botnets: Incentives, Evolution, Detection and Current Trends
### Contribution and Research Questions  
This systematic literature review presents a survey on the incentives and evolution of botnets as well as detection and mitigation mechanisms developed to combat botnets. The main contribution of this paper is a diverse overview of these topics according to mostly peer-reviewed literature during the period 2005-2021, with a particular focus on botnet detection and mitigation. The second contribution is an analysis of the evolution of botnets and mitigation strategies in order to develop an idea of the current trends and challenges within the field of botnets.  
The specific research questions asked by this paper are:  
1. What incentives are behind the development of botnet attacks?
2. How have botnet attacks evolved over time?
3. What has the research industry proposed to mitigate the threat of botnets?
4. What current trends and challenges related to botnets have been identified by contemporary research?