esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Auxiliary Testing Platforms
#### 2.3.1. Arbin Battery Test Bench  
In order to carry out the different tests to the battery individual cells and the battery pack the Arbin MT-ML-40V-20-2 Chanels [83; 84] battery testing system was used (Figure 12). This battery testing system provides:  
* Fully independent high-precision test channels with full potentiostatic, galvanostatic control.
* True bipolar linear circuitry providing cross-zero linearity and zero switching time between charge and discharge.
* Each channel provides 3 or 4 current ranges with 16-bit resolution.
* Powerful embedded controllers provide fast data logging (2000 points per second, per system) and control flexibility for the most advanced test requirements.
* Any number of channels can be operated in parallel for increased current-handling capacity.
* Communicate with internal BMS via CANBus or SMBus protocols.
* Systems are air-cooled so no additional facility infrastructure or regular maintenance is needed.
* Software provides easy data analysis and plotting based in Data Watcher and Microsoft Excel.
* A wide array of auxiliary inputs/outputs are available for additional data collection or control such as temperature monitoring, additional reference electrodes, and more. Table 9 summarizes the main characteristics and Figure 12 shows a detail of the battery test bench.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Number of Channels** & **4** \\ \hline Voltage range & 0 to 60 V \\ Control and voltage measurement accuracy & (\(\pm\) 0.05\% FSR (full scale range)) \(\pm\) 60 mV \\ Min voltage to max current & 0 V \\ Input impedance & \(\sim\)10 G\(\Omega\) \\ Max current (charge/discharge) & 100 A \\ Current and voltage resolution & 16 bits \\ Current ranges (\(\pm\)0.05\% FSR (full scale range)) & \\ (a) High & 100 A \(\pm\) 100 mA \\ (b) Medium & 10 A \(\pm\) 10 mA \\ (c) Low & 1 A \(\pm\) 1 mA \\ Current rise time & 3 ms \\ \hline \hline \end{tabular}
\end{table}
Table 9: Arbin® battery test bench characteristics.
## 2 Data Details
### Dunmy Variables for North and Houston Regions for Hours 3 AM, 11 AM, and 4 PM  
From Table 2, for Hour 11 AM in the North region, the median price is USD 21.74. However, the minimum is only USD 0.07, while the maximum price is USD 556.92. In contrast, for the same region, during Hour 3 AM, the median, minimum, and maximum prices are USD 16.93, USD 0.01, and USD 82.7, respectively; note the dramatic reduction in the maximum value.  
Consider Figure 1 Panels A, B, and C, which depict the time series of the price data for the North region at 11 AM, 4 PM, and 3 AM, respectively. The wild swings in prices are apparent in these three panels. Since energy demand tends to be higher at 11 AM and 4 PM than at 3 AM, we see much larger prices during the daytime. In contrast, prices tend to be generally lower at 3 AM. A careful examination of the data shows that some of the large values occur during the summer months, but not all of them.  
We conducted a percentile analysis of the price data for Hours 11 AM and 4 PM in the North zone. For 11 AM, we chose a cutoff value of USD 50 at the upper tail of the data distribution, since only roughly 2% of the data are above USD 50. Similarly, for 4 PM, we choose a cutoff value of USD 90 at the upper tail. The analysis for the Hour 3 AM data yields cutoff values for the upper and lower ends of the price distribution, since the North price data at Hour 3 AM are left-skewed from Table 2. For the lower tail, we chose a cutoff value of USD 1.00, and for the upper tail we choose a cutoff value of USD 40. Collectively, these account for roughly 2.5% of the data.  
Figure 1: Panel A (**Top**): Time series of North price, Hour 11 AM. Panel B (**Middle**): Time series of North price, Hour 4 PM. Panel C (**Bottom**): Time series of North price, Hour 3 AM.  
The Houston region's price data distribution behaves almost Identically to that of the North. Hence, the same dummy variable construction is used for the Houston price data distributions for the different hours. The above analysis yielded the following independent (dummy) variables for the North and Houston zones.  
**For Hours 11 AM and 4 PM in North and Houston:**  
If 11 am North Price >= $50, define _NPrice50_ = 1, else 0;  
If 11 am Houston Price >= $50, define _HPrice50_ = 1, else 0;  
If 4 pm North Price >= $90, define _NPrice90_ = 1, else 0;  
If 4 pm Houston Price >= $90, define _HPrice90_ = 1, else 0.  
**For Hour 3 AM in North and Houston:**  
If 3 am North Price <= $1, define _NPrice1_ = 1, else 0;  
If 3 am Houston Price <= $1, define _HPrice1_ = 1, else 0;  
If 3 am North Price >= $40, define _NPrice40_ = 1, else 0;  
If 3 am Houston Price >= $40, define _HPrice40_ = 1, else 0.
## 1 Introduction
Considering WCSs closed over IEEE 802.11b WLAN, this paper develops a cross-layer adaptive feedback scheduling (CLAFS) scheme [25] that dynamically adjusts the sampling periods with respect to variable channel conditions. The primary objective is to provide QoC guarantees for WCSs via flexible resource management in dynamic environments that feature noise interference and variability of the network transmission rate. Based on cross-layer design, this scheme takes advantage of sharing and exchanging of information across the physical layer and the application layer within the communication protocol stack of WLAN. The sampling periods of control loops are adapted online to control the deadline miss ratio (DMR). To cope with dynamic variations of the link capacity, the feedback scheduler uses a simple proportional control algorithm with adaptive parameters. Since interference and node movement in wireless systems are stochastic and unpredictable in most situations, it is usually hard to select an appropriate invocation interval for a time-triggered feedback scheduler. To address this problem, an event-driven invocation mechanism for the feedback scheduler is suggested. This mechanism contributes not only to reduction of overheads (on average), but also to quick responses to changes in communication resource availability, resulting in further improvement of practical performance of the feedback scheduler. This paper is organized as follows. Section 2 describes the architecture of the control system considered. The case used as an illustrative example throughout the paper is also given. In Section 3, the employed cross-layer design methodology is described, followed by an analysis of the temporal properties of the studied WCS in terms of DMR. Then the CLAFS scheme is presented along with relevant algorithms. Section 4 presents the event-driven invocation mechanism for the feedback scheduler. In Section 5 the effectiveness of the proposed approach is validated by simulations highlighting its advantages. Finally, Section 6 concludes with discussions on possible extensions over the proposed approach.
# A Learning-Based Methodology to Optimally Fit Short-Term Wind-Energy Bands
## 3 Optimization of Wind-Energy Bands
Regarding forecasts, however, the previous equality doesn't hold. In fact, \(p_{24}^{d}\) (a sample, forecasted 24 h ahead) is different from \(p_{0}^{d+1}\) (the actual value measured a day later). It is clear that, given any two bands containing the real process inside of them at the same instants, the narrower band is of better quality. Wind-power generation is a process hard to anticipate, and violations to computed bands is a fact we must coexist with. However, not every violation has the same severity in terms of its impact to the power grid. In the context of the short-term energy dispatch, how much cumulated energy falls down outside the band is a convenient metric to assess the confidence of the pair: forecast plus computed band. In this work, we define the following expression as a metric for the reliability (The expression on the right hand side corresponds precisely to the anti-reliability, which of course is the complement of the reliability; hence the notation for the left hand side) \(\mathcal{R}^{d}\), of a band around a given forecast \(p^{d}\):  
\[1-\mathcal{R}^{d}(w,lb,ub)=\frac{1}{T}\sum_{t=0}^{T-1}\max\Bigl{[}w(t)-ub \left(p^{d},t\right),0\Bigr{]}+\frac{1}{T}\sum_{t=0}^{T-1}\max\Bigl{[}lb\left( p^{d},t\right)-w(t),0\Bigr{]} \tag{1}\]  
where \(ub\) and \(lb\) respectively are the functions that determine upper and lower limits for the bands along the forecasted period, and \(w\) is the actual generation, unknown until the near future where reality is revealed. Functions \(lb\) and \(ub\) take a forecast (\(p^{d}\)) and an instant (\(t\)) as their inputs, while their outputs are the respective bounds to expect. As mentioned, the feasible region of the optimization model imposes limits to the severity of violations to the band. Besides, in order to improve the quality as further as possible, the model allows to discard up to a limit of elements in the training set, which are _atypical_, specially bad forecasts that whether included would either: deteriorate the accuracy of the result, or force us to use too broad bands. So, to complete an instance we must set values to those quantities. The parameter \(\theta\in[0,1]\) limits the amount of energy allowed to fall down outside the band along the optimization horizon. The parameter \(\lambda\in[0,1]\) sets a minimum fraction of _regular_ (i.e., not atypical) forecasts to be used in the effective training set or, in other terms, \((1-\lambda)\) is the maximum fraction of atypical days allowed to be discarded. It is worth mentioning that the limit for off-band energy only accounts over regular forecasts.
# Business Process Configuration according to Data Dependency Specification
## 2 Detailing an Example  
One clear example, where the fundamental aspect is the successful execution of a BP, arises in the form of the organization of a trip. Generally, the customer searches by hand on the Internet for the cheapest combination of flight and hotel, for specific dates and cities. In addition, if the results obtained remain unsatisfactory, then other dates are sought until a convenient combination is found. Moreover, if necessary and also cheaper, a car can be rented in order to drive to another city to take a flight from another airport, thereby expanding the range of cities of departure and arrival.  
To obviate the search of several combinations by hand, it is possible to combine the activities that represent the three providers (Hotel, Flight, and Car Rental Providers) into a single BP. The question becomes how to configure the activities in a business process since the input and output are related between them. Therefore, the model of the BP implies two difficulties:* **To find the input of the activities that optimize the trip by means of minimizing the price**: The process has to provide customers with various possibilities of trips with flights, hotel and car rental (if necessary), while taking into account the existing combinations between a set of possible dates and airports. In addition, there is an objective function to optimize which selects only one of all the possible combinations obtained by the process. An evaluation and a proposal of this part of the problem is analyzed in [15].
* **To obtain an imperative model that minimizes the execution time of the BP taking into account the data dependencies**: Although the activities to enjoy during the trip are: _\"take a car to go to the airport\"_, _\"catch the flight\"_ and _\"arrive to the hotel\"_, the process of booking each part of the trip does not have to follow the same sequence. If the input data of each activity were known, all the activities related to the provider could be executed in parallel. The problem arises when certain activity inputs are related to other activity outputs, or when the activities are executed or not depending on the information obtained from activities executed previously. The objective of this paper is the use of Artificial Intelligence techniques based on Constraint Programming in order to create an imperative model where the data dependencies are taken into account to minimize the execution time of each instance. For example, it is possible for a flight to arrive at its destination on a different day to when it takes off (overseas route), and therefore the check-in date in the hotel cannot be determined until this information is known. The question is: how to configure a business process formed by these activities to minimize the execution time of the instances? This implies the analysis of every combination of activities and to select the control flow gates that relate them.
## 4 Discussion
Very powerful approaches based on cumulative displacement maps of InSAR data, have in fact been successfully used to monitor ongoing phenomena, which are typically in their overt phase [21; 22]. The present method aims to be an early-stage monitoring system instead, able to forecast the incipient of potential ground instability phenomena. On the other hand, the early-warning monitoring techniques available in the literature are generally aimed at predicting specific events for which an analytical or semi-empirical model is known. For example, the instant in which a landslide will occur was predicted in [56] through the inverse speed method, while the failure mechanism of a cataloged landslide event was studied in [57] to forecast the occurrence of similar events in different geographical areas according to some influencing factors. An attempt to predict the collapse of large infrastructures was also made in [28] using deformation outliers. These approaches were shown to be effective albeit dependent on the assumed physical model and focused on monitoring a particular phenomenon. Unlike these examples, the method here proposed is independent of any preliminary theoretical model of the involved geological phenomena. It generates an alarm on the basis of the analogy of the monitored timeseries with a reference timeseries, which has already been classified or chosen for the rarity of the event. In practice, the monitoring can be broad-spectrum and not limited to a specific type of event, while the analysis of the detected potentially critical events is assumed to take place after the alarm generation. Nevertheless, the method can even be calibrated to forecast specific geological phenomena (incipit of landslides, activation of faults, foundation settlements due to human underground activities or to the variation of groundwater levels) by duly setting the monitoring criteria and hyperparameters. It is worth noting that the proposed method is still at a preliminary stage of development and its potential can be highlighted only after its application to real cases. In any case, even at an advantaged stage of development and application, the method is not autonomous since it requires a post-monitoring phase with in situ inspections. The abovementioned techniques available in the literature can be helpfully combined with the present method for a more comprehensive monitoring.
# Driver Identification and Detection of Drowsiness while Driving
###### Abstract  
This paper introduces a cutting-edge approach that combines facial recognition and drowsiness detection technologies with Internet of Things capabilities, including 5G/6G connectivity, aimed at bolstering vehicle security and driver safety. The delineated two-phase project is tailored to strengthen security measures and address accidents stemming from driver distraction and fatigue. The initial phase is centered on facial recognition for driver authentication before vehicle initiation. Following successful authentication, the subsequent phase harnesses continuous eye monitoring features, leveraging edge computing for real-time processing to identify signs of drowsiness during the journey. Emphasis is placed on video-based identification and analysis to ensure robust drowsiness detection. Finally, the study highlights the potential of these innovations to revolutionize automotive security and accident prevention within the context of intelligent transport systems.  
facial recognition; drowsiness detection; driver safety; machine learning; video-based identification +
Footnote †: journal:  
and authorities: the prevalence of driver drowsiness as a contributing factor to traffic accidents. In this regard, a detailed examination of the figures provided in the DGT report can shed light on the magnitude of this issue and guide future initiatives to enhance safety on Spanish roads. The report reveals several pertinent figures that underscore the imperative of implementing measures to address traffic accidents associated with fatigue or somnolence. Notably, in 2020, a total of 67 fatal accidents were attributed to this cause, constituting 9% of the concurrent factors contributing to accidents. Furthermore, there were 1458 accidents with victims linked to tiredness or sleepiness, representing 8% of the various factors contributing to accidents. These statistics highlight the critical need for targeted interventions to mitigate the impact of driver fatigue on road safety.  
This work has been developed in two fundamental stages (Figure 1). The first phase was designed to carry out facial recognition of the driver when attempting to start the vehicle, rigorously verifying whether the driver possesses the required authorization to operate it. Once this initial stage was completed successfully, the second phase involved the constant monitoring of the driver's characteristic signs of drowsiness throughout the journey, using facial features and the condition of their eyes as reference points, aided by IoT sensors. If a prolonged eye closure is detected, an alarm is activated through the vehicle's speakers to prevent potential accidents.  
This paper is structured as follows. Sections 2 and 3 provide context via a review of previous research and a discussion of different technical aspects, respectively. Subsequently, Section 4 describes the system design, and Section 5 delves into practical implementation details. Finally, Section 6 shows the results and Section 7 summarizes the main findings and their implications.
# Lead Monoxide Nanostructures for Nanophotonics: A Review
## 2 Synthesis of PbO Nanostructures
### Three-Dimensional PbO Hierarchical Heterostructures  
Because of relatively easier manipulation and large-scale preparation compared to 0D, 1D, and 2D nanostructures, 3D hierarchical nanostructures have become more appealing in many fields, such as catalysis [76; 77; 78], sensors [79; 80], optoelectronics [81; 82], etc. Although low-dimensional (0D, 1D, and 2D) PbO nanostructures have achieved great progress in recent years, 3D hierarchical PbO nanostructures are also significant due to their unique properties [44; 83; 84]. For example, [PERSON] and [PERSON] [83] successfully synthesized 3D PbO nanostructures by the thermal decomposition of PbC\({}_{2}\)O\({}_{4}\) nanostructures at 420 \({}^{\circ}\)C for 30 min, as shown in Figure 5a. Additionally, [PERSON] et al. [44] reported that 3D PbO nanostars and nanodendrites with several hundreds of nanometers were successfully synthesized when the subphase concentration of round or irregular PbO NPs was 1 \(\times\) 10\({}^{-4}\) mol L\({}^{-1}\). It can be seen that the as-synthesized 3D PbO nanostars contain a couple of nanodendrites, and each nanodendrite is composed of a trunk and several branches that are nearly vertical to the predominant trunk (Figure 5b). The SAED pattern shows that the as-synthesized 3D PbO nanostars display regularly arranged spots, which can be assigned to those of the \(\beta\)-PbO, suggesting that the as-obtained nanostars are single-crystalline.  
Figure 4: The structural characterizations of 2D PbO NSs. (**a**) Typical SEM image of PbO NSs synthesized by laser ablation in water and subsequent ambient aging (top-left inset: TEM image of an NS; top-right inset: the corresponding SAED pattern). Reproduced with permission from Reference [26]. Copyright 2020, Elsevier B. V. (**b**) Representative TEM image of PbO NSs prepared by an alkylamine-mediated thermolysis route in the presence of HDA (inset showing the corresponding magnified image). Reproduced with permission from Reference [49]. Copyright 2020, Elsevier B. V. (**c**) Typical TEM image of the PbO NSs fabricated by an LPE method showing the circle-shaped 2D NS feature (inset showing the HRTEM image). Reproduced with permission from Reference [19]. Copyright 2018, American Chemical Society.
# A Self-Paced Multiple Instance Learning Framework for Weakly Supervised Video Anomaly Detection
## 3 Proposed Method  
### Overview  
An overview of the proposed general SP-MIL framework is shown in Figure 2. For each input video, it is first split into non-overlapped, consecutive segments of unequal lengths. Then, the pre-trained features of these segments are extracted, and temporal modeling is applied to derive temporal context features. It is important to note that temporal modeling is a optional module. The temporal context features of the segments are then fed into a randomly initialized classifier to predict the abnormal score of each segment. Subsequently, the most representative segments are selected along with video-level labels for training this classifier. Once the training is complete, the trained classifier is employed as the baseline model of our proposed SP-MIL framework, which is then refined to obtain an enhanced classifier for unbiased prediction. The pseudo-code of the proposed SP-MIL framework is provided in Algorithm 1. Specifically, the anomaly scores of segments are first predicted and then converted into pseudo-labels via threshold binarization. The difficulty of each segment is then calculated based on its loss. Next, during each epoch of training, the age parameter of each video is first evaluated, reflecting the overall difficulty of the video and each segment, and is determined by the loss of all segments in the video. Then, the evaluated age parameter is used as a threshold to determine the self-paced learning weight of each segment, which is a binary hard weight with values of 0 or 1. Finally, the segment subset for training is selected based on the self-paced learning weights, and the selected segments along with their pseudo-labels are used to update the classifier. This process is repeated \(T\) times to obtain the enhanced classifier \(f_{T}\) with the optimal parameter \(\mathbf{\omega}_{T}\).  
```
Input: Baseline classifier \(f\) with parameter \(\mathbf{\omega}\), feature extractor \(g\), training video \(\{\mathbf{X}_{k}\}_{k=1}^{K}\), video-level labels \(\{y_{k}\}_{k=1}^{K}\), max epochs \(T\). Output: The optimal parameter \(\mathbf{\omega}_{T}\) of enhanced classifier \(f_{T}\).
1 Initialize classifier \(f_{0}=f\) with parameter \(\mathbf{\omega}_{0}=\mathbf{\omega}\).
2 for\(t=1,2,\cdots,T\)do
3 Predict the segment-level anomaly scores via Equation (6);
4 Generate the segment-level pseudo-labels via Equation (7);
5 Estimate the age parameter \(\mathbf{\lambda}_{t}\) via Equation (10);
6 Update the self-paced learning weights \(\mathbf{V}_{t}\) via Equation (12);
7 Select the segment subset with index \(\{l_{k}^{t}\}_{k=1}^{K}\) via \(\mathbf{V}_{t}\);
8 Update the classifier parameter \(\mathbf{\omega}_{t}\) via Equation (14);
9
10 end for Return \(\mathbf{\omega}_{T}\).
```  
**Algorithm 1**Pseudo-code of the proposed SP-MIL framework