esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Training and Evaluation Metrics
#### 2.3.2 Evaluation Metrics  
We divided the maize leaf dataset into a training set, validation set, and test set in a 7:2:1 ratio to facilitate model training, optimization, and evaluation for subsequent research. The evaluation metrics employed in the maize leaf disease detection model in this study included precision, recall, F1 score, mean average precision (mAP), detection speed, model parameters, computational load (GFLOPs), and memory cost. Higher precision indicates  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Epochs & 300 & Optimizer & SGD \\ Patience & 50 & Weight\_decay & 0.0005 \\ Batch & 8 & momentum & 0.937 \\ Imgsize & 640 & Warmup\_momentum & 0.8 \\ Workers & 8 & Lrf & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training Parameter Settings.  
Figure 7: Illustration of the DynamicATSS approach [37].  
more reliable positive predictions by the model. Recall measures the model's ability to detect all actual positives, with higher values indicating fewer false negatives. The F1 score balances precision and recall, providing a comprehensive measure that is especially useful for imbalanced datasets. Finally, mAP evaluates the model's ranking of predictions across multiple classes, with higher values indicating better overall performance. _Precision_, _recall_, \(F1\) score, and mAP are formulated as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{7}\]  
\[Recall=\frac{TP}{TP+FN} \tag{8}\]  
\[F1=2\times\frac{Precision\cdot Recall}{Precision\cdot Recall} \tag{9}\]  
\[\text{AP}=\int_{0}^{1}Precision\cdot Recalldr \tag{10}\]  
The model's real-time performance was assessed based on detection speed, model parameters, computational load (GFLOPs), and memory cost. Detection speed refers to the time required for the model to process an image, measured in seconds (s) or milliseconds (ms). Faster detection speeds indicate better real-time performance in practical application. Model parameters are the total number of trainable parameters, measured in millions (M); fewer parameters generally indicate a more lightweight model that is easier to deploy on resource-constrained devices. GFLOPs refer to the number of floating-point operations required during the model's forward propagation, measured in billions of floating-point operations (gigaFLOPs). Lower computational load indicates lower hardware resource requirements and faster runtime speeds. Memory cost is the memory required for the model to run, measured in megabytes (MB). Lower memory cost indicates that the model is more suitable for running on memory-constrained devices.
## 4 Results and Discussion
### Time Consumption Comparison of Power Trajectory Estimation  
To further test the performance of our proposed method, the computation time of proposed method was compared to the existing literature. In literature [32], the SDTW (Subsequence Dynamic Time Warping) method was applied to estimate the power trajectory of a single appliance. We operated our proposed method and the SDTW-based method in literature [32] for the power consumption estimation of fridge, and the computation times are shown in Table 8.  
From Table 8, we found that the OSVR-based method in our paper has an advantage on time consumption compared with the SDTW. As SDTW needs to scan the whole feature sequence and iteratively using DTW to search for best matched subsequence, the computation time is relative to the length of the signature time series. The OSVR-based method used the time feature for training, so only the subsequences with the same length duration were chosen for training, which reduced the computation time of the estimation algorithm.
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 2 Background
### Identification of Facial Landmarks
#### 2.2.1 Blink Detection  
To determine the feature positions, we apply facial feature detection including the eyes, eyebrows, nose, ears, and mouth [17; 18]. We extract specific facial structures by knowing the index of specific parts of a face. For blink detection, we are interested in eyes features. Each eye is represented by six coordinates, as illustrated in Figure 3, starting at the left corner of the eye and going clockwise around the rest of the area.  
Figure 2: Illustration of 68 landmarks on a human face.  
\[EAR=\frac{(|p_{2}-p_{6}|+|p_{3}-p_{5}|)}{(2|p_{1}-p_{4}|)}. \tag{1}\]  
Based on [PERSON] and [PERSON] [18], we can derive Equation (1), reflecting the eye-opening value, where \(p_{1},\ldots,p_{6}\) are features located on a 2D face. The numerator of Equation (1) calculates the distance between the eye features vertically, while the denominator calculates the distance between the eye features horizontally. Denominator balancing is suitable since there is only one set of horizontal points but there are two sets of vertical points. The eye-opening value is almost unchanged when the eye is opened, but it quickly decreases to 0 when blinks occur. Using this method, we can avoid image processing techniques and simply rely on eye-opening values to determine blinking actions.
# Sensors for Sustainable Smart Cities: A Review
## 3 Results
### Smart Cities in the World  
In this section, some smart cities deployments across the world are presented. In Europe: Tampere, Helsinki, Amsterdam, Vienna, Copenhagen, Stockholm, Milton Keynes, London, Malaga, Barcelona, Santander, Paris, and Geneva [4; 25; 26; 27]. In Asia: Singapore, Hong Kong, Shanghai, Beijing, Songdo, Seoul, and also smart cities in Taiwan, Indonesia, Thailand, and India [4; 25; 27; 28; 29]. In North America: Toronto, Vancouver, New York, Washington, and Seattle [4; 25; 27]. In South America: Medellin and Rio de Janeiro [27; 30; 31]. In Oceania: Melbourne, Perth, Sydney, Brisbane, and Adelaide [27; 32]. The most common smart city project implementations across these cities include: development of collaborative business districts in Barcelona [21; 25] and Hong Kong [4; 27]; citizen security by traffic monitoring in Rio de Janeiro [4; 31], and natural disaster monitoring in Singapore and Indonesia [29; 33]; public service, smart government, and communication transformation in cities of China (Wuhan, Shanghai, Beijing, Dalian, Tianjin, Hangzhou, Wuxi, Shenzhen, Chengdu, and Guangzhou) [28]; adaptation of cultural spaces in Medellin [30]; citizen engagement and data enhancement in New York and Washington [27]; deployment of experimental testbeds and living labs in Santander [26] and London [27]; integration of local and foreign universities in Tampere [27] and Songdo [25]; deployment of smart green projects and policies in Seoul [27] and Toronto [4]; fiber optic and smart grids in Geneva [27]; energy efficiency and innovation enhancement in Vienna [27]; improved water consumption in Copenhagen, Hong Kong, and Barcelona [4]; deployment of electric charging stations in Malaga and Paris and Amsterdam [4]; Big data integration and analysis in India and Thailand [29]; wired communities, pedestrian mobility, and mass transit solutions in Sydney, Brisbane, Adelaide, Melbourne, and Perth [32]; carbon emission reduction in Seattle [25]; smart waste collection systems in Helsinki [25], Songdo [25], Barcelona [25]; smart parking in Milton Keynes [25]. Table 1 shows the main Smart City implementations reported in the literature for health, security, mobility, water, waste management, and energy efficiency, for the countries reviewed in this section. A more extensive search on smart city deployments around the world was performed using the results from the literature review, as well as information gathered from related websites [34; 35; 36]. The results of this search are presented in Figure 3.
## 7 Security Analysis
### Formal Security Analysis Using BAN Logic
#### 7.3.1 BAN Logic Rule  
The rules of BAN logic are as follows.  
* Message meaning rule: \[\frac{Q\bigm{|}\equiv Q\stackrel{{ K}}{{\leftrightarrow}}W,\ \ Q \lhd\{M\}_{K}}{Q\ |\equiv W\ |\sim M}\]
* Nonce verification rule: \[\frac{Q\ |\equiv\#(M),\ \ Q\ |\equiv W\ |\sim M}{Q\ |\equiv W\ |\equiv M}\]
* Jurisdiction rule: \[\frac{Q\ |\equiv W\ |\implies M,\ \ Q\ |\equiv W\ |\equiv M}{Q\ |\equiv M}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Security Feature** & **[PERSON] [28]** & **[PERSON] [20]** & **[PERSON] et al. [21]** & **[PERSON] et al. [6]** & **Ours** \\ \hline \hline Masquerade attack & \(\circ\) & \(\circ\) & \(\circ\) & \(\times\) & \(\circ\) \\ Smart grid device stolen attack & \(\circ\) & \(\circ\) & \(\circ\) & \(\times\) & \(\circ\) \\ Replay attack & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) \\ Session key disclosure attack & \(\times\) & \(\times\) & \(\circ\) & \(\times\) & \(\circ\) \\ Man-in-the-middle attack & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) \\ Mutual authentication & \(\circ\) & \(\circ\) & \(\circ\) & \(\times\) & \(\circ\) \\ Anonymity & \(\times\) & \(\circ\) & \(\circ\) & \(\circ\) & \(\circ\) \\ Dynamic node addition phase & \(\times\) & \(\times\) & \(\times\) & \(\circ\) & \(\circ\) \\ \hline \hline \end{tabular}  
\(\circ\): security feature is satisfied; \(\times\): security feature is not satisfied.  
\end{table}
Table 2: A comparative summary: security features.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(Q|\equiv M\) & \(Q\)**believes** statement \(M\) \\ \#\(M\) & Statement \(M\) is **fresh** \\ \(Q\lhd M\) & \(Q\)**sees** statement \(M\) \\ \(Q|\sim M\) & \(Q\) once **said**\(M\) \\ \(Q\Rightarrow M\) & \(Q\)**controls** statement \(M\) \\ \(<M>_{N}\) & Formula \(M\) is **combined** with formula \(N\) \\ \(\{M\}_{K}\) & Formula \(M\) is **encrypted** by key \(K\) \\ \(SK\) & **Session key** used in the current authentication session \\ \(Q\stackrel{{ K}}{{\leftrightarrow}}W\) & \(Q\) and \(W\) communicate utilizing \(K\) as the **shared key** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Notations used for BAN logic.  
* Freshness rule: \[\begin{array}{c}Q\;\big{|}\equiv\#(M)\\ \hline Q\;\big{|}\equiv\#\left(M,N\right)\end{array}\]
* Belief rule: \[\begin{array}{c}Q\;\big{|}\equiv(M,N)\\ \hline Q\;\big{|}\equiv M\end{array}\]
## 2 Materials and Methods
### Energy-Harvesting Device  
Commercial lead zirconate titanate (PZT)-based piezoelectric cantilevers were used to validate the concept. A low-frequency (125 Hz w/no mass) cantilever (Mide, Woburn, MA, USA, V25W) with dimensions of 57.2 \(\times\) 38.1 \(\times\) 0.6 mm (\(L\), \(w\), and \(t\)), and a higher frequency (440 Hz w/no mass) cantilever (Mide, S233-H5 FR-1107 XB) with dimensions of 53 \(\times\) 20.8 \(\times\) 0.83 mm were used. Most of the results illustrated in this paper used the low-frequency (V25W) cantilever unless otherwise specified. The high-frequency cantilever was used to illustrate methods of increasing frequency range. The proof mass was 3D printed using polylactic acid (PLA) and an Ultimaker S5 printer. The proof mass had dimensions of 30 \(\times\) 38.1 \(\times\) 20 mm, with a 1 mm thick capping layer. The initial proof mass had a 9 \(\times\) 7 cavity array of 3 \(\times\) 3 \(\times\) 20 mm. Additional proof masses of 5 \(\times\) 7 arrays were made to determine the effects of increasing cavity size on tuning ability. The overall mass of the (0% filled) 9 \(\times\) 7 proof mass was 12.76 g, and the 5 \(\times\) 7 proof mass was 13.22 g. The proof masses were then bonded to the cantilever. A rectangular-shaped proof mass was chosen due to the rectangular shape of the cantilever, where the width of the proof mass was equal to the width of the cantilever. The size of the cavities was selected based on the array size and overall width of the proof mass.
# _Forentation_
## 4 Discussion
### Physicochemical and Aromatic Composition of Ciders
#### 4.4.4 Selection of the Inoculation Strategy  
Few studies have explored mixed fermentations of _S. cerevisiae_ and _P. kudriavzevii_ in wines. [PERSON] et al. [21] demonstrated that a 1:1 inoculation ratio of _S. cerevisiae_ and _P. kudriavzevii_ in Cabernet Sauvignon increased the number of aromatic compounds and enhanced fruity, floral, herbaceous, and caramel-like notes compared to single-strain fermentations. Similarly, [PERSON] et al. [19] found that mixed fermentations in Cabernet Sauvignon improved phenolic and aromatic compounds, particularly isoamyl alcohol and ethyl esters, which contribute rose-like and fruity aromas. However, the authors reported even better results when _P. kudriavzevii_ was used at ten times the inoculum level of _S. cerevisiae_.  
A recent study demonstrated that different species of non-Saccharomyces yeast do have an important impact on the characteristics of cider and that co-inoculation of non-_Saccharomyces_ yeast with _S. cerevisiae_ for cider fermentation may be a strategy to improve the flavor of cider [16]. However, to our knowledge, no studies have reported mixed fermentations exclusively using _S. cerevisiae_ and _P. kudriavzevii_ in cider. Two recent studies [14,15] employed these yeasts as part of a triple inoculation strategy along with _Lactobacillus plantarum_. Notably, in both studies, the inoculum ratio of _S. cerevisiae_ to _P. kudriavzevii_ was 1:1, the same ratio selected in this work.  
In this study, as revealed by the PCA, ciders obtained through the Sim 1:1 and Seq S-P strategies shared similar characteristics within the mixed fermentations. These ciders were associated with a high contribution of compounds such as glycerol, lactic and succinic acids, isoamyl alcohol, 2-phenylethanol, isoamyl acetate, and 2-phenylethylly acetate. Additionally, these strategies showed a higher malic acid consumption compared to pure fermentations. These features make them particularly attractive to the cider industry. Due to the practicality of co-culturing, which enables the preparation of a single inoculum and its inoculation in a single step on the same day, we propose the Sim 1:1 strategy to be tested in natural must at a pilot scale.
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
###### Abstract  
Marine aquaculture has been expanding rapidly in recent years, driven by the growing demand for marine products. However, this expansion has led to increased competition for space and resources with other coastal zone activities, which has resulted in the need for larger facilities and the relocation of operations to offshore areas. Moreover, the complex environment and exposure to environmental conditions and external threats further complicate the sustainable development of the sector. To address these challenges, new and innovative technologies are needed, such as the incorporation of remote sensing and _in-situ_ data for comprehensive and continuous monitoring of aquaculture facilities. This study aims to create an integrated monitoring and decision support system utilizing both satellite and _in-situ_ data to monitor aquaculture facilities on various scales, providing information on water quality, fish growth, and warning signs to alert managers and producers of potential hazards. This study focuses on identifying and estimating parameters that affect aquaculture processes, establishing indicators that can act as warning signs, and evaluating the system's performance in real-life scenarios. The resulting monitoring tool, called \"Aquasafe\", was evaluated for its effectiveness and performance by test users through real-life scenarios. The results of the implemented models showed high accuracy, with an R\({}^{2}\) value of 0.67. Additionally, users were generally satisfied with the usefulness of the tool, suggesting that it holds promise for efficient management and decision making in marine aquaculture.  
remote sensing; aquaculture; precision fish farming; machine learning +
Footnote †: journal: Applied Sciences  
1 Laboratory of Environmental Quality and Geospatial Applications, Department of Marine Sciences, University of the Aegean, 81100 Mytilene, Greece 1
# Minimum Audible Angle in 3 rd-Order Ambisonics in Horizontal Plane for Different Ambsonic Decoders
## 4 Selected Ambsonic Decoders
This framework accommodates four distinct reproduction scenarios: (1) a virtual source positioned within a speaker triplet, activating all three loudspeakers; (2) a virtual source positioned between two loudspeakers, activating both; (3) a virtual source coincident with a physical loudspeaker position, activating only that speaker; and (4) a virtual source positioned outside the convex hull, resulting in null reproduction. To address the limitations of Scenario 4, [PERSON] et al. [24] proposed the implementation of \"imaginary loudspeakers\" to preserve VBAP functionality in non-uniform configurations where the condition \(t\geq 2N+1\) cannot be satisfied. These virtual elements facilitate continuous spatial reproduction across otherwise discontinuous regions of the sound field. The AllRAD maintains numerical stability across diverse loudspeaker configurations while satisfying [PERSON]'s objective measures of energy distribution, energy vector (\(\mathbf{r}_{\mathrm{E}}\)), and vector magnitude. The algorithm demonstrates superior performance in non-\(t\)-design reproduction systems, providing efficient signal distribution for arbitrary multichannel configurations [27]. Quantitative analysis, illustrated in Figure 5, confirms significantly reduced spatial errors and enhanced uniformity in amplitude and energy distribution compared to alternative decoding methodologies. The angular errors of the velocity and energy vectors of the AllRAD are the smoothest among the three decoders. Fluctuations of Amplitude \(A\) and Energy \(E\) are also negligible. The velocity vector angular error's overall value spans from  
Figure 4: Distribution of the objective measures for the combination of the investigated playback system and the EPAD. Red dots mark the loudspeaker positions. Please note that the scales for \(\mathbf{r}_{\mathrm{V}}\) angular error and energy are not equal to better visualise the differences in distributions. less than 0.5\({}^{\circ}\) to 2\({}^{\circ}\) for the horizontal plane with the highest value around 90\({}^{\circ}\). Irregular fluctuations can be observed across all measures.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 2 Related Works
### Early History of Complete Blood Cell Counting  
The observation of blood cells emerged when [PERSON] inspected his blood in 1675 [8]. Since the earliest time, scientists have carried on with microscopic investigations. They observed and analyzed blood cells while continuing to improve microscope technology during the 18 th and 19 th centuries. The first blood cell counting based on a microscope was accomplished by [PERSON] in 1852, and the hemocytometer was launched in 1874, leading to more accurate results [8; 9]. Research on automated blood cell counting started in the early 20 th century. One RBC counting method published in 1928 used photometry to estimate the blood volume but failed in accurately assessing abnormal cells [10]. Other approaches integrated photoelectric detectors and microscopes also generated discouraging counting results over the next twenty years [9]. In the 1950s, the Coulter counter was introduced, which passed cells through an aperture to do the counting and estimate the sizes of RBC by using the conductivity of the blood as the reference [11]. The Coulter counter was modified and applied to RBC and WBC counting; the Technicon SMA 4A -7A could simultaneously examine multiple cells [12]. Automated platelet counts with Technicon Hemalog-8 were presented in 1970 and integrated into Coulter's S Plus series analyzers in 1980 [9]. The examination of blood samples mostly employed laboratory analysts. They operate automated analyzers to collect information, annotate blood smear images, and recheck them when ambiguous cases are encountered.
## 3 Results
### Anatomic Data Acquisition  
The data available from the existing literature revealed the following acquisition modalities: CT scans [26; 29; 31; 36; 39; 41; 43; 46; 54; 60; 67; 71], MRIs, CBCTs (for maxillary obturators) [22; 24], structured light scanners [19; 20; 21; 23; 58], laser scanners [37; 49; 51; 56; 57; 62; 63; 64; 68; 69; 70], light intensity scanners [30], facial scanners, intraoral scanners (IOSs) [3; 18; 25; 27; 32; 44; 53], desktop scanners [34; 66], 3D photogrammetry [28; 40; 52; 55; 59; 69], the monoscopic photogrammetry technique with a mobile phone [15] or two (or more) of the following combined registration modalities: CT and a facial scanner [61], CT and an intraoral scanner [42], CT and an MRI [33], an MRI and a laser scanner [50], CT and 3D photogrammetry [47], CBCT and IOS [17], 3D photogrammetry and a structured light scanner [38], 3D photogrammetry and an intraoral scanner [35], and a facial scanner and a laser scanner [16].
## 1 Introduction
Researches have focused on the influence of different factors on the final performance, such as: solvent for extraction and adsorption [21; 22; 23], extraction time [18], pH and temperature during dye adsorption onto the nanoporous film [24], dipping time [25], dye mixing or co-sensitization [26; 27; 28], together with \"tandem\" cells [28], film thickness [22; 29] etc. Some papers include also theoretical calculations or simulations [24; 30; 31], for example, adsorption isotherms of a dye onto TiO\({}_{2}\) film are fitted by models established through statistical physics treatment [24] or chlorophyll is used as a reference dye to simulate absorption spectra and theoretically calculate ground and excited state properties [30]. There have also been some interesting ideas related to the improvement of cell efficiency. Due to its nature or different chemical structure, chlorophyll dyes performed different in different liquid electrolytes [32] and this could be related to iodide mobilities [31]. Exposing the photo anode 10 min to microwave frequency enhances electrode characteristics and efficiency and absorption of dye [33]. Studying the recombination processes for different natural dyes, the most efficient dyes can be discriminated [34]. To obtain dyes, a strategy to induce anthocyanins synthesis in in-vitro cultures, has been presented: Stress induced by nutrient deficiencies leads to the production of secondary metabolites such as anthocyanins [35]. Also, to improve the efficiency and anchoring ability of natural dye, two algal buffer layers (sodium alginate and spirulina) are introducedpreviously to dye (anthocyanin) sensitization [36]. Firstly, the TiO\({}_{2}\) film is treated with a layer of sodium alginate to help in producing more hydroxyl groups on the surface, improving the dye concentration in the film. Secondly, an algal layer made of Spirulina is sensitized above the film (with alginate) for uniquely enhancing the anthocyanin efficiency in DSSC. In this paper, we summarize, according to our expertise, the main advisable steps to obtain natural pigments without complicated chemical procedures. We review previous works and give some examples of our results regarding dye extraction and characterization and also their use as sensitizers for NDSSCs performance, as an application use of betalain (bougainvillea and beetroot extracts) and anthocyanin (eggplant extracts) dyes. The following three phases include the steps of the whole procedure. (i) Dyes preparation and characterization: select natural raw vegetable pigment and select the simplest and best extraction solution and method as well as characterize the resulting dye solutions. (ii) Exam dye solutions behaviour: how they adsorb onto substrates, how stable they are, what the role of pH is, solutions mixtures, etc. (iii) NDSSC tests: Optimize a simple and economical cell fabrication method and measure cell performance varying the dye with all the other parameters fixed.