esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
The amount of recombinant ricin recovered from the full submersion control coupons used for calculating recovery efficiencies was 10 ng, 7.1% CV, for each liquid deposition replicate and 10 ug, 6.3% CV, for each aerosol deposition replicate. Recovery efficiencies for all four swab types on all four surface materials were greater when recovering liquid deposited agent than dry aerosol agent from each surface material. In every swab type and surface material comparison between deposition methods, we found there to be a statistically significant difference with a confidence level exceeding 99.99% (p-value equals 0.0001). On glass coupons, Dacron and cotton swabs recovered 70% and 57%, respectively of the liquid deposited material while rayon and macrofoam recovered 55% and 61% respectively (Table 1). These numbers are significantly more than observed with the dry aerosol deposition where Dacron swabs recovered 13% of the aerosolized agent from glass. Cotton and macrofoam recovered 7% and 10% respectively, and rayon failed to recover an amount of material exceeding the background electrochemicaluminescent (ECL) signal. On polycarbonate, a similar surface material, Dacron, Cotton, and macrofoam swabs recovered 73%, 59%, and 65% respectively of the liquid deposited ricin while the rayon swab recovered 64% of the liquid deposited material. However, for the dry deposited ricin, Dacron swab recovered only 38% of the agent aerosolized onto polycarbonate, and the cotton, macrofoam, and rayon swabs recovered 13%, 12%, and 8% respectively (Table 1). Of the two materials with irregular surfaces, CARC painted steel and vinyl floor tile, recovery efficiencies of liquid deposited protein were higher when sampling from the CARC painted steel. Dacron recovered 76% of the agent, Rayon recovered 60%, and Cotton and Macrofoam recovered 50% and 65% respectively of the ricin seeded by liquid deposition. However, the recovery efficiencies of each swab material were much lower with the dry deposited agent. Only 25% of the deposited ricin was recovered with a Dacron swab and cotton, rayon, and macrofoam each failed to recover a measurable amount of protein from the CARC painted steel coupons. On vinyl floor tile, Dacron swabs recovered 70% of the liquid deposited material, macrofoam and cotton recovered 58% and 47% respectively, and rayon swabs recovered 64% of the liquid deposited protein. The recovery efficiencies were significantly lower when sampling dry deposited agent from vinyl floor tile. Dacron swabs recovered 18%, cotton, rayon, and macrofoam failed to recover a measurable amount (Table 1).
## 3 Results and Discussion
### Optimization of HPLC Chromatographic Separation Conditions  
For efficient chromatographic separation of compounds **1-18**, various parameters, such as make and type of column [SunFireTM (Waters, Milford, MA, USA), Gemini (Phenomenex, Torrance, CA, USA), Capcellpak UG120 (Shiseido, Tokyo, Japan), QuasarTM SPP (PerkinElmer, Shelton, CT, USA), and INNO (Youngjin Biochrom Co., Seongnam Korea)], acids (formic acid, trifluoroacetic acid, acetic acid, and phosphoric acid), and column temperatures (30, 35, 40, and 45 \({}^{\circ}\)C), were compared. Optimal analysis was achieved with a SunFire C\({}_{18}\) column (4.6 \(\times\) 250 mm, 5 \(\upmu\)m), a column temperature of 40 \({}^{\circ}\)C, and two-component mobile phase system of distilled water-acetonitrile, both containing 0.1% (\(v/v\)) formic acid. Under the optimized HPLC conditions, compounds **1-18** were baseline separated with resolution values \(\geq\)3.85, with HPLC run-times within 45 min (Figure 1 and Table S3).
# Skin Cancer Classification Using Fine-Tuned Transfer Learning of DENSENET-121
## 5 Conclusions  
This study investigated the efficacy of classification machine learning models and pretrained transfer learning models for skin cancer classification. Among the evaluated models, DenseNet-121 emerged as the most promising choice. Its unique architecture facilitates feature reuse and enables efficient information flow, with each layer receiving feature maps from preceding layers and passing its own feature maps to subsequent ones. By fine-tuning the DenseNet-121 model and incorporating additional layers, a noteworthy accuracy rate of 87% was achieved. This result highlights the suitability of DenseNet-121 for skin cancer classification, emphasizing the value of leveraging its intricate layer connections to enhance performance. In essence, this study underscores the significance of utilizing advanced convolutional neural network designs such as DenseNet-121 for dermatological tasks like skin cancer classification. The study holds significant managerial implications for healthcare organizations and dermatological practices. Implementing DenseNet-121 as part of a computer-aided diagnosis system could enhance the efficiency and accuracy of skin cancer diagnosis processes. By leveraging DenseNet-121's unique architecture, which promotes feature reuse and efficient information flow, healthcare professionals can potentially streamline the diagnosis workflow, reduce diagnostic errors, and improve patient outcomes. Healthcare organizations may consider allocating resources toward acquiring and implementing these cutting-edge technologies to enhance their diagnostic capabilities and stay at the forefront of medical innovation. Additionally, ongoing research efforts should focus on exploring alternative models and preprocessing techniques to further improve accuracy and address specific challenges in skin cancer diagnosis. However, it is important to acknowledge that the study faced a limitation of data. The publicly available dataset used in this research did not cover all skin colors, which may limit the generalizability of the findings. Therefore, acquiring more diverse datasets that encompass a broader range of skin colors and ethnicities would be essential to train the model more comprehensively and achieve greater accuracy in real-world applications. Conceptualization, A.B., S.-C.N. and M.-F.L. ; methodology, A.B., S.-C.N. and M.-F.L. ; software, A.B. ; validation, A.B., S.-C.N. and M.-F.L. ; formal analysis, A.B. ; investigation, A.B. ; resources, S.-C.N. and M.-F.L. ; data curation, A.B. ; writing--original draft preparation, A.B. ; writing--review and editing, A.B., S.-C.N. and M.-F.L. ; visualization, A.B. ; supervision, S.-C.N. and M.-F.L. ; project administration, S.-C.N. ; funding acquisition, S.-C.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data used to support the findings of the study are available from the first author upon request. The author's email address is [PERSON]]. The authors declare no conflicts of interest.
## 2 The Vision of IoPVT-Enabled Outdoor Health Monitoring
### Technological Innovations Enabling the Vision
* _Metaverse Integration and Immersive Visualization:_ The fusion of physical and virtual worlds through the Metaverse fundamentally changes how data are understood and acted upon [54; 55]. Rather than combing through static graphs or disparate data streams, stakeholders build interactive, three-dimensional environments that mirror real-time conditions. Weather patterns, human movement, infrastructure stressors, and environmental quality come together in a shared virtual space. Decision makers can \"walk\" through digital twins of neighborhoods, identify high-risk zones, and simulate interventions, such as rerouting pedestrian traffic, adjusting energy usage, or optimizing evacuation plans, gaining insights that would be hard to discern from traditional analytics alone [56]. * _Automation, Control, and Feedback Mechanisms:_ Some time-sensitive tasks will be carried out by automated mechanisms as the system transitions from immature to mature. Intelligent signage could change to route walkers around threatened areas, autonomous drones could inspect infrastructure before it fails, and remotely controlled environmental controls could help mitigate hazards before they start. Significantly, an increasing degree of automation does not marginalize human judgment. On the contrary, it augments and enhances human decision-making capabilities [57; 58]. Using continuous feedback loops supported by bidirectional communication channels provides for ongoing responsiveness to community input, expert advice, and governance principles and ensures that accountability and transparency are upheld [59]. In essence, the technology that makes this vision possible is a stacked synergetic ecosystem. The sensor and network layer, the compute and AI-driven analytics layer, and the immersive interface layer provide distinct capabilities. Together, these tools offer an opportunity to redirect how we move beyond reactive, shattered approaches to proactive, integrated strategies that protect human health, build ecological resiliency, and reimagine the very nature of how we live, work, and move in the outdoors.
# Intelligent Video Surveillance Platform for Wireless Multimedia Sensor Networks
## 3 Description of WMSNs System  
### Sensor Node Architecture  
A multimedia sensor node consists of many components including a sensing unit with a camera, a wireless communication unit, and a processing unit; the whole sensor should be powered by a power unit as shown in Figure 1. The processing unit is particularly essential to the sensor node. The processing unit should have low power consumption, high speed calculation, and also should be small. For these reasons, we use raspberry pi (RPi) (Raspberry Pi Foundation, Cambridge, United Kingdom) as a processing unit as shown in Figure 2a.The Raspberry pi 2 model B includes 1 GB of RAM and powerful 900 MHz quad-core ARM Cortex-A7 (ARM Holding, Cambridge, United Kingdom) CPU. It is based on broadcom BCM2835 system on a chip. It has HDMI socket, a SD slot card memory, a couple of USB connectors, and anethernet connector. With these connections, raspberry pi is a decent educational desktop PC. We use raspbian operating system, which is based on a debian distribution. Raspbian operating system (raspbian-wheezy, Raspberry Pi Foundation, Cambridge, United Kingdom, 2015) is a linux/GNU version (3.18) developed for raspberry pi hardware. Our platform consists of 7 raspberry pi model 2B sensor nodes.  
The wireless communication unit has the responsibility of transmitting video data in WMSNs. For wireless communication, we use commercially available EDIMAX wi-fi dongle as shown in Figure 2b. EDIMAX wi-fi dongle (EW-7811 Un, Edimax, Taipei, Taiwan) is attached to USB port of each raspberry pi board. The dongle has data rates up to 150 Mbps, complies with wireless 802.11 b/g/n, and it supports smart transmit power control and auto-idle adjustment. With low power consumptions, these dongles are particularly suitable for fast prototyping for wireless multimedia sensor network applications.  
Figure 1: Sensor node architecture.
## 5 Experiments and Discussions
### Comparisons of Dimensionality Reduction  
Table 2 lists the results of different combined features of DNST. It can be seen that the accuracy of the training set and the test set of DNST-GLCM is 96.77% and 90.73% respectively, the accuracy of the training set and the test set of DNST is 93.55% and 89.92% respectively, and both results of DNST-GLCM are higher than those of DNST, which indicates DNST feature combined with GLCM texture features can improve the recognition accuracy. In addition, the accuracy of DNST-KSR is \(94.35\%-89.92\%=4.43\%\) higher than that of DNST, and the accuracy of DNST-GLCM-KSR is \(96.37\%-90.73\%=5.64\%\) higher than that of DNST-GLCM. The above shows that KSR can effectively remove redundancy and interference features and improve recognition accuracy. At the same time, we noticed that the classification time was shortened from tens of seconds to several seconds by KSR. This is because KSR reduces feature dimensionality to \(c-1\), where \(c\) is the number of classes. The continuous casting slabs samples include positive samples and negative samples--that is to say, the number of classes is 2. The feature number was reduced to 1 dimensionality. Finally, it should be noted that the number of subbands by DNST decomposition is different when different feature combinations achieve the highest recognition accuracy.  
Figure 5 shows the recognition accuracy of the training set and test set when using KSR and not using KSR. With the increase of SVM kernel parameter \(\gamma\), the accuracy of the DNST-GLCM training set gradually increases and is finally close to 100%, while the accuracy of the test set first increases and then decreases, which indicates that DNST-GLCM feature data is sensitive to SVM kernel parameter; in other words, the feature is complex and not conducive to classifier learning. For DNST-GLCM-KSR, the accuracy of test set and training set are both high, and the curve fluctuation are small. The above results show that the DNST-GLCM-KSR scheme makes the extracted features more discriminative, easier to learn and classify, and it has a strong robustness to the kernel parameter of SVM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Feature Type**} & \multirow{2}{*}{**The Number of Features**} & \multicolumn{2}{c}{**Time Cost (ms)**} & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \cline{3-5}  & & **Classification** & **Training Set** & **Test Set** \\ \hline
**DNST** & 66 & 33.10 & **93.55** & 89.92 \\ DNST-GLCM & 36 & 25.04 & **96.77** & 90.73 \\ DNST-KSR & 1 & 3.39 & 96.37 & 94.35 \\
**DNST-GLCM-KSR** & **1** & **3.13** & **97.58** & **96.37** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result of feature combination.  
Figure 5: The recognition accuracy of training set and test set.
## 3 Concepts, Models and Technologies
### Voice over IoT--VIoT and Video over IoT  
Real audio and voice traffic over IP operates through applications packaged by the protocol utilizing voice over IP (VoIP) technology, running on network or Internet infrastructures. Over the years, this has expanded with IPv6, distinguishing itself through client-to-client addressing and connectivity.  
Regarding IoT devices, as previously discussed, there are _middle-end_ or _high-end_ boards or embedded devices and applications capable of running protocols such as Real-Time Protocol (RTP), SIP, and others. Applications with robust performance are referred to as voice over IoT (VIoT), featuring VIoT gateways that internally implement an SIP client to scale it. As detailed in [26], it will be applied with main links to connect with gateways or routers on the edge of SBs.  
However, the integration, availability, response time and cost-effectiveness of a VIoT implementation are not necessarily straightforward. For example, in the case of the Smart Building use case, there is significant research potential in the integration of VoIP (using SIP) with the IoT edge or, more advanced, audio and video streaming, as well as the emerging video over IoT, which involves multimedia protocols, video compression and related technologies. To simplify implementation and account for critical, simultaneous data traffic on the same network, a Quality of Service (QoS) mechanism must be deployed. This mechanism ensures an optimal Quality of Experience (QoE) for the services provided to users by the service provider.
## 2 The Mechanisms of Additives
### Coordinate Covalent Bond
Figure 14a compares SEM images of perovskite films prepared without and with additives, with 2% Pb(SCN)\({}_{2}\) resulting in significant growth of apparent grains compared to the original film. However, use of 1% of PEAI reduces the apparent grain size due to the larger size of the PEA, which limits or alters the growth of the 3D perovskite. Figure 14b compares the XRD patterns of the same sample as shown in Figure 14a. Use of PEAI and Pb(SCN)\({}_{2}\) increased the main XRD perovskite peak by nearly 14 times compared to the original sample. One potential problem with the Pb(SCN)\({}_{2}\) additives is the formation of excess PbI\({}_{2}\). A slight excess of PbI\({}_{2}\) is generally advantageous over a short period of time. However, excessive PbI\({}_{2}\) is generally not conducive to PSC performance. It can also be observed from Figure 14b that the formation of such PbI\({}_{2}\) is markedly suppressed when PEAI and Pb(SCN)\({}_{2}\) are simultaneously added. Strong molecular interaction between PEAI and PbX\({}_{2}\) (X = I or SCN) is a good explanation for this. At the same time, as shown in Figure 14d, defects associated with insufficient lead coordination or halide vacancies may be passivized by SCN located at or near the GB. Figure 14c shows the J-V curve for the various devices mentioned above. The synergistic effect of using both PEAI and Pb(SCN)\({}_{2}\) is evident in dramatically improved device characteristics, with PCE increasing from 16.3% to 18.7%, an increase of more than 2%. Figure 13: **(a)** Molecular structure of phenomenally acid (PSA), 2-aminophenol-4-sulfonic acid (APSA) and hydroquinone sulfonate potassium salt (KHQSA). **(b)** Scanning transmission electron microscope-high angle annular dark field (STEM-HAADF) image and Scanning transmission electron microscope-electron energy loss spectroscopy (STEM-EELS) image of O, Sn and I, corresponding to 10% KHQSA modified Formamidinium tin iodide (FASnI\({}_{3}\)) film (without SnCl\({}_{2}\)). The X-ray photoelectron spectroscopy (XPS) S 2p spectrum was measured from the surface and the entire surface of a 1.5% KHQSA modified FASnI3 film. **(c)** XRD patterns of pristine, 3% PSA, 3% APSA and 1.5% KHQSA modified FASnI3 membranes. **(d)** PCE dose dependence of FASnI3-based PSCs with different amounts of PSA, APSA and KHQSA additives. **(e)** J-V curves of the pristine and KHQSA modified FASnI3 solar cells. Ref. [47] Reproduced with permission from ref 47. Copyright 2019 Wiley-VCH.
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 2 Related Work
### Related Data Acquisition Methods  
#### 2.2.1 Real UAV Data Acquisition Method  
In 2020, [PERSON] et al. [13] proposed that consumer drones are usually used in civilian environments, and traditional physics-based methods will become ineffective in some cases. They propose to conduct drone intrusion detection through encrypted Wi-Fi data traffic. In 2021, [PERSON] et al. [14] based on the Carbon Z T-28 fixed-wing UAV designed four kinds of faults: disabling the engine, disabling the elevator, disabling the rudder, and disabling the aileron, realizing the collection of UAV flight logs in the fault state. In 2022, [PERSON] et al. [15] conducted a vulnerability analysis of drones used in the education sector. The drone platform used was Ryze Tello TLW004. This data set contains Wi-Fi Deauthentication Attack, WPA2-PSK Wi-Fi cracking attack, and a Tello API vulnerability attack.
# Research on Real-Time Control Strategy for HVAC Systems in University Libraries
## 3 Experimental Cases
### Device Model and Objective Function
#### 3.2.2 Cooling/Chilled Water Pump Model  
The pump power and flow rate are fitted through the following equations:  
\[P_{\rm P}=b_{0}G_{w}^{2}+b_{1}G_{w}+b_{2} \tag{8}\]  
where \(P_{\rm P}\) represents the pump power, \(b_{0}\), \(b_{1}\), and \(b_{2}\) represent the pump site data obtained by least-squares fitting, and \(G_{w}\) represents the water flow rate (m\({}^{3}\)/h). The chilled water pumps in parallel, and the average temperature, \(t_{\rm m}\) (\({}^{\circ}\)C), can be calculated according to the following formula:  
\[t_{m}=\frac{\sum c_{w}^{i}t_{w}^{i}}{\sum c_{w}^{i}} \tag{9}\]  
In the formula, \(c_{w}\) represents the specific heat capacity of water (J/(\({}^{\circ}\)C\(\cdot\)kg)) and \(t_{w}\) represents the water temperature (\({}^{\circ}\)C).
# Remote Sensing of Snow Cover Using Spaceborne SAR: A Review
## 5 Discussion
### Influence of Filtering Algorithms  
The speckle noise in SAR images affects all SAR-based snow cover detection algorithms, especially the backscattering-based approaches. The available options for filtering algorithms to overcome this problem include Frost filter (126, 137, 142, 186, 187, 195), refined Lee filter (163, 163, 205, 206),  
Figure 11: Total and wet SCE of Monte Rosa (MR) region (**a**) month1: 2018 March 12 (**b**) month2: 2018 May 11. Figures are revised from [PERSON] et al. (2014). Note the total SCE is modeled based on backscatter, InSAR coherence, and PolSAR parameters; wet SCE is estimated based on conventional backscatter-threshold approach.  
median filter [102; 103; 131], low pass filter [135; 207], multichannel intensity filter [140], binary partition tree [167], [PERSON] filter [208], multi-scale multilooking [209], and Kuan filter [136]. Some studies attempted to compare the ability of different filters. [PERSON] et al. [134] used the effective number of looks (ENL) and standard deviations to compare the performance of median, Gamma DEMAP and Frost filter. Results showed that the median filter performs best in a purely statistical aspect; Frost filter, however, should be selected since the median filter is not an adaptive filter, which preserves image details while smoothing speckles by using unequal weights [210]. They also implied that the window size influences the performance of filters. [PERSON] et al. [102] preferred the median filter, as other filters might cause information loss at the pixel level. However, these studies only compared few filters and so far, no extensive comparison has been made to discuss the advantages and disadvantages of all available filters in a comprehensive overview.
# Demand Flexibility: A Review of Quantification Methods, Models, and Required Data
## 3 Results
### Methods Deployed Across Studies
#### 3.3.4 Simulation  
Studies that included simulations utilized software, such as EnergyPlus [24; 33; 34; 38; 51; 58], Comstock [68], Modelica [17; 31] and Dymola [37], among others. Simulations were often paired with other methods to compare demand under normal conditions with demand and system behaviors during a DR event.