esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Network Layer  
DoS attacks can exhaust the energy on the victim node by sending additional unnecessary packets at the network layer to establish a link between the source and the sink. Thus, this attack can delete or change routing information by injecting false information into networks. Thus, most energy drain attacks belong to approaches at network layers.  
In this section, we review recent approaches in terms of attack types, that is, selective forwarding, sinkholes, wormholes, Sybil attacks, and floods. Prior to the details of each approach, we reviewed a previous survey paper [48] for DoS attacks in WSNs. In this study, the effect of the DoS attack and its countermeasures at each layer is analyzed. However, a survey paper for DoS attacks is yet to be presented. Therefore, summarizing recent approaches in this section is crucial. As for the DoS attack, [PERSON] and [PERSON] [49] presented how to detect DoS attacks efficiently. Their lightweight approach, the deep learning-based defense mechanism, was designed to detect attacks in the data forwarding phase. In the training phase, the input to detect the attack behavior consists of the received signal strength, the number of packets received per second, the packet receiving delay, the status of data changed, the data dropping ratio, and the data forward ratio. After creating the deep learning prediction model, the query processor interacts with this model to identify the types of DoS attacks.
## 2 Study Area and Data
### Regional Soil, Weather, and Crop Information  
The soil map for the study area was derived from the Soil and Terrain database of primary Chinese data at a scale of 1:1 million; this map was compiled using enhanced soil information within the framework of the FAO's Land Degradation Assessment in Drylands (LADA) program. Data on the physical and chemical properties of the soil profiles were collected from the Soil Species of Hebei Province [42].  
Meteorological data were derived from the National Meteorological Information Center, China Meteorological Administration and included daily estimates of maximum and minimum temperature, precipitation, sunlight hours, wind speed, and relative humidity. In addition, radiation was estimated at the station level based on sunlight duration [43]. In this study, daily meteorological parameter data for the study area were interpolated as a raster map with grid cells using ANUSPLIN software [44].  
Detailed crop management information was also surveyed, including the dates of planting and harvest, planting depth and spacing, planting density, irrigation dates and volumes, fertilization date and volume, phenological calendar, and other data. Winter wheat yield data were measured at 53 field plots, and the regional statistical yields for 11 counties were obtained from the Hebei Rural Statistic Yearbook [45].
## 2 Materials and Methods
### Flowsheet Calculations Using the AMUSE Code  
The single centrifugal contactor distribution ratio data measured for each section of the proposed flowsheet was used in conjunction with the Argonne Model for Universal Solvent Extraction (AMUSE) Code [39] to evaluate the proposed ALSEP flowsheet for testing in 1 cm centrifugal Contactors. For the extraction and scrub sections, the goal was to achieve greater than 99% recovery of the minor actinides (Am and Cm) with less than 0.1% extraction of non-Ln metals such as other fission products and transition metals.  
The following adjustments to the reference flowsheet [27] and assumption were made:  
1. The co-extraction section was reduced to 6 stages and scrub 2 section increased to 8 stages to enable more complete Mo scrubbing.
2. Distribution data from single-stage contactor tests were used in the AMUSE code with no further adjustment of stage efficiency.
3. Results using the final \(D\) values (steady state) as well as with average \(D\) values (excluding first and second samples) during the single-stage tests were both used to evaluate recovery.
4. Scrub 1 \(D\) values were assumed the same as the extraction \(D\) values.  
The experimental \(D\) values utilized in the modeling from the single-stage contactor tests are summarized in Table S7. Both the final \(D\) values (steady state) as well as the average \(D\) values (excluding first and second samples) during the single-stage tests were listed and were used to determine the recovery of each element. These results are presented in Table 3. The calculations predicted that 99.9% Am and 99.99% Cm would be recovered. The trivalent lanthanides recovery was calculated to range from 10% for La to >99.99% for Gd. The heavier Ln(III) are better extracted in the ALSEP system, as reflected by the increasing recovery. Yttrium was calculated to be recovered with 47-78%, depending on which \(D\) values were used for the calculations. The number of Scrub 2 stages was predicted to be suitable to reach a carryover of <0.01% of the Mo into the MA separation section. The data from the single-stage tests showed measured extraction \(D\) values to be low (approx. 0.3) in the transient samples but 4.9 when equilibrated. However, even assuming a \(D\) value of 4.9 in the extraction stages, the recovery of Mo was found to be less than 0.1% due to the low scrub \(D\) values. Iron was found to extract only slightly (0.2-0.6%).  
Based on the AMUSE calculation, the flowsheet shown in Figure 7 was developed and tested in the countercurrent test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Recovery in the Loaded Solvent after Scrub 2** \\ \hline  & **Steady State** & **Average** \\ \hline Am & 99.87\% & 99.92\% \\ Eu & \textgreater{}99.99\% & \textgreater{}99.99\% \\ Cm & 99.99\% & 99.97\% \\ La & 10.00\% & 10.70\% \\ Ce & 70.20\% & 72.80\% \\ Pr & 96.90\% & 97.40\% \\ Nd & 99.70\% & 99.80\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Predicted recovery into solvent exiting Scrub 2 section.
# Attack Categorisation for IoT Applications in Critical Infrastructures, a Survey
## 5 Analysis of the Categorisation Methodologies  
In the previous section, we presented eight categorical methodologies using in the literature. Each of these categorisation techniques were explored and the various approaches were defined and explained from the standing point of their respective authors. An overview of these approaches can be seen in Table 2.  
In this table, we can clearly see that some approaches have been employed more than others. For example, the Attack Type categorisation using _DoS_, _Probing_, _R2L_, and _U2R_ has been used in seven different publications. However, we also notice that both the Attacker Position (_Inside/Outside_) and the Attacker Implication (_Active/Passive_) have also been used numerous times, in five and six publications, respectively. Furthermore, in taking a closer look at the publications from these two categories, we can identify that the publication [13] used both categories. This highlights the fact that, in many cases, a single categorisation technique is insufficient to both structure and organise cyber attacks. Although each of these methodologies have proven their worth through their different publications, we hope to provide a novel analysis from the standing point of IoT applications in CIs. Certain approaches analysed here provide insufficient information as to their proper use. The first would be the Attack Severity categorisation, which organises attacks based upon the severity of their impact. However, as explained previously, their severity can vary dependent on many factors, such as system use, hardware type, or even network paradigm. For example, categorising a jamming attack would significantly depend on the use of wireless technologies in the system. Furthermore, the severity of jamming of an IoT device would vary depending on the application of the device itself. For instance, if the device is providing temperature readings to a weather station, then jamming the data exchange results is a low severity since the data is not considered essential. On the other hand, if the device was part of a personal medical surveillance device, such as a pacemaker or smart insulin pump, jamming the exchange between the device and the hospital servers could result in severe consequences for the patient, making the severity sky rocket. The second categorisation of issue is the **Wireless Ad-Hoc Networks-Use-Case Specific** approach. Their usage of simply separating attacks, such as targeting MANETs or WSNs, does not provide any precise information regarding the attacks themselves. This is only useful if covering multiple types of wireless direct networks; however, to be able to structure attacks in a more detailed way, other approaches must be used in tandem. When analysing IoT devices that are constantly communicating with the outside world, the use of a **Network-Layer-Oriented** approach is appealing. Using this approach, all network-based attacks can be organised depending on the layer in which they occur. However, the limitation of this approach is just that: network-based attacks.
## 3 Methodology  
The algorithm consists of two phases:  
1. Visual Odometry: At the moment of GPS loss, the algorithm estimates a latitude and longitude coordinate based only on monocular vision. The accumulated error in this phase will depend on the precision with which the scale is calculated. This phase is explained in detail in Section 3.1.
2. Satellite image preprocessing: This stage is carried out offline before the flight and is described in detail in Section 3.2. This phase is composed of the following subprocesses: We start with a flight plan, which can either be provided by the user or calculated using an exploration algorithm, in this case, Rapidly-exploring Random Trees (Section 3.2.1). From the flight plan, the keypoints of the satellite image are filtered to reduce their cardinality (Section 3.2.2). Finally, the problem of spatial indexing is solved using a Quad-tree to efficiently find the 2D points closest to the path (Section 3.2.3).
3. Reduction in accumulated error: We search for correspondences between the UAV image and the geo-referenced map to correct the estimation errors from the previous phase. The reduction error phase is explained in Section 3.3. This process is performed online and onboard the UAV.  
To clarify the foundational premises and conditions of this research, as well as to define the scope and limitations of the study, we present the following assumptions.  
**Assumption 1**.: _The aircraft is equipped with a GPS signal from take off until some time after reaching the operating altitude._  
**Assumption 2**.: _The camera is always pointing towards the ground._  
**Assumption 3**.: _We have the terrain elevation map (in our case, it was obtained from the National Institute of Statistics and Geography in Mexico)._  
**Assumption 4**.: _We have access to IMU and barometer data, even in the absence of GPS information._
# A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
## 4 Results and Evaluation
### HDR-to-xHDR Conversion: Subjective Evaluation
Consequently, this results in an xHDR image with less brightness and contrast than expected, closely resembling the original HDR input. Figure 19: Results of our subjective experiment, comparing user preferences for xHDR images generated through our proposed HDR to xHDR pipeline against the original HDR video sequences. The vertical bars indicate the frequency of participants’ preferences, showing whether they favored one type of video sequence over the other or found both sequences to be of equal quality.
## 1 Introduction
### Main Functionalities of Smart Grids  
Smart grids are complex systems that offer \"more than simply the sum of the constituent parts\" [26]. With respect to power transmission and distribution networks, smart grids integrate interconnected and geographically widely distributed hardware and software components, both on the demand and on the supply side, and pool their resources to create richer functionalities, such as the following:  
1. Advanced metering and monitoring for close to real-time transmission and reception of data for information, monitoring, and control purposes of the energy network to acquire/provide feedback for the grid operation and enable consumers to better manage consumptions.
2. Active network management for operational optimization through predictive maintenance, energy network remote reconfiguration, and recovery scheme activation in almost real time.
3. Flexibility services from DERs, such as distributed generation, energy storage assets, and demand-side response, leveraging end-user flexibility.
4. Smart charging services, such as vehicle-to-grid or vehicle-to-home solutions (for battery electric and plug-in hybrid vehicles) and additional growth of electrification grade (i.e., heating and cooling), increasing RESs' grid hosting capability.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 3 Experimental Procedures  
### Batch Adsorption Experiments  
The effect of concentration on the adsorption rates was investigated using different concentrations (20, 40, 60, 80, 100 mg/L) for 720 min. Contact time was studied between 5 and 720 min using a working solution of 100 mg/L. The effect of pH was carried out at pH 2, 4, 6, 8 and 10 using 100 mg/L for 720 min. The temperature effect was measured between 288 and 308 K using 100 mg/L. All the parameters were investigated using 20 mg of the adsorbent in 50 mL of the working solution. The batch experiments were conducted in triplicate.  
Equations (1) and (2) were used to calculate (\(q_{e}\)) cadmium and methylene adsorbed (mg/g); (\(C_{o}\)) and (\(C_{e}\)) denote the initial and final amounts of cadmium and methylene left in the solution (mg/L), respectively. The volume (\(V\)) of the working standard was measured in (mL) and the (m) mass of the adsorbent was measured in (g).  
\[qe=\frac{(C_{o}-Ce)V}{W} \tag{1}\]  
\[\text{Removal }(\%)=\frac{(C_{o}-Ce)}{Co}\times 100 \tag{2}\]
## 6 Conclusions and Future Work  
In this paper, we investigate the challenging problem of MLLM task offloading and resource allocation in UAV-assisted SEC networks. We formulate an MINLP problem to minimize the weighted sum of system latency and energy consumption while satisfying accuracy requirements for diverse MLLM services. The problem poses significant challenges due to the hybrid discrete-continuous action space, involving discrete offloading decisions and continuous power allocation and UAV trajectory optimization. To address these challenges, we proposed a novel AD-SAC algorithm that effectively handles the hybrid action space by employing separate agents for discrete and continuous actions. Our approach leverages the maximum a posteriori policy optimization to integrate the decoupled policies, ensuring stable learning and efficient exploration. The AD-SAC framework enables intelligent decision-making that considers the trade-offs between UAV-based edge computing with lower latency but limited accuracy and satellite-based computing with higher accuracy but increased communication overhead. Extensive experiments demonstrate the superiority of our proposed AD-SAC algorithm over state-of-the-art baselines.  
While our work demonstrates the advantages of SEC-based MLLM deployment, a limitation of this study is the lack of a comprehensive quantitative comparison with cloud-based deployment scenarios. In future work, we will expand the research scope to include cloud computing deployment scenarios for MLLM services and provide concrete real-world examples and quantitative data, such as latency metrics from actual IoT deployments, bandwidth consumption patterns for multimodal data transmission, and comparative analysis between cloud, UAV, and SEC paradigms. This expanded investigation will offer comprehensive empirical evidence to justify the bottlenecks in different IoT environments.  
Conceptualization, H.Y.; Software, H.Y.; Validation, H.Y.; Formal analysis, H.H.; Writing--original draft, H.Y.; Writing--review & editing, H.H., Z.Z. ([PERSON]) and Z.W.; Supervision, Z.Z. ([PERSON]); Project administration, Z.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 2 Related Work
### Review-Based IPA  
IPA categorizes and prioritizes product attributes based on their relative importance and performance [13; 15]. As shown in Figure 1, it is performed based on a four-quadrant graph with importance and performance scores on the \(y\)- and \(x\)-axes, respectively. Each product attribute belongs to one of the four quadrants, and different improvement strategies are suggested according to the quadrant as follows:  
* Quadrant I: Keep up the good work. Customers consider the attributes in this quadrant important, and they are satisfied with the current performance of the attributes. * Quadrant II: Concentrate on improving the attributes in this quadrant. Customers consider these attributes very important but feel low satisfaction with them. * Quadrant III: Give low priority to the attributes in this quadrant. There is no need to improve the performance levels of these attributes as they are relatively less important to customers. * Quadrant IV: Be cautious about possible overkill because the attributes in this quadrant are less important to customers. Overinvestment should be avoided. The conventional approach for conducting IPAs is through customer surveys, which can be time-consuming and resource-intensive [25; 26]. Recently, review analytics has recently emerged as an alternative for more efficient IPAs. Table 2 summarizes the studies on review-based IPA, where sentiment analysis is typically employed to assess the importance and performance levels of product attributes, which are then presented in an IPA chart. Attribute importance measures can be classified into two types: appearance and significance. Appearance indicates the term frequency of each product attribute, which is the proportion of reviews that mention the attribute. Significance, on the other hand, represents the relative impact of an attribute on overall customer satisfaction. Because it is implicit, an additional approach such as regression analysis, decision tree classification, or artificial neural network analysis is required for estimation. For attribute performance, most studies used sentiment analysis results directly. The percentage of positive reviews and sentiment scores have been the most common measures. [PERSON] et al. [27] used online customer reviews to conduct an IPA comparing two types of smartphone applications for hotel reservations: one offered by online travel agencies and the other by hotel brands. To assess attribute importance and performance, the authors used the proportion of references and the proportion of positive reviews, respectively. [PERSON] and [PERSON] [28] combined a topic modeling technique called latent Dirichlet allocation (LDA) with an IPA approach. The authors defined product attributes and attribute-related words based on the topics obtained from the LDA, and the frequency and sentiment intensity of these words were used to determine attribute importance. To estimate attribute performance, the average star ratings of reviews that included attribute-related words were analyzed.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 5 Simulation Results and Performance Evaluation  
The overall training goal here is to determine weight matrices, gauge the bias vectors that reduce the loss model for the entire time steps during the training phase, as modeled next. See Table 6 for the overall parametric settings selected for the layers in the LSTM network.  
Dataset: The work here leverages and applies the _BigData Challenge_ dataset in [35] collected in the City of Milan. This dataset studies the behavior of subscribers (MSs) in sectorized geographical grids (each of 200 m), in terms of the requested data forms, such as call-in, call-out, SMS-in, and SMS-out.  
Figure 6 shows the LSTM network predicts the beam index over various time steps at the MS (likewise at the BS), i.e., beam index used in order to achieve link recovery once a blockage is introduced. It is noticed here that index \(i=5\) features the highest popularity class among other indices. The scheme also yields a high approximation between the ground truth and prediction pattern. Namely, the proposed scheme achieves high accuracy and thereby it can successfully predict the subsequent beam indices with high success probabilities, once the network is sufficiently trained. Moreover, the accuracy of the proposed scheme is further studied by computing the loss function shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Parameter** & **Value** \\ \hline LSTM layers & No. of cells in LSTM layers & 4: [50,50,50,50] \\ Dense layers & Activation Function & ReLU \\ Hyper-parameters & Batch size & 16 \\  & Epochs & 350 \\  & Dropout rate & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameter settings for the LSTM Model.  
Loss Function: The training goal aims to reduce the loss model, which is represented in the form of the mean square error (MSE) between the prediction vector \(\hat{Y}\) and the actual ground truth \(Y\) at the upcoming time step, is evaluated over \(U\) predictions points. Note that both the prediction model and the ground truth have the same distribution over the same time period. The loss function for every (\(t\!-\!1\)) th time step is modeled as,  
\[Loss\big{(}Y_{u},\hat{Y}_{u}\big{)}=\frac{1}{U}\sum_{u=1}^{U}\big{(}Y_{u},\hat{ Y}_{u}\big{)}^{2} \tag{17}\]  
Figure 7 depicts the error function for the proposed scheme versus the ground truth computed over 350 epochs. Here, the reduced MSE achieves approximate fit, which enhances accuracy and success probability of the proposed recovery scheme. The proposed beam restoration method due to blockage leveraging the deep learning model is now compared and assessed versus existing prominent schemes in the literature, in terms of recovery times and energy consumption. See Table 7 for the overall system parametric settings.  
Figure 6: LSTM-based beam index prediction.  
Figure 7: LSTM-based model loss.