esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 KDN-Based Heavy-Hitter Identification Approach
### Architecture and Modules
#### 4.2.3 Heavy-Hitters Application Module
Finally, it is important to mention that MiceDCER asks the controller for updating the defined rules and generating new PMACs when topology modifications occur in the network. Aiming at evaluating our MiceDCER-based HH-APM, MiceDCER was compared with IP-based and MAC-based routing. The evaluation was performed in a FatTree topology that consists of \(p\) pods, which are management units interconnected by the (\(\eta\)/2)\({}^{2}\) switches that make up the core layer. Each pod consists of \(p\)/ edge switches and \(p\)/2 aggregate switches, and each ToR switch, connects to \(p\)/2 end hosts [63]. Therefore, a \(p\) -ary FatTree topology supports \(p\)\({}^{3}\)/4 hosts. Table 10 summarizes the number of rules generated by MiceDCER, IP-based and MAC-based routing when \(p\) is 16, 20, 24, 28, 32, 36, 40, and 48. The evaluation results reveal that, in the edge  
Figure 6: Flowcharts of the three major procedures performed by MiceDCER. layer, MAC-based and IP-based routing install more rules than our HH-APM running MiceDCER. Considering these results, we can conclude that MiceDCER reduces the number of rules per edge switch significantly when compared with other routing solutions. The results also reveal that, in the aggregate layer, MAC-based routing installs much more rules than MiceDCER. The IP-based routing installs approximately the double of rules than MiceDCER does. Thus, we can conclude that MiceDCER reduces the number of rules per aggregate switch significantly when compared with the MAC-based and IP-based routing. In the core switch layer, the results expose again that MAC-based routing installs more rules than MiceDCER. In turn, the IP-based routing installs about the same amount of rules as MiceDCER. We can conclude that MiceDCER reduces or at least generates the same number of routing rules to install in the core switches. For more information about MiceDCER, its design, implementation, and evaluation, we refer the reader to Amezquita-Suarez [35].
## Data and Methodology
### Characterizing Land Surface Phenology in VODs and NDVI
Vegetation Optical Depth (VOD) was derived as a negative natural logarithm of vegetation transmittance [VOD = -log\({}_{\mathrm{e}}\)(tc) = -ln(tc)]. In a previous study [21], we fitted the core growing season VODs from the three microwave frequencies (6.925 GHz, 10.65 GHz, and 18.7 GHz) using the CxQ model in a two-step process. The fits in that study were good (coefficients of determination were larger than 0.90 (R\({}^{2}>0.90\)) in time and space) but limited to around the peak of the growing season. Here, however, we need to consider a larger fraction of the growing season; thus, we needed a different approach. The VOD shapes for the entire growing season are not well fitted using the CxQ model. VOD is sensitive to aboveground biomass structure, including crop residues, and to the vegetation water content found in entire aboveground biomass, while NDVI is sensitive primarily to vegetation greenness. VOD has a different seasonal progression shape that is not suitably captured by a quadratic curve compared to that of NDVI. Therefore, we used the maximum VOD value approach to determine cropland vegetation PH and TTP. That is, the observed maximum VOD value was taken as PH VOD, while the corresponding AgDD was taken as TTP. Similar approach was also used for the NDVI for a direct comparison with the CxQ model fit derived PH and TTP NDVI. The PH NDVI and PH VOD values were regressed to see the relationship between cropland peak greenness and peak biomass and water content. We then used analysis of covariance (ANCOVA) to test whether the slopes and/or intercepts from the linear regression model fit were significantly different by microwave frequency: the microwave frequencies (6.925, 10.65, and 18.7 GHz) were the independent categorical effect variables, the peak greenness was covariate, and PH VOD was the response variable. The first statistical assumption for ANCOVA is that the covariate (peak greenness in our case) is uncorrelated with other independent variables (microwave frequencies), and the second assumption is that the covariate (peak greenness) is correlated with the dependent variable (PH VOD; [45]). In all cases, \(p\leq 0.05\) was considered significant. Figure 3: Example of the filtering and temporal alignment of the vegetation optical depth (VOD) data with MODIS data. The unfiltered VOD time series appears in the black circles, the eight-day forward moving average filtered VOD data appears at the blue circles, and the MODIS NBAR NDVI appears in blue pluses.
## 3 System Architecture and Modeling
### Appliance Dynamics Modeling and Setup  
#### 3.3.1 State-Space Model  
In order to apply the proposed scheduling algorithm, the dynamics of all the appliances are modeled mathematically into a discrete linear time-invariant state-space representation. The discrete state-space model of a linear system with control inputs (\(m\)), outputs (\(p\)), state variables (\(n\)), and external disturbances (\(r\)) is written as in Equation (1) [48]:  
\[\begin{split}&\mathbf{x}_{k+1}=A\mathbf{x}_{k}+B\mathbf{u}_{k}+E \mathbf{d}_{k}\\ &\mathbf{y}_{k}=C\mathbf{x}_{k}.\end{split} \tag{1}\]\(\mathbf{x}\in\mathbb{R}^{n}\) is the _state vector_, \(\mathbf{u}\in\mathbb{R}^{m}\) is the _control vector_, \(\mathbf{y}\in\mathbb{R}^{p}\) is called the _output vector_, and \(\mathbf{d}\in\mathbb{R}^{r}\) is called the _disturbance vector_. Similarly, \(A\in\mathbb{R}^{n\times n}\) is the _state matrix_, \(B\in\mathbb{R}^{n\times m}\) is the _control matrix_, \(C\in\mathbb{R}^{p\times n}\) is the _output matrix_, and \(E\in\mathbb{R}^{r\times n}\) is the _disturbance matrix_. \(k\) denotes the time instant.
## 2 Materials and Methods
### Structural and Morphology Characterization  
The X-ray powder diffraction (XRPD) patterns were measured by a PANalytical X'Pert Pro X-ray diffractometer (Ni-filtered Cu _Ka_1 = 1.54060 A, \(V\) = 40 kV, \(I\) = 30 mA). The recorded XRPD patterns were compared with the reference standard from the Inorganic Crystal Structure Database (ICSD--26204 and ICSD--9444, respectively) to check if the pure hexagonal nHAp and nFAp were formed.  
The morphology and chemical composition of obtained materials were measured using a field emission scanning electron microscope (FE-SEM, FEI Nova NanoSEM 230) equipped with an energy-dispersive X-ray spectrometer (EDX, EDAX Apollo X Silicon Drift Detector) compatible with Genesis EDAX Microanalysis Software v. 6.41. The specimen preparation technique recommended for nanomaterials was used. Each sample was dispersed in alcohol, and then a droplet of the suspension was placed on the carbon stub. This stub was then heated in a vacuum oven for 30 min at 100 \({}^{\circ}\)C and subsequently cooled to room temperature. The SEM images were recorded at 5.0 kV in a beam deceleration mode, improving imaging parameters such as resolution and contrast. The samples for EDS measurement were prepared by embedding them in carbon resin (PolyFast Struers) and pressing them with an automatic mounting press (CitoPress-1 Struers) to obtain a large and flat area. The EDS analyses were performed at 30.0 kV over a large area (250 \(\times\) 200 \(\mu\)m) of the measured materials. Signals from three randomly selected areas were collected to ensure satisfactory statistical averaging. The IR spectra were performed using a Nicolet iS50 FT-IR (Thermo Scientific, Waltham, MA, USA) spectrometer supplied with an Automated Beamsplitter exchange system (iS50 ABX containing DLaTGS KBr detector), a built-in all-reflective diamond ATR module (iS50 ATR, Thermo Scientific Polaris(tm)) and a HeNe laser as an IR radiation source. Polycrystalline mid-IR spectra were collected in the 4000-400 cm\({}^{-1}\) range with spectral resolution of 0.5 cm\({}^{-1}\).
## 2 Related Work
### Object Detection  
Before delving into object detection, it is essential to explore image classification, which serves as a fundamental task that significantly influences subsequent domains. When it comes to categorizing an extensive dataset such as ImageNet, pioneering models such as AlexNet [12] and GoogLeNet [13] introduced innovative architectures. For example, AlexNet featured an eight-layer structure, while GoogLeNet introduced the Inception module, proposing a deeper network with fewer parameters than AlexNet. However,  
Figure 3: The 360-degree view and mask results of a camel as a non-rigid object.  
due to its complex structure, GoogLeNet poses challenges in subsequent applications. VGGNet [14] incorporated deeper networks with 16 or 19 layers using 3 \(\times\) 3 filters, proving that the use of more filters improved performance but came with computational demands. ResNet [15] addressed the gradient vanishing problem by introducing residual blocks, allowing for even deeper networks while reducing computational load compared with VGGNet. These models laid the foundation for various image-processing tasks, including object detection and image segmentation.  
Object detectors are typically categorized as two-stage and one-stage detectors. Two-stage detectors involve the generation of object proposals in the first stage, followed by object classification with predefined labels in the second stage. In contrast, one-stage object detectors directly predict the object class and location in a single pass over the image.  
Object detection has seen the incorporation of the transformer [16] architecture, which has demonstrated success in natural language processing tasks. This has notably improved performance, with DETR [17] being a pioneering example. DETR is based on the transformer encoder-decoder architecture and is capable of predicting all object bounding boxes simultaneously without the need for separate region proposal networks or non-maximum suppression [18]. This simplicity and superior performance have spurred further studies, resulting in enhanced versions such as DINO and H-DETR, which provide a more robust baseline [19; 20]. CoDETR has adopted a novel approach with auxiliary heads using one-to-many label assignment models such as ATSS and Faster R-CNN [21; 22; 23].  
Another noteworthy one-stage object detection method is the YOLO series [24], which is known for its ability to perform object detection in a single pass, making it exceptionally fast and suitable for real-time applications. The latest iteration, YOLOv8 [25], surpasses its predecessors in terms of speed and accuracy. YOLO-NAS [26] introduces a new architecture derived from VGGNet, offering a balanced trade-off between accuracy and latency while leveraging AutoNAC quantization for enhanced efficiency and performance.
## 5 Discussion
### Deployment and Sensor Node Coverage  
UAN sensor nodes need to be deployed in order to enable Smart City applications. However, UAN classes differ in the need and strategy for the deployment of infrastructure components (_i.e._, backhaul and gateway). We next compare the UAN classes in this regard, and also point out the related implications in terms of sensor node coverage.  
In LR-WPAN UANs, infrastructure has to be expressly deployed, which incurs installation and maintenance cost. However, the deployment can be optimized for providing coverage to sensor nodes located in specific points of interest for the intended applications.  
In contrast, WLAN UANs avoid the need for an express deployment of backhaul and gateway nodes. Nevertheless, they are limited to the fact that WLAN infrastructure has been designed prior to the deployment of sensor nodes. Thus, they may not provide optimized (or even sufficient) coverage to all the sensor node locations.  
On the other hand, MNO UANs allow, especially when 2.5G is used, the deployment of sensor nodes almost without geographic constraints, whereas SO UANs coverage is not currently the same, although their worldwide deployment is underway. In MNO or SO UANs, infrastructure is provided by the operator. However, when a sensor node is deployed, care must be taken to assure that the link between the sensor node (in its intended location) and the corresponding base station has sufficient quality. The flexibility in this regard is limited since the cellular infrastructure is typically managed by a third party.  
Finally, DTN UANs do not require the deployment of fixed infrastructure throughout the city. Instead, a relatively low number of vehicles have to be provided with wake-up and gateway functionality. Of course, sensor nodes must be located close enough to data mule routes.
## 2 Mathematical Model of Energy and Quality of Service at the Local and Remote Cluster
### The Remote Cluster Model  
Similarly, the remote cluster (RC) is assumed to incorporate a rack of \(R\) processors and related peripheral devices, with a simplified power profile given by the expression:  
\[\Pi_{R}=A_{R}+R.B_{R}\rho_{R} \tag{8}\]  
where:  
* \(A_{R}\) is the power consumption in the RC related to the internal networking and shared memory systems (main and secondary) plus the power consumption for cooling and ventilation;* \(B_{R}\) is the workload proportional power consumption, including cooling, per processor in the RC rack; and
* \(\rho_{R}\) is the individual utilisation (percentage of time it is busy) of each of the \(R\) processors in the RC rack.  
The computational workload in the RC is represented by a flow of \(\lambda_{R}\) jobs per second, each of which on average takes \(S_{R}\) of processing time, and jobs are equally distributed to the \(R\) processors, so that:  
\[\rho_{R}=\frac{\lambda_{R}S_{R}}{R} \tag{9}\]  
As a result, the total expenditure of energy per job in the RC is the ratio of power consumption to the total job arrival rate, or:  
\[E_{R}=\frac{A_{R}}{\lambda_{R}}+B_{R}S_{R} \tag{10}\]  
Assuming the same average response time formula \(W(F,\lambda)\), function of job arrival rate and job service time distribution, we have:  
\[W_{R}=W(F_{R},\frac{\lambda_{R}}{R}) \tag{11}\]
## 5 Results
### Qualitative Analysis with Gemini 1.5 Flash  
To complement the quantitative evaluation of redaction effectiveness, we conducted a qualitative analysis using Gemini 1.5 Flash. This analysis aimed to assess the LLM's ability to understand the content of the drug labels after redaction, providing a different perspective on the system's performance. Instead of focusing on identifying PII for redaction, we tasked Gemini 1.5 Flash with explaining the purpose and usage instructions of the medication based on the redacted label image. This tests whether the redaction process preserves enough information for a separate LLM to understand the label's core meaning.  
Figure 1 shows an example of a redacted label image. The text extracted by the combined EasyOCR and Tesseract from the original, unredacted image contained errors, as discussed in Section 5. The LLM-based redaction stage then identified and masked PII, including the hospital name, phone number, patient name, and hospital number.  
We then provided the redacted image to Gemini 1.5 Flash, along with the following prompt (translated from the provided Thai prompt):  
Figure 1: Example of a redacted drug label image. PII has been masked with black boxes.  
Explain the medication and instructions on this drug label.  
Gemini 1.5 Flash generated the following response (translated from the provided Thai response):  
From the image, the medication is Folic acid 5 mg, which is used to treat and prevent vitamin B9 (Folate) or folic acid deficiency. It is often used in women who are pregnant or planning to become pregnant to prevent birth defects, such as neural tube defects.  
Instructions:  
How to use: Take 1 tablet once a day.  
When to take: After breakfast.  
Precautions: You should consult a doctor or pharmacist before using this medication, especially if you have any underlying medical conditions or are taking other medications.  
This result demonstrates that, despite the redaction of PII and the presence of OCR errors in the original text extraction, Gemini 1.5 Flash was able to correctly identify the medication (Folic acid), its purpose, and the dosage instructions from the redacted image. This indicates that the redaction process, while removing sensitive information, successfully preserved the essential information necessary for understanding the label's content. This qualitative analysis supports the quantitative findings, suggesting that the system achieves a good balance between privacy protection and data utility. It also demonstrates the potential of using a separate LLM for downstream tasks on redacted label data.
## 2 Architecture and Operational Analysis
### Queue-Length Models
#### 2.3.3 Model 3: Maximum Queue Length in Current Cycle  
By combining the model for the queue length of delayed vehicles from the previous cycle and the model for the queue length of vehicles arriving ahead of schedule in the current cycle, one can jointly derive the model for the maximum queue length in the current cycle as  
\[\begin{array}{ll}L_{p,k}^{max}&=L_{p,k-1}^{det}\times\delta^{det}+L_{p,k}^{ arr}\times\delta^{arr}\\ &=\left\{\frac{\left[L_{g}+\left(g_{ls,sr}-g_{ls,s}-T_{os,k-1}\right)\times V_{ g}\right]\times V_{pw}}{V_{g}+V_{pw}}\right\}{V_{g}+V_{pw}}\right\}\times\delta^{ det}+\\ &\left\{\left\{\begin{array}{c}V_{pw}\times V_{pw}\times V_{pw} \times\\ V_{pw}-V_{pw}\times\end{array}\times\\ &\left\{\begin{array}{c}L_{g}-\frac{\left[L_{g}+\left(g_{ls,sr}-g_{ls,s}-T_{os,k-1}\right)\times V_{g}\right]\times V_{pw}}{V_{g}+V_{pw}}-V_{pw}}{V_{g}+V_{ pw}}-V_{pw}\\ &\frac{\left[L_{g}+\left(g_{ls,sr}-g_{ls,s}-T_{os,k-1}\right)\times V_{g}\right] \times V_{pw}}{V_{g}+V_{pw}}-T_{os,k}\end{array}\right\}\times\delta^{arr} \right.\end{array}\right\}\times\delta^{arr}\end{array} \tag{8}\]  
Figure 4: Queue of vehicles arriving ahead of schedule in the current cycle and being delayed.  
where \(L^{max}_{p,k}\) = maximum queue length in the current cycle, \(\delta^{det}\) = categorical variable that can take on one of two possible values (0 and 1), indicating, respectively, the absence and presence of queued or delayed vehicles from the previous cycle, and \(\delta^{arr}\) = categorical variable that can take on one of two possible values (0 and 1), indicating, respectively, the absence and presence of queued vehicles arriving ahead of schedule in the current cycle.
# A Wideband Termination Based on Laser-Scribed Lossy Microstrip Line Structures
## 3 Fabrications  
### Device Fabrication  
The aqueous dispersion of graphene oxide (GO) was synthesized from graphite flakes via a modified Hummers method [13] at a GO concentration of 3.5 mg/mL. For the sample preparation, the microstrip transmission lines of 5 cm in length were customized using commercial copper-clad laminates (RO4003C, thickness: 0.508 mm, Rogers Corporation). Then, the GO dispersion was spread onto the microstrip samples and dried on a hot plate at 50 \({}^{\circ}\)C in an ambient atmosphere. The areal density of GO was 0.5 mg/cm\({}^{2}\).  
To transfer GO film to rGO film resistor, laser direct writing was conducted using a 532 nm continuous wave laser with a typical laser power of 45 mW. The laser beam was focused onto the GO film surface through a laser focus lens (GCO-150111, 25 mm focal length, Daheng Optics, Beijing, China). The spot size was about 100 \(\upmu\)m. A motorized platform moved the samples at a typical writing speed of 2 mm/s and a 50 \(\upmu\)m sweep spacing to pattern the GO film.
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
## 3 CLAMBDA-Search Model
### Error Analysis
#### 3.3.1 Baseline Length Check  
For the minimum requirement of the ambiguity objective function, the baseline obtained by CLAMBDA should conform to the known baseline length condition. However, due to wrong data and excessive searching time, a satisfactory solution may not be obtained within a specified searching duration. The threshold is set as 0.03 m in this paper.
## 3 Results and Discussion
(**d**) Recycled PE-Fur/Bism bars. In contrast to the virgin PE-fur/Bism-2:1 material, it is seen that the recycled material exhibits a slightly lower storage modulus and loss modulus, indicating somewhat less mechanical strength. However, this difference is only marginal and proves that even after complete recycling, the mechanical properties are still equivalent. The same phenomenon described earlier (the shift of the softening point to higher temperatures after each cycle) is also observed for the recycled polyester resin. This again suggests increasing difficulty for the rDA reaction to occur after multiple heating cycles, possibly due to conformational changes of the adducts resulting in DA adducts with higher thermal stability (e.g., from _endo_ to _exo_[1]). When comparing the softening temperatures of the newly prepared test specimens with those of the recycled ones, it is seen that the softening points of the recycled specimens lie at higher temperatures. Since the recycled test specimen has been exposed to more heating cycles than the virgin material, it is fair to assume that the recycled polyester network possesses a relatively higher amount of thermally stable adducts. Again, this ultimately results in the increased temperatures needed to induce the rDA reaction. This phenomenon, however, does not seem to have any significant effect on the mechanical properties of the material. The mechanical properties of the recycled material are unchanged throughout the subsequent DMTA cycles. This shows that the recycled material does not yield poorer mechanical properties, even after multiple measurements at various temperatures. Ultimately, the fact that the mechanical properties of the virgin and the recycled PE-fur/Bism material are comparable indicates that this indeed constitutes a fully thermoreversible recyclable polymer. Figure 7: Dynamic Mechanical Thermal Analysis of recycled specimens of PE-fur/Bism (**a**) Storage modulus E\({}^{\prime}\). (**b**) Loss modulus E\({}^{\prime\prime}\). (**c**) Damping factor tan \(\delta\).
# AWEncoder: Adversarial Watermarking Pre-Trained Encoders in Contrastive Learning
## 2 Preliminaries
### Contrastive Learning
#### 2.1.1 SimCLR [4]  
SimCLR aims to learn representations by maximizing agreement between differently augmented views of the same sample via a contrastive loss function in the latent space. Briefly, SimCLR consists of three modules that are _data augmentation_, _feature encoder_\(f\) and _projection head_\(g\). Data augmentation transforms a sample \(\mathbf{x}\in\mathcal{X}\) into two augmented views \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) treated as a positive pair. The feature encoder \(f\) extracts representation vectors from augmented examples, for example, \(\mathbf{h}_{i}=f(\mathbf{x}_{i})\) for the augmented sample \(\mathbf{x}_{i}\). The projection head \(g\) maps representations to the space where the contrastive loss is applied, for example, \(\mathbf{z}_{i}=g(\mathbf{h}_{i})=g(f(\mathbf{x}_{i}))\). With \(N>0\) samples in a mini-batch, we are able to collect \(2N\) augmented samples. Two augmented samples determined from the same sample constitute a positive pair. Otherwise, it is treated as a negative pair. Let \(\sin(\mathbf{u},\mathbf{v})=\mathbf{u}^{\intercal}\mathbf{v}/||\mathbf{u}|||| \mathbf{v}||\) represent the dot product between \(\ell_{2}\) normalized \(\mathbf{u}\) and \(\mathbf{v}\) (that is, cosine similarity). The loss function for a positive pair \((\mathbf{z}_{i},\mathbf{z}_{j})\) and a negative pair \((\mathbf{z}_{i},\mathbf{z}_{k})\) is then defined as [4]:  
\[\mathcal{L}_{i,j}=-\log\left(\frac{\exp\bigl{(}\sin\bigl{(}\mathbf{z}_{i}, \mathbf{z}_{j}\bigr{)}/\tau\bigr{)}}{\sum_{k=1}^{2N}\mathbb{I}[k\
eq i]\cdot \exp\bigl{(}\sin(\mathbf{z}_{i},\mathbf{z}_{k})/\tau\bigr{)}}\right), \tag{1}\]  
where \(\tau\) denotes a temperature parameter and \(\mathbb{I}[k\
eq i]\in\{0,1\}\) is an indicator function equal to \(1\) if, and only if \(k\
eq i\).