esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
## 4 Experiments and Result Analysis
### Experiment Setup  
All experiments in this section have been carried out using the following configurations:  
* Datasets: We have used two well-known datasets, namely Corel-5K and MSRC v2. **Corel 5K:** This is a publicly available dataset that is commonly used for the task of image annotation. It is composed of 5000 images from 50 photo stock CDs annotated with 374 labels in total. Each CD includes 100 images on the same topic, annotated with 1-5 keywords per image. Due to the unbalanced nature of label distribution over images, most previous works consider using a few numbers of concepts (i.e., a subset of images) that appear frequently. However, we evaluate our proposed algorithm on both subset and complete datasets to prove its effectiveness and tolerance to the problem of unbalanced label distribution. Corel-5K is already split into train and test subsets comprising 4500 and 500 images, respectively. **MSRC v2:** This dataset contains 591 images grouped into categories having 23 concepts, each image explained using 1-7 keywords. MSRC v2 is split into train and test subsets comprising 394 and 197 images, respectively. Table 1 lists the essential characteristics of the two datasets used.  
Evaluation Metrics: To evaluate the performance of the proposed scheme, four widely known metrics for image annotation tasks have been opted for, namely precision (P), recall (R), F1-score (F1), and N+. The formulas to calculate these quantities are given respectively by the following equations:  
\[P=\frac{1}{|S|}\sum_{s\in S}\frac{|images\ annotated\ correctly\ with\ label\ s|}{|images\ annotated\ with\ label\ s|}\times 100\% \tag{4}\]  
\[R=\frac{1}{|S|}\sum_{s\in S}\frac{|images\ annotated\ correctly\ with\ label\ s|}{|images\ having\ label\ s\ in\ the\ ground\ truth|}\times 100\% \tag{5}\]  
\[F1=2\times\frac{P\times R}{P+R} \tag{6}\]  
N+ = the number of concepts assigned correctly at least once. (7)  
It must be mentioned that region features are extracted from the final fully connected layer of the CNN model. This is because the information collected from the final FC layer is more suited to characterizing areas, especially when there is no stable color distribution (i.e., objects rather than textures) [45]
# Defending against FakeBob Adversarial Attacks in Speaker Verification Systems with Noise-Adding +
## 6 Performance Evaluation
### Performance Evaluation of Adaptive FakeBob Attacks against the Noise-Adding Defense
#### 6.4.2 Updating the Objective Function  
Next, we study the adaptive FakeBob attack that both bypasses the plugin function and updates the objective function to generate adversarial audios against SV systems with noise-adding. Table 5 shows the ASR and the total running time of adaptive attacks with \(\epsilon=0.002\) in generating adversarial audios in a GMM SV system without the defense. It can be observed that when the value of \(a\) in Equation (15) increases, the targeted score of the SV system for the attack increases, and as a result, it takes longer time for the attack to obtain adversarial audios. Moreover, the ASR in Table 5 is based on the threshold \(\theta+a\theta\), instead  
Figure 6: Performance of the strategy of bypassing the plugin function by adaptive FakeBob attacks with different \(\epsilon\) in GMM SV systems.  
of \(\theta\), so the value of the ASR decreases when \(a\) increases. Specifically, when \(a\) increases from 0 to 30%, the ASR decreases from 90.24% to 84.03%, whereas the total running time increases from 31 h 3 min to 44 h 47 min.  
Figure 7 shows the performance of adaptive FakeBob attacks with \(\epsilon=0.002\) against the noise-adding defense during the test time. It can be observed that when \(\sigma=0\), an SV system is based on the threshold \(\theta\), and adaptive attacks with different values of \(a\) have a similar value of the ASR (i.e., about 90%). Moreover, by increasing \(a\), the resulting adversarial audios are more robust against noise when \(\sigma\) is not large. For example, when \(\sigma=0.002\), the values of the ASR are 39.40%, 45.36%, and 49.81% for attacks with \(a=10\%\), 20%, and 30%, respectively. On the other hand, when \(\sigma=0.005\), the ASR is about 21% for all cases. From Table 5 and Figure 7, it can be observed that an adaptive FakeBob attack with a higher value of \(a\) can better mitigate the effect of noise-adding when \(\sigma\) is not large, but takes longer time to generate adversarial audios, which indicates a tradeoff between the robustness and the efficiency of adaptive FakeBob attacks. On the other hand, the noise-adding defense still provides a considerable level of protection against adaptive FakeBob attacks. For example, the noise-adding method with \(\sigma=0.002\) can reduce the ASR from 89.40% to 49.81% for the adaptive FakeBob attack with \(a=30\%\).
# Electrodegradation of Selected Water Contaminants: Efficacy and Transformation Products
## 2 Materials and Methods
### Electrochemical Treatment  
The EO process was applied to a model wastewater containing three different pollutants dissolved in distilled water (Table 1). In order to increase the electrical conductivity to approx. 3 mS/cm, a supporting electrolyte was added to each of the model wastewaters. The type and concentration of the supporting electrolyte was chosen based on our previous research [40].  
EO was performed in a batch electrochemical reactor made of glass (Figure 1). All experiments were performed at an ambient temperature with the initial sample volume of 400 mL, stirred by a magnetic stirrer at 300 rpm. A BDD anode on a Nb substrate (Metachem, Luedenscheid, Germany) and stainless steel cathode (EN 1.4301/AISI 304) were used, both with a useful area of 58.43 cm\({}^{2}\). The distance between the electrodes was 1 cm. Before each experiment, electrodes were cleaned to remove contaminants and connected to a power source (GW Instek PSP-2010, Taipei, Taiwan) and the electrolysis was performed at the desired current density--Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pollutant** & **Concentration** & **Electrolyte** & **Current Density** & **pH** \\ \hline \multirow{3}{*}{\(1H\)-benzotriazole} & \multirow{3}{*}{20 mg/L} & 2 g/L NaCl & & 5.75 \\  & & 2 g/L Na\({}_{2}\)SO\({}_{4}\) & 10 mA/cm\({}^{2}\) & 6.01 \\  & & 0.005 M H\({}_{2}\)SO\({}_{4}\) & & 2.19 \\  & & 2 g/L NaCl & & 6.06 \\ Dibutyl phthalate & 5 mg/L & 2 g/L Na\({}_{2}\)SO\({}_{4}\) & 10 mA/cm\({}^{2}\) & 5.79 \\  & & 0.005 M H\({}_{2}\)SO\({}_{4}\) & & 2.13 \\ Triton X-100 & 10 mg/L & 2 g/L Na\({}_{2}\)SO\({}_{4}\) & 5 and 10 mA/cm\({}^{2}\) & 5.49 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of EO experiments.  
Figure 1: Scheme of a laboratory batch reactor: 1—electric power source, 2—electrochemical cell, 3—anode; 4—cathodes, 5—magnetic stir bar, and 6—magnetic stirrer.
# Agreement Technologies for Coordination in Smart Cities
## 3 Applications
### Emergency Medical Service Coordination
#### Dynamic Redeployment
The probabilities \(p_{ci}\) can be obtained by tracking historical data on emergency cases. Furthermore, it is possible to model different situations (like seasons, day of the week, time interval, etc.) through different probability distributions. In the application of Lloyd's algorithm, at a given point in time, we used the positions of all idle ambulances as the set of initial generation points \(S\). We applied a fixed number of iterations (in the experiments we used 50) and the resulting set \(S\) represents the \"recommended\" distribution of ambulances at this particular moment. The new positions are sent to the ambulances, and the ambulances are situated such that they can move towards that positions. Because of road and parking conditions, the obtained positions are considered as indications of an area. That is, once given such an area, the ambulance driver will decide which is the most appropriate waiting location in that area. We used the Euclidean norm as a distance measure to generate the Voronoi regions for the ambulances. While in a real traffic scenario, as is our case, the Euclidean distance is a rather imprecise approximation of real distances on the road network, from a global perspective, and assuming a rather homogeneous connection between different points of the region of interest (as is usually the case in many urban areas), the Euclidean norm seems to work reasonably well for our purposes. Furthermore, using \"road-network distances\" when calculating the Voronoi regions is a rather complicated task that would increase the computation complexity considerably. Similar to the ambulance assignment problem, the dynamic nature of an EMA service implies that the optimal positions of the idle ambulances will change when changes in the environment occur. In order to cope with such changes, the ambulance positions are recalculated dynamically whenever any of the following events occur:  
* An ambulance that was assigned to a patient has been deassigned. * An ambulance has finished a patient assistance mission and has changed its state from occupied to idle. * An ambulance that was idle has been requested to assist a patient. It changes its state from idle to assigned. * The situation that determines the underlying probability distribution changes (e.g., a distribution for a new time interval, day of the week, or season should be used).
## 1 Introduction  
With the growth of the aging demographic worldwide, by 2050, the number of elderly people is expected to reach 1.5 billion, accounting for 16% of the world's total population. Since the aging population is rapidly increasing, and imbalance, which leads to falls, is a major concern for this demographic, a specific focus on balance rehabilitation methods and technologies are critical. According to the Centers for Disease Control and Prevention, within the United States each year over twenty-five percent, or over 14 million, adults sixty-five (65) years old or older have suffered a fall; nearly 40% of these fall victims are treated for fall-related injuries [1,2]. One in ten falls results in an injury severe enough to limit daily activities or require medical attention [3]. Annually, falls among older adults account for approximately three million emergency department visits and one million hospitalizations [3]. Beyond physical harm, the fear of falling can lead to reduced physical activity, muscle weakness, and an increased risk of future falls [3]. The consequences of falls can be severe (death, injury, and hospitalization), cause increased anxiety about daily activities (leading to reduced mobility), and ultimately cause a loss of independence, affecting the quality of life. The prevalence of frailty in people aged older than 65 years is 7.2%, increases with age, and represents the main risk factor for incident falls, disability, hospitalization, and death [4]. The diagnosis of frailty comprises several domains, including weight loss, weakness, exhaustion, slowness, and low physical activity [4]. Falls are a major concern for all older adults. But aside from the loss of balance leading to the act of falling, one may possess an over-concern (fear) related to the anticipation of falling--this can ultimately limit one's confidence and willingness to go about their daily activities [5]. Due to gait and balance deficiencies being the primary risk factors for falls in older persons, it is crucial to understand the effects of training on the stability of balance and gait [6]. Further, it is also important to examine the dynamic interaction between (actual) balance ability and balance confidence (perceived balance ability); lower perceived balance ability (or decreases in balance confidence for daily activities) can limit one's quality of life.  
Traditional rehabilitation programs, while beneficial, often face limitations in accessibility, engagement, and/or long-term adherence. As a result, emerging technologies such as robotic-assisted balance training (RABT) and Virtual Reality (VR) have gained attention as an innovative and potentially effective alternative for improving balance, mobility, and fall prevention among older adults.
## 5 Analysis
### Activity Duration Variability  
Having set the (initial) activity durations, the activity duration variability was modeled with normal distributions and lognormal distributions predominantly (although a few simulations were also run using uniform distributions, see dataset XII in Table 2). Normal distributions were used because they are the most common in academic settings and theoretical studies. Lognormal distributions were also used because, as explained earlier, this distribution seems to be the closest for modeling real construction project activities. Furthermore, building on the recent findings of [PERSON] et al. [30] for construction projects, we assumed that the median of our distributions would equal the initial (deterministic) activity durations defined in the previous subsection. This would ensure that half of the time the activities would end soon and half of the time late. This would avoid biasing the results toward predominantly early or late projects.  
Next, activity duration variability was reproduced by randomly allocating a coefficient of variation between 0.10 (low variability) and 0.3 (high variability) to each activity as measured by [PERSON] et al. [30] (in log\({}_{10}\) scale). The median in the normal distribution is equal to the mean, which indicates that the stochastic activity duration values were generated with the following expression:  
\[d_{i}\sim\mu_{i}\text{-}Normal^{-1}\ (mean=1,\ st.dev.=CV_{i}). \tag{14}\]  
In the expression above, \(\mu_{i}\) represents the default (deterministic) duration of each activity (defined in the previous subsection). \(Normal^{-1}(\cdot)\) represents the quantile function of the normal distribution, that is, the one in charge of generating the normally distributed stochastic values. \(CV_{i}\) is the coefficient of variation randomly chosen for each activity in the range [0.1, 0.3]. It is worth noting that this range of \(CV_{i}\) values in natural scale produces significantly less variability than in log scale (see Equation (15)). However, values of \(CV_{i}>0.3\) are very likely to generate negative activity durations, which makes no sense and should be avoided.  
In the case of lognormally distributed activity duration variability, the median does not coincide with the distribution mean unless it is transformed into a log scale first. That is why the following expression was adopted to generate thousands of (stochastic) activity duration values for each activity in the Monte Carlo simulations:  
\[d_{i}\sim\mu_{i}e^{Normal^{-1}(mean=0,\ st.dev.=2.302 CV_{i})}. \tag{15}\]  
As before, the coefficient 2.302 is included in Equation (15) to change the base from 10 to \(e\). Variables \(\mu_{i}\), \(Normal^{-1}(\cdot)\), and \(CV_{i}\) are analogous to those in Equation (14).
# All-Optical Modulation and Ultrafast Switching in MWIR with Sub-Wavelength Structured Silicon
###### Abstract  
We investigated and optimised the performance of the all-optical reflective modulation of the Mid-Wave Infrared (MWIR) signal by means of the optically-pumped sub-wavelength-structured optical membranes made of silicon. The membranes were optically pumped by a 60-femtosecond, 800-nm laser, while another laser operating in the MWIR ranging between 4 and 6 \(\upmu\)m was used to probe the optical response and modulation. We were able to achieve the conditions providing the modulation depth of 80% using the pump fluence of 3.8 mJ/cm\({}^{2}\). To get a better insight into the performance and the modulation mechanism, we developed an optical model based on a combination of the Wentzel-Kramers-Brillouin approximation, Drude and Maxwell-Garnett theories. The model allowed us to estimate the values of the dielectric function, carrier concentration and scattering rate of the optically-excited membrane in the MWIR range. Using the model, we optimised the performance and found the conditions at which the reflective modulation can be operated with the ultrafast response of 0.55 ps and modulation contrast of 30%.  
ultrafast; all-optical modulation; MWIR modulator; sub-wavelength structured silicon +
Footnote †: journal: Applied Physics Letters B
## 3 Results
### Model Comparison Experiments  
To further explore the applicability and robustness of the model, reduce the impact of randomness and data bias, and provide strong evidence for the reproducibility and interpretability of the results, five groups of comparative experiments were set up in this study [27]. In these experiments, the improved YOLOv10, YOLOv10, YOLOv12, CornerNet, and SSD networks were used for model training and testing on the dataset. The comparison results are shown in Table 3. Compared with YOLOv10, YOLOv12, CornerNet, and SSD, the improved YOLOv10 achieved increases in precision of 3.40%, 9.22%, 16.25%, and 13.51%, in recall of 10.05%, 12.05%, 18.59%, and 17.53%, in F1 of 6.75%, 10.62%, 17.40%, and 15.51%, and in mAP of 11.95%, 12.59%, 20.31%, and 18.04%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** & **Layers** & **Parameters** & **Gradients** \\ \hline YOLOv10 & 81.98 & 78.03 & 80.97 & 402 & 2,497.778 & 2,497.762 \\ YOLOv10 + W & 80.33 & 81.75 & 84.93 & 323 & 2,766,354 & 2,744,834 \\ YOLOv10 + H & 80.49 & 82.47 & 85.67 & 369 & 3,431,302 & 3,431,286 \\ YOLOv10 + L & 80.87 & 81.35 & 85.21 & 402 & 2,497.778 & 2,497,762 \\ YOLOv10 + W + H & 82.97 & 84.88 & 89.48 & 339 & 2,825,894 & 2,804,374 \\ YOLOv10 + W + L & 81.03 & 83.57 & 87.33 & 323 & 2,766,354 & 2,744,834 \\ YOLOv10 + H + L & 83.85 & 86.37 & 90.16 & 369 & 3,431,302 & 3,431,286 \\ YOLOv10 + W + H + L & 85.38 & 88.08 & 92.92 & 339 & 2,825,894 & 2,804,374 \\ \hline \hline \end{tabular} Note: L represents loss optimization, W represents Wavelet Transform Conv optimization, and H represents Histogram Transformer improvement.  
\end{table}
Table 2: Ablation experiment results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **mAP (\%)** \\ \hline Improved & & & & \\ YOLOv10 & 85.38 & 88.08 & 86.71 & 92.92 \\ YOLOv10 & 81.98 & 78.03 & 79.96 & 80.97 \\ YOLOv12 & 76.16 & 76.03 & 76.09 & 80.33 \\ CornerNet & 69.13 & 69.49 & 69.31 & 72.61 \\ SSD & 71.87 & 70.55 & 71.20 & 74.88 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model comparison experiment results.  
To further verify the adaptability of improved YOLOv10 in the disease detection task of Yunnan large-leaf tea tree based on UAV remote sensing, this study conducted external validation under scenarios including blurry images, complex backgrounds, strong illumination, and occlusion, with all data collected at Yunnan Agricultural University. Some of the comparative results are shown in Figure 10. The research results indicate that the improved YOLOv10 network maintains high effectiveness under various interference factors, accurately capturing and localizing disease targets.
## 4 Results and Discussions
### Implementation Challenges
#### 4.4.1 Scalability and Cost
Such challenges are highlighted by [74], where an IoT-based environmental monitoring system in manufacturing encountered packet loss under variable network loads and faced trade-offs between affordability and data stability. For instance, VR, DT, and metaverse applications require substantial processing power and storage, demanding continuous updates and expert involvement, which can be time-consuming and challenging for SMEs [6; 54; 62; 68]. Furthermore, IM necessitates ultra-high network reliability, extremely low latency, and high throughput, which existing 5G networks may not fully support, thereby impacting critical applications like environmental monitoring, precise control, and immersive experiences [4]. Hardware design inefficiencies, such as inadequate headset brightness, can also hinder the immersion experience, leading to eye strain, reduced hazard recognition, and training ineffectiveness [43]. Likewise, the lack of haptic feedback hindering intuitive interaction with virtual objects [12] also impedes user adoption and overall effectiveness. Ensuring compatibility across diverse technology standards and integrated software tools is another significant hurdle for metaverse systems [4; 64]. Moreover, human factors play a crucial role, as traditional skills may not directly translate to using innovative technologies, and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Barrier** & & **Description** & **References** \\ \hline High Costs and Investment Uncertainty & - & Significant financial outlays for hardware, software, setup, and specialized personnel. & \\ - Initial investment & - & Integrating new technologies into existing legacy infrastructure & [6; 12; 33; 37; 43; 45; 57; 64; 68; 85] \\ - & Compatibility and integration costs & - & Highly important investment often leads to uncertainty regarding & [6; 12; 33; 37; 43; 45; 57; 64; 68; 85] \\ - & Cost justification and ROI uncertainty & - & High upfront investment often leads to uncertainty regarding & [6; 12; 33; 37; 43; 45; 56; 57; 64; 68; 85] \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Key challenges related to scalability and cost. there is a concern that workers' natural instinctive responses to abnormalities might be delayed or ignored in virtual environments [4; 7]. To conclude, lowering the implementation costs of metaverse technology is crucial for its adoption, especially by SMEs. Although immersive environments involve high upfront costs, these are justified by benefits such as reduced safety risks and increased efficiency [12].