esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Setup
### Case Study
#### 5.1.2 Data Acquisition  
The acquisition system has been chosen for the proposed approach based on the flexibility and manageability that an industrial monitoring system should have. Generally, a production line does not have a complete acquisition system containing all the information about the state of all variables involved in the process but instead exposes only a few details related to the production state, such as the production ID, the alarms, the on/off state, the number of items to be produced, etc. [65; 66]. In addition to this, installing sensors on the production line can be expensive from an economic point of view and sometimes invasive in terms of wiring and CE certification. Based on these considerations, an energy IoT monitoring module, the S604 by Seneca [67], that includes a three-phase network analyzer, has been installed following the electrical connection guidelines as shown in Figure 9.  
The module carries out one reading per second of over 250 different electrical parameters (current, voltage, power factor, displacement power factor, active energy, etc.) and performs calculations (every ten seconds) to define complex variables such as the minimum, maximum, or average value of another 150 variables. Furthermore, the harmonic  
Figure 8: Sifim’s production line. **(1)** Loading station. **(2)** Working area. **(3)** Unloading station. **(4)** Complete overview.  
Figure 9: Seneca S604’s IoT module. **(1)** Electric schema. **(2)** Installed module.  
calculation is performed every seven seconds. Due to the limited hardware resources, only 88 variables are acquired every second, as listed in Table 1.  
In the Figure 10, some values of the acquired variables during the operational tests are shown. Next, the data are saved in a csv file and organized by day in order prepare the dataset for feeding into the proposed model.
###### Abstract  
Accurate knowledge of the flow rate is essential for hydraulic systems, enabling the calculation of hydraulic power when combined with pressure measurements. These data are crucial for applications such as predictive maintenance. However, most flow rate sensors in fluid power systems operate invasively, disrupting the flow and producing inaccurate results, especially under transient conditions. Utilizing pressure transducers represents a non-invasive soft sensor approach since no physical flow rate sensor is used to determine the flow rate. Usually, this approach relies on the Hagen-Poiseuille (HP) law, which is limited to steady and incompressible flow. This paper introduces a novel soft sensor with an analytical model for transient, compressible pipe flow based on two pressure signals. The model is derived by solving fundamental fluid equations in the Laplace domain and converting them back to the time domain. Using the four-pole theorem, this model contains a relationship between the pressure difference and the flow rate. Several unsteady test cases are investigated and compared to a steady soft sensor based on the HP law, highlighting our soft sensor's promising capability. It exhibits an overall error of less than 0.15% for the investigated test cases in a distributed-parameter simulation, whereas the HP-based sensor shows errors in the double-digit range.  
soft sensor; high-frequency compressible volumetric flow rate determination; frequency analysis; Laplace domain; residue theorem; convolution +
Footnote †: journal: Physics Letters A  
1
Footnote 1: Institute for Fluid Power Drives and Systems (ifas), RWTH Aachen University, 52074 Aachen, Germany  
2
Footnote 2: Independent Researcher, 85088 Vohburg, Germany  
3
Footnote 3: Correspondence: [EMAIL_ADDRESS]; Tel.: +49-241-80-47743
## 2 System Model of the Proposed Scheme
### Rate Matching and RS Encoder  
For data transmission, codewords of RS codes are embedded in order to correct errors instead of directly embedding data in encrypted images. For data hiding, the encrypted image is segmented into a number of non-overlapping blocks sized \(s\times s\). Then, the maximum number of blocks, \(N_{\text{b}}\), that can be embedded in an encrypted image is \(\lfloor\frac{L}{s}\rfloor\lfloor\frac{M}{s}\rfloor\). Therefore, in rate matching, the generated message sequence is needed for embedding the codeword. To explain this process, the RS code parameter should be discussed.  
RS codes [28] are defined in a Galois field, GF\((2^{q})\), where \(q\) is a positive integer. Then, the parameters of RS\((n,k)\) codes with length \(n\) and dimension \(k\) are defined as \(n=2^{q}-1,\tau=\lfloor\frac{(n-k)}{2}\rfloor\), where \(\tau\) denotes the maximum number of symbol errors that the RS decoder can correct. The code rate of RS codes are defined as \(k/n\). It is known that the message and codeword of RS codes can be expressed as:  
\[m(X)= m_{0}+m_{1}X+\cdots+m_{k-1}X^{k-1}\] \[c(X)= c_{0}+c_{1}X+\cdots+c_{n-1}X^{n-1},\]  
where \(m(X)\) and \(c(X)\) are a message polynomial and a codeword polynomial, respectively. The generator polynomial is defined as:  
\[g(X)=(X-\alpha)(X-\alpha^{2})\cdots(X-\alpha^{2\tau})=g_{0}+\cdots+g_{2\tau}X ^{2\tau},\]  
Figure 1: Block diagram of the proposed data-hiding system.  
where \(\alpha\) is a primitive element of GF(2\({}^{q}\)). The encoding for systematic RS codes is defined as:  
\[c(X)=p(X)+X^{2\tau}m(X),\]  
where \(p(X)\) is the parity polynomial with degree \(<2\tau\) and is a remainder polynomial when \(X^{2\tau}m(X)\) is divided by \(g(X)\).  
The maximum number of codewords, \(N_{\mathrm{c}}\), in the encrypted image is \(\lfloor\frac{N_{\mathrm{b}}}{\ln q}\rfloor\). In the 'rate matching' in Figure 1, the \((kqN_{\mathrm{c}})\)-bit message is the input for the RS encoder. The remaining \(N_{\mathrm{b}}-kqN_{\mathrm{c}}\) bits are zero-padded. The choice of codes parameter and block size \(s\) in the proposed system is determined by the capability of data recovery. The \(s\) is dependent on the calculation of spatial correlation, and the difference of the correlation for each data increases as the size \(s\) increases. The increase of \(s\) leads to enhanced data recovery, but it needs to reduce room for data-hiding in the encrypted image. When the code rate of RS codes decreases, the error correcting capability of the RS codes is enhanced since the \(\tau\) of RS codes increases. The decreasing code rate also leads to better data recovery, but transmission efficiency also becomes worse. Since the data recovery in the proposed systems is strongly related to image recovery, the choice of parameters for better data-recovery are required according to the purpose or environment of the systems. Therefore, discussion about the proper design of the size of \(s\) and code parameters is needed, and the analysis of performance according to code rate and the size of \(s\) is explained in the Experimental Results.
## 2 Problem Definition  
The problem configuration under analysis is shown in Figure 1. The position in this configuration is specified by the coordinates \(\{x,y,z\}\) with respect to an orthogonal, Cartesian reference frame with the origin \(\mathcal{O}\) and the three base vectors, \(\{\mathbf{i}_{x},\mathbf{i}_{y},\mathbf{i}_{z}\}\), forming in the indicated order a right-handed triad. The position vector is then written as \(\mathbf{r}=xi_{x}+y\mathbf{i}_{y}+z\mathbf{i}_{z}\). The time coordinate is \(t\), and the continuous time-convolution operator is represented by \(*\). The Heaviside unit-step function is denoted by \(\mathrm{H}(t)\), and the Dirac-delta distribution is \(\delta(t)\). Partial differentiation is denoted by \(\partial\), which is supplied with the pertaining subscript. For instance, to differentiate with respect to \(x\), we use \(\partial_{x}\).  
We shall analyze the EM-field penetration through a bounded aperture in an unbounded PEC screen. The PEC plane lies in \(z=0\), and the aperture occupies a rectangular domain \(\mathcal{S}=\{-\Delta_{x}<x<\Delta_{x},-\Delta_{y}<y<\Delta_{y},z=0\}\) with \(\Delta_{x,y}>0\). In the present analysis, it is assumed that the maximum dimension of the aperture is relatively small with respect to the spatial support of the exciting EM pulse. The surrounding medium is assumed to be linear, isotropic, and loss-free in its EM properties. It is described by (real-valued, positive scalar) electric permittivity \(e_{0}\) and magnetic permeability \(\mu_{0}\). The corresponding EM wave speed is \(\epsilon_{0}=(\epsilon_{0}\mu_{0})^{-1/2}>0\), and the wave admittance is denoted by \(Y_{0}=(\epsilon_{0}/\mu_{0})^{1/2}>0\).  
The aperture is supposed to be irradiated by the (causal) incident EM wave field (denoted by superscript \(\mathrm{t}\)), which is specified by its electric-field strength, \(\mathbf{E}^{i}(\mathbf{r},t)\), and magnetic-field strength, \(\mathbf{H}^{i}(\mathbf{r},t)\). The incident EM field is not necessarily a plane-wave. To account for the presence of the aperture, we next define the scattered EM wave field (denoted by superscript \(\mathrm{e}\)) as the difference between the total fields, \(\{\mathbf{E},\mathbf{H}\}(\mathbf{r},t)\), and excitation EM wave fields (denoted by superscript \(\mathrm{e}\)), viz.  
\[\{\mathbf{E}^{s},\mathbf{H}^{s}\}(\mathbf{r},t)=\{\mathbf{E}-\mathbf{E}^{e},\mathbf{H}-\mathbf{H}^{e}\}( \mathbf{r},t), \tag{1}\]  
for all \(\mathbf{r}\in\mathbb{R}^{3}\) and \(t>0\), where the excitation fields have the meaning of total fields in the _absence_ of the aperture (i.e., with the \"short-circuited aperture\"). Since \(\mathbf{i}_{z}\times\mathbf{E}^{e}(\mathbf{r},t)=\mathbf{0}\) over  
Figure 1: Rectangular aperture in a PEC plane.  
the entire \(z=0\) plane for all \(t>0\), the use of the explicit-type boundary condition applying to the total EM field on the PEC screen in Equation (1) implies that \(\dot{\mathbf{i}}_{z}\times\mathbf{E}^{\text{s}}(\mathbf{r},t)=\mathbf{0}\) on the PEC screen for all \(t>0\).
## 3 Results
For sequences 2 and 5, the \(p\)-value was larger than 0.05 with values of 0.3955 and 0.6177, respectively. This was the expected outcome, as answering the questions from sequence 5 had the least difference between the groups, regarding the time passed before the questions needed to be answered and Section 2 being the one designated as hard, with material very unfamiliar to the students. The efficiency of the proposed learning concepts is also illustrated in Figure 10 by presenting the percentage of participants who correctly answered the questions for both methodologies. Figure 10 shows that, in the case of the S-LM methodology, there were no respondents who scored less than 40% of correct answers, while for NS-LM, there were three respondents who scored less than 40%. The difference between the two methodologies is also illustrated by the fact that, when segmented linear multimedia was used, 18 respondents answered the questions with an accuracy higher than 60%; whereas, in the case of the non-segmented linear multimedia, the number amounted to eight respondents. It is obvious that significantly more participants had a higher percentage of correct answers when the S-LM methodology was used rather than the NS-LM methodology. The QoE assessment was carried out by applying the previously proposed methodology, and the results obtained are given in Table 4.  
Figure 10: Number of respondents with a different percentage of correct answers. The table contains \(\text{MOS}_{\text{S-LM}}\) and \(\text{MOS}_{\text{NS-LM}}\) values for the QoE assessment by applying S-LM and NS-LM methodologies, respectively. The results indicate that the students were very satisfied with the multimedia content's technical characteristics and were fully acquainted with the testing procedures for each session. Segmented multimedia also improves students' confidence and experience while answering questions. It can be seen that the QoE was higher in all aspects in the case of the S-LM methodology. It is important to emphasize that the students declared that it is much easier for them to answer the questions if the video segmentation methodology is used with pauses and questions between the segments.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 6 Open Challenges and Future Research Directions
### High False Positive Rates  
A persistent issue for many defenses is the difficulty of distinguishing malicious interests from legitimate but unsatisfied interests. Current solutions, especially those using simple heuristics or even some ML, can still misidentifify bursts of valid traffic as attacks. Misclassification leads to false positives, which result in legitimate requests being throttled or dropped, effectively causing a denial of service to honest users, the very thing we want to avoid. Future research is needed to reduce these false positives, which might involve developing more nuanced detection that considers context (e.g., recognizing a genuine flash crowd vs. a botnet) or dynamically adjusting thresholds based on network conditions. Techniques like adaptive baselines (where normal behavior is continuously learned and the system flags deviations that are statistically significant) could help [33].
## 5 Evaluation
### Intrusion Detection Performance
The CIC-IDS2017 data set was collected in a virtual environment designed to emulate real PCAP data. Lastly, the Bot-IoT data set comprises data from five IoT devices, including smart fridges, garage doors, weather monitors, lights, and thermostats. While UNSW-NB15 dominated in terms of the number of attack types and CIC-IDS2017 led in the instance ratio of shared attack types with the target domain, ultimately, the IoT Intrusion data set, generated from the same smart home environment, demonstrated the highest performance. This underscores the significant influence of the data collection environment on the target domain's performance. In situations requiring the selection of a source domain for transfer learning, it is recommended, as evidenced by our paper, to prioritize the collection environment over the shared attack types for better effectiveness.
## 4 Conclusions  
In this paper, multiple variables were used to predict future vegetation condition index (VCI) as a proxy to drought conditions. The predictor variables were 1-3-month lags of precipitation and vegetation indices. The methodology used two techniques in a setup where the general additive model (GAM) statistical approach is first run against several possible model configurations. The GAM method is then used to reduce the model space and by extension the set of viable variables. After variable selection and with the model space reduced, a brute force approach is then employed using the artificial neural networks (ANN) approach.  
One month ahead forecasts of the VCI using the best ANN model showed good performances with \(R^{2}\) ranging between 0.71 and 0.83. After grouping into five drought classes, 63 to 71% of the months were correctly classified--the remaining months showed a maximum difference of one class. Prediction skills deteriorated with lag times longer than 1 month. The poor performance of variables with longer times lags, in the prediction of drought events was established. Since the approach builds multiple models, prior to evaluation in the search for the best model, it is possible to support model ensembling that supports the use of multiple models in the prediction of future events.  
The study demonstrated that the model space reduction is beneficial to the building of neural networks that are known to generally have slower training times as compared to other approaches. The automation of the model training and model validation processes, and the measure of performance with a view to quantifying and avoiding overfitting, make for a scalable approach.  
conceptualization, C.A. ([PERSON]); formal analysis, C.A. ([PERSON]); investigation, C.A. ([PERSON]); methodology, C.A. ([PERSON]), R.O., P.W.W. and C.A.; supervision, R.O. and P.W.W.; validation, R.O., P.W.W. and C.A. ([PERSON]); visualization, C.A. ([PERSON]); writing--original draft, C.A. ([PERSON]), R.O. and P.W.W.; writing--review & editing, C.A. ([PERSON]).  
Figure 16: Performance gain/loss in \(R^{2}\) for models with different lags of the same predictor variable.  
**Funding:** This research received no direct external funding. The data used in the study was however, partly funded by the European Commission's funding under a grant contact to the Institute for Surveying, Remote Sensing and Land Information, University of Natural Resources and Life Sciences (BOKU).  
**Acknowledgments:** Our appreciation to the National Drought Management Authority for providing the data from the operational drought monitoring system. We are also grateful to [PERSON] for the continued discussion of the ideas of the paper towards shaping it to have outputs applicable in an operational drought monitoring environment. The help from [PERSON] especially with plotting in R cannot go unmentioned. The helpful contributions of the editors and reviewers are also acknowledged.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Threat Model
### Selective Existence (Totally Selfish Behaviour)  
A misbehaving node is said to exhibit a totally selfish behaviour if it does not participate in network operations, but uses the network for its advantage to enhance its performance and save its own resources, such as energy. This selfish node only makes its presence known to other nodes in the network whenever it wants to send its own packets. These selfish behaviours are known as selective existence attacks [3]. When a selfish node wants to send its own packet to another node, it performs a route discovery and then sends the required packets. When the node no longer needs to use the network, it returns to the silent mode. It ensures that all control packets that are presented during route discovery requests by other nodes are dropped. After a given period, its neighbouring nodes update their various route entries and delete their entries with routes with the destination to this node; this makes the node invisible to the network. Employing the passive acknowledgement mechanism used by some of the existing TRM systems mentioned in Section 2 will not be able to detect these totally selfish behaviours.  
For instance, Figure 1 shows a MANET with nine nodes consisting of various paths from a source to a destination. If Node 1 wants to send a packet to Node 9, there are several possible paths to the destination assuming that all of the nodes are cooperative. If Node 4 and Node 6 were non-cooperative nodes exhibiting totally selfish behaviours, this means that all data from Node 1 must go through Node 3 (1-3-5-8-9) or through Node 5 (1-5-8-9), as seen in Figure 2.  
Figure 1: A MANET with nine cooperative nodes.  
Figure 2: A MANET with two uncooperative nodes.  
Consequently, if Node 2 or Node 3 wants to send a packet to Node 7, Node 8 or Node 9, it must go through Node 5. As a result of the totally selfish behaviours exhibited by Node 4 and Node 6, Node 5 is at a high risk of exhausting its limited energy resources due to the energy-constrained nature of nodes in MANETs. This type of behaviour creates an unfair environment if left unchecked. If Node 4 and Node 6 were cooperative, this will lessen the burden on Node 5, due to the fact that the amount of energy consumed during packet forwarding will be shared amongst the three nodes (with the assumption that the costs of the links of the three paths are almost the same). This will, in turn, elongate the life-time of the overall network.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology
### Benchmark Models
#### 3.6.3 CNN 3D-1 Layer [65]  
The 3 DCNN model is based on 3D Convolutional Neural Networks, taking (3,3,2) as input tensor size and expanded to (3, 3, 2, 1) with the 3D Convolutional Neural Network that can handle batch, spatial input data. It begins with a 3D version of a convolutional layer, containing 32 2 \(\times\) 2 \(\times\) 1-pixel filters to perform 2D convolutions through the input channels while preserving channel dimension. However, non-linearity is introduced with ReLU activation functions. The output is a feature map set reshaped to a 1D-vector that flows through a fully connected layer with 16 ReLU hidden units. The network also has a final dense output layer with 2 units and Softmax activation that provides probability distributions of the target classes.
## 3 Proposed Real-Time Data Fusion Method Based on a New Distance Metric
### Data Fusion for Trajectory Information  
The fusion of trajectory information of the object requires a different approach from object type information. The trajectory information for the detected object consists of the measured position, velocity, and the measurement accuracy of the sensor. As explained previously, sensors have an inherent limitation in accuracy. We will use a \"_multisensor weighted data fusion_\" technique [53; 54; 55] so as to combine the reported sensing results and accuracy. The technique provides a fast and robust scheme to overcome the dynamic changes in the vehicular environment.  
Let us define \(v_{k}^{j}\) and \(\gamma_{k}^{j}\) as the velocity of object \(k\) measured by vehicle \(j\) and the associated accuracy, respectively. These data are transmitted to nearby vehicles through V2V communications.  
Figure 4: Example of multisource inter-vehicle data fusion process.  
We denote \(f_{k}^{i}\) as the fused output based on the data gathered at vehicle \(i\) about object \(k\). As in the case of classification, only the vehicles with \(m_{k}^{j}(\mathrm{E})>0,\forall j\in\mathcal{M}_{i(k)}(n)\) have the data on object \(k\). Thus, we have that  
\[f_{k}^{i}=\sum_{j\in E_{k}^{i}}w_{k}^{j}v_{k}^{j} \tag{26}\]  
where \(w_{k}^{j}\) is the weight of \(v_{k}^{j}\), and \(\sum_{j\in E_{k}^{i}}w_{k}^{j}=1\). We assume that the sensor measurements are independent and follow a normal distribution with standard deviation \(\gamma_{k}^{j}\) (measurement accuracy) [56; 57]. According to the extreme value theory of multivariate function [54; 55; 58], we can obtain the weights corresponding to the minimum mean square error as below.  
\[w_{k}^{j}=\frac{1}{(\gamma_{k}^{j})^{2}\sum_{l\in E_{k}^{i}}\frac{1}{(\gamma_{ k}^{l})^{2}}}. \tag{27}\]  
The weights in (27) can be interpreted that large variance (low accuracy) results in low weight on measurements. Note that this scheme does not require any prior information, and computing (26) has polynomial time complexity, \(O(|E_{k}^{i}|)\). In our experiment, on Intel(R) Core(TM) Xeon 2.4 GHz, it takes only \(7\times 10^{-6}\) s on average in combining 1000 points of velocity data, which is a negligible delay. Likewise, we can combine the location data as well, which are used to estimate the object trajectories.
## 3 Results
### Comparison of Homogeneous and Heterogeneous Techniques  
Figure 13 summarizes the degradation efficiency of the optimum homogeneous and heterogeneous photocatalytic techniques applied, as well as the results obtained from the experiments conducted only in the presence of an oxidant (H\({}_{2}\)O\({}_{2}\) and S\({}_{2}\)O\({}_{8}\)\({}^{2}\)).  
As observed from the results, the conventional photo-Fenton system demonstrates the highest efficiency in degrading the target dye. Nevertheless, this does not diminish the effectiveness of heterogeneous photocatalytic techniques--particularly when combined with persulfate ions--which successfully address the key limitations commonly associated with homogeneous systems. These include the requirement for highly acidic conditions (typically around pH3) and the problematic formation of iron-containing sludge during the reaction [45]. Furthermore, solid catalysts not only exhibit excellent photocatalytic activity, often comparable to their homogeneous counterparts, but also offer additional advantages such as reusability and ease of separation from the treated solution. Their confirmed recyclability reinforces their potential for sustainable and practical applications in wastewater treatment processes [46].