esa-sceva/satcom-chunk-collection huggingface dataset:
# Seasonal Shifts and Smart Stats: Improving Biodrying in Waste Management
## 4 Results
### Process Stability and Control  
The X-bar control chart illustrated in Figure 2 shows the drying efficiency across the bioboxes over time, with the mean drying efficiency represented with a green dashed line and the control limits (\(\pm 3\sigma\)) marked with red dashed lines. The outliers, identified as points exceeding the control limits, are highlighted with red \"X\" markers.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Herhof Rottebox® & **Count** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline
1 & 38 & 0.25 & 0.05 & 0.14 & 0.22 & 0.25 & 0.3 & 0.37 \\
2 & 43 & 0.24 & 0.07 & 0.08 & 0.19 & 0.24 & 0.28 & 0.4  
The analysis reveals a mean drying efficiency of approximately 28%, with the upper and lower control limits set at \(\pm\)3 standard deviations (\(\sigma\)) from the mean. These control limits define the boundaries within which the process is expected to operate under normal conditions, providing a clear framework for assessing process stability. The biodrying process demonstrates statistical control, as the majority of data points fall within the established control limits. However, several outliers--extreme drying efficiency values--are apparent on the chart, indicating occasional occurrences of special cause variation. These deviations suggest that certain cycles may have been influenced by irregular factors, such as variations in waste composition, operational inconsistencies, or environmental conditions. Although the process is generally stable, it consistently underperforms relative to the desired lower specification limit of 30% drying efficiency. This indicates a need for further optimization of the biodrying process to achieve higher and more consistent efficiencies. Addressing the factors contributing to outliers and overall underperformance could enhance both the stability and effectiveness of the system, ensuring it meets operational and environmental goals.
## 4 Application of the Concepts: Design of an In-Line Measuring Machine
### Control System Architecture and Connectivity  
The design of the control architecture plays an important role in data collection and processing time. Figure 8 shows a functional analysis tree of the current measuring station. The system was conceived to measure the flatness of the casting and display the data. No provision was made to do any processing or storage of the measurement data.  
Figure 7: Flash and heat checking (thermal fatigue) marks are typically found on the die’s surface. Such defects can affect the measurements.  
In comparison, Figure 9 shows a section of the function tree of the new design. The main function, Monitor Casting, has several steps such as fix, position, and measure the part, display quality control results, provide safety to operators, monitor environmental conditions, and provide Connectivity to the system.  
Compared with the current equipment, the new design adds a step, \"Position part\". This step was added as a precaution to isolate the measuring probe and the environment's positioning system as much as possible and while it does not contribute to improving the  
Figure 8: Simplified function analysis tree of the current measuring station. Physical components are shown in dashed boxes.  
Figure 9: The functional tree for the proposed design. Connectivity and monitoring of the environment are support functions that help improve intelligence of the machine.  
operation, it helps guarantee reliability. The other new functions, Provide Connectivity and Monitor the Environment, are intended to improve the intelligence of the control, and provide the capacity to respond to unexpected conditions. For example, information about the part, or about what the machine perceives from the environment can be used to anticipate and avoid conditions that can result in the waste of energy or resources. The machine performs a series of automatic functions once the part is placed on the fixturing plate: Clamp the part, insert it into the measuring volume, measure the target features, report to the operator the result of the measurements, and store selected data. The following sections describe how Connectivity was used to improve the capacity of the control system to monitor the product, the process, and adapt to changes. The result is an overall improved intelligence thanks to the access to the I4.0 resources provided by connectivity.
## 7 Conclusions  
Interoperable data among sensors making similar measurements is a topic gaining more importance as the number of optical remote-sensing satellites increases. Being able to combine data sets significantly increases the number of data points that can be incorporated into time-series analyses of the Earth's surface properties. However, difficulties remain developing these time series because of differences in the design and observations made by the sensors. The impact of calibration on data interoperability is not well understood and represents an area of improvement for the community. To address this issue, a workshop with a panel of experts was held in conjunction with the Pecora 20 conference focused on data interoperability between Landsat and the Sentinel 2 sensors.  
Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between agencies flying optical remote-sensing satellites, as well as between agencies and the broader public. The use of multiple electronic methods, including social media, was suggested. The second recommendation was to adopt collection-based processing of data recorded by the sensors. This means that the entire archive is reprocessed, not just a portion of it, and only when needed to update parameters that significantly affect applications of the data. The third area of recommendation dealt directly with calibration methodologies. It consisted of a list of changes that are both simple and difficult to implement that would improve radiometric, geometric, and cross-calibration. The fourth,and most ambitious, recommendation is to develop a comprehensive process for validating land surface reflectance products. This is needed because nearly all science users require a surface product for their work. It is difficult because these measurements are not made routinely, a standard process needs to be developed, and the effort needs to be global in nature. Fortunately, it appears that many agencies worldwide are aware of this issue and interested in working together to address it.  
Introduction, D.H.; Calibration Status of OLI, B.M., J.S., R.M.; Calibration Status of Sentinel 2, F.G., S.C., B.L.; Calibration Comparison of Landsat 8 and Sentinel 2, J.B.; Current Status and Limitations of Data Interoperability with Landsat and Sentinel, J.M., D.R., N.P., A.L.; Recommendations, D.H., Conclusions, D.H.  
This research received no external funding  
**Acknowledgments:** The authors wish to acknowledge support of the workshop by the organizations which employ the panelists. Authors 5, 6 and 7 thank [PERSON] (ARGANS) and [PERSON] (Thales Alenia Space) for providing the results for Sentinel-2 radiometric and geometric validation respectively.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Design of the Control System
#### 2.3.2 Dragon Fruit Weight Information Access  
The sorting system is used to achieve the weighing and weight sorting of dragon fruits. The STM32F4 microcontroller is selected to be the controller; after the target dragon fruit is in contact with the pressure sensor, the pressure of the fruit makes the resistance of the resistance strain gauges change and causes a change in the voltage, so as to output a signal. The signal is transmitted to the HX711 A/D converter module and converted into a digital signal; the signal is then converted into an accurate weight value by the microcontroller program. The pressure sensor weighs the dragon fruit, and the weight information is transmitted to the microcontroller through an HX711 A/D converter module. Processed by the internal program of the microcontroller, the signal was converted into an accurate weight value. The conversion process is as follows.  
The output voltage, \(U\), of the sensor at full scale is as follows:  
\[U=U_{I}\cdot k \tag{3}\]  
where the following is the case:  
\(U_{I}\)--excitation voltage.  
\(k\)--sensitivity, \(k\) = 1 mV/V.  
Figure 11: (**a**) Growth state of dragon fruit under a dark scene; (**b**) visual recognition rendering under a dark scene.  
Figure 10: Comparative visualization of the model test set.  
If the range of the pressure sensor is set to 5 kg and 5 V is used for power supply, then the output voltage, \(U\), is 5 (mV), which is equivalent to the sensor under 5 kg gravity, the output voltage \(U\) is 5 mV, according to the circuitry of the load cell module, and the actual output voltage \(U_{2}\) = 4.6 mV. When the pressure sensor is operating, the output voltage, \(U\), is as follows:  
\[U=\frac{4.6X}{R} \tag{4}\]  
where the following is the case:  
\(X\)--fruit weight, kg.  
\(R\)--sensor range, kg.  
Substituting the sensor range \(R\) = 5 kg into Equation (4), we obtain the output voltage \(U\) = 0.92X (mV). The HX711 A/D converter module can amplify the sensor output voltage 128 times, at which point the output voltage, \(U\), is 128 \(\times\) 0.92\(X\) = 117.8\(X\) (mV). Set the measured AD value to \(Y\); convert the output voltage, \(U\), into a 24-bit digital signal; and then obtain the following:  
\[Y=\frac{U\cdot 2^{24}}{4.6} \tag{5}\]  
Substituting \(U\) = 117.8\(X\) in Equation (5), we obtain \(Y\) = 429,496.7\(X\). Hence, we obtain the following:  
\[X\approx\frac{Y}{429.5}(g) \tag{6}\]  
Equation (6) shows the relationship between the measured AD value and the fruit weight. According to this formula, the corresponding microcontroller program can be written, which increases the processing of the oscillation signal in the program, and the acquisition of dragon fruit weight information can be completed by eliminating the oscillating signals.
## 3 Results  
### Poulticing Tests  
The water calibration for impedance between 1 cm separated electrodes is shown in Figure 1.  
A good linear response was recorded, with detection down to 0.23%, as defined by ISO 11929 [27]. Whilst lower concentrations of liquid water would not be detected by the system, this value was considered sufficient to determine the waterfront from politicing.  
The initial analysed salt contents at different depths are shown in Figures 2 and 3. Figure 3 shows results considered to be representative of the whole large data set.  
The stone impregnation appears to have been consistent, with less than 10% difference between the triplicate drillings.  
The ratios of chloride:nitrate:sodium for the initial 2 cm of the stone drillings and the solutions extracted from the poultices are shown in Table 2, with nitrate or chloride set to 1.  
The salt concentrations from the subsequent drillings show reduced levels at 3 cm depth (positive t tests with 96-99% certainty) and likely also at 4 cm (positive t tests with 89-93% certainty). No reductions in salt concentrations at 5 and 6 cm were observed.  
Figure 6 shows the electrode impedance results from representative stone C.  
The embedded electrodes indicated liquid water had penetrated to a depth of 2 cm, but none was detected at a 3 cm depth.  
The poultices appear to have removed a substantial portion of the salt from the surface 2 cm of the stone. The ion balances appear to have been retained with the system investigated. This is important as thermodynamic modelling, ECOS, uses these values to determine the stable and unstable temperature and RH regions for the salt mixture present. The ECOS sensitivity analysis showed the variation in values from the two techniques led to the upper RH stability value increasing from 68 to 70%. For the salt mixture tested, temperature has little effect. This RH variation is within the instrumental error of many hygrometers used to monitor heritage environments [28]. Poulticing appears sufficiently representative of the salts present to allow ECOS calculations to assess suitable environments. Uncertainty as to the information depth means the conversion from ion concentration per area to per volume is unpredictable. Whilst the results are likely to allow gross-scale calculations of stability, this uncertainty would mean results near the boundaries between stable and unstable would need to be confirmed with drilled samples. However, a significant portion of stones and tablets could be successfully analysed this way.
## 3 Results
### Cancer Classification  
We also evaluated the performance of the _cancer_ classification task, which determines whether the lesion is malignant, as shown in Table 4. Due to the high similarity of benign and malignant lesions, the cancer classifier is a much more challenging task than the mass classifier. As seen in the first row of Table 4, without a _mask branch network_, the network is hardly trained with approximately 0.5 metrics. The performances of all of the cases with DenseNet are improved compared with the plain network. MBN-V2 looks the best-performing version in cancer classification, similar to mass classification in terms of sensitivity and AUC, although the highest specificity is from MBN-V4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Template Label** & **Se** & **Sp** & **AUC** \\ \hline No mask & 56.3\% & 86.3\% & 0.855 \\ Loc. & 67.5\% & 90.0\% & 0.908 \\ Loc.+Size & 72.5\% & 90.0\% & 0.921 \\ Loc.+Size+Orien. & **75.0\%** & **92.5\%** & **0.925** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison on set of characteristics utilized for creating Mask Label; position, diameter, orientation, posterior eco pattern. Metrics ranked with 1 st place are in bold.  
Figure 7: Class activation maps (CAMs) to show the effect of the mask branch network (MBN). (**Top row**) Breast mass samples on ABUS images marked by red boxes. (**Middle row**) CAMs of DenseNet. (**Bottom row**) CAMs of DenseNet with MBN.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 6 Conclusions  
This paper presented a simplified localization method based on BLE and supervised ML classifiers that could apply in robust indoor localization applications. Since the proposed localization method is based on ML, it could be easily deployed in practical environments as the ML model could run on tiny embedded systems. The RSSI values were collected from thirteen different iBeacon nodes installed in an indoor environment utilized in this experiment. The RSSI data were linearized using the weighted least-squares method and filtered using moving average filters to remove the outliers. Afterward, RSSI data were trained using the SVM, kNN, and FFNN algorithms. During the experiments, each algorithm's hyperparameters were tuned, and the impact on its accuracy was observed. It was observed that there were significant improvements in the accuracy once the sample size was increased. Further, training time was observed against the sample size and the number of epochs versus accuracy. The training time increased as the number of samples increased. Further, accuracy had a significant improvement once the epoch size increased. Overall, the kNN offers reasonably good accuracy in classifying the right zone of 85%, the FFNN provides 76% when the batch size is 100, under a learning rate of 0.01, and the SVM provides 84% with 75 support vectors.  
Conceptualization, M.W.P.M. and V.T.; methodology, M.W.P.M. and V.T.; software, M.W.P.M.; validation, M.W.P.M., V.T. and R.A.; formal analysis, M.W.P.M. and V.T.; investigation, V.T. and R.A.; resources, M.W.P.M. and V.T.; data curation, M.W.P.M. and V.T.; writing--original draft preparation, M.W.P.M., V.T. and R.A.; writing--review and editing, M.W.P.M. and V.T.; visualization, M.W.P.M. and V.T.; supervision, V.T. and R.A.; project administration, V.T. and R.A.; funding acquisition, V.T. and R.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare that there are no conflict of interest regarding the publication of this paper.
## 4 Experiments and Results
### Proposed Model Construction
#### 4.4.2 Analysis of Linguistic Features  
We investigated the influence of each linguistic feature set on predictive performance. We trained the \(F_{0}\) models by using several combinations of linguistic feature sets. The best-performing RNN architecture (FF-BLSTM) with the same hyper-parameters was used. The RMSE and CORR of the models trained by different combinations of feature sets are shown in Table 5. The results show that the total feature set substantially improved prediction performance (PH_TN was better than PH). However, the combination of all features (PH_TN_PS_DU) achieved the best performance. This indicates that including a combination of phone and tonal feature sets improved prediction performance.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model Architecture** & **Number of Parameters (Million)** & **RMSE (Hz)** & **CORR** \\ \hline SBLSTM & 0.32 & 8.306 \(\pm\) 2.71 & 0.924 \(\pm\) 0.04 \\ FF-BLSTM & 1.32 & 8.172 \(\pm\) 2.65 & 0.927 \(\pm\) 0.04 \\ BLSTM-FF & 0.60 & 8.167 \(\pm\) 2.76 & 0.925 \(\pm\) 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Objective results of best model in each network architecture (\(\pm\)denotes standard deviation and RMSE denotes root mean-squared error).  
Figure 4: Model architecture. (**a**) Stack bi-directional long-short term memory (SBLSTM). (**b**) FF-BLSTM: Feedforward followed by bidirectional LSTM (BLSTM). (**c**) LSTM-FF: BLSTM followed by feedforward.
Sensors_**2009**, \(9\), 3958-3980; doi:10.3390/s90503958  
_Acticle_  
**Using Reputation Systems and Non-Deterministic Routing to Secure Wireless Sensor Networks**  
**[PERSON] \({}^{\star}\), [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Dpto. Ingenieria Electronica, Universidad Politecnica de Madrid, ETSI Telecomunicacion,  
Av. Complutense, 30, 28040 Madrid, Spain  
E-mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS];  
[EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS]; Tel.: +34 915495700, ext 4224: Fax.: +34 913367323  
_Received: 9 April 2009; In revised form: 4 May 2009 / Accepted: 19 May 2009_  
_Published: 25 May 2009_  
**Abstract:** Security in wireless sensor networks is difficult to achieve because of the resource limitations of the sensor nodes. We propose a trust-based decision framework for wireless sensor networks coupled with a non-deterministic routing protocol. Both provide a mechanism to effectively detect and confine common attacks, and, unlike previous approaches, allow bad reputation feedback to the network. This approach has been extensively simulated, obtaining good results, even for unrealistically complex attack scenarios.  
**Keywords:** security; reputation system; wireless sensor networks; routing protocol; sybil attack; countermeasure
## 3 Materials and Methods
### Headspace Solid-Phase Microextraction (HS-SPME)  
The excess seawater from the fresh sample was removed by placing a part of the sample between two layers of filter paper for a few minutes. HS-SPME was performed using an autosampler, PAL Auto Sampler System (PAL RSI 85, CTC Analytics AG, Zwingen, Switzerland), and two SPME fibres of different polarities. One fiber was covered with DVB/CAR/PDMS (divinylbenzene/carboxen/polydimethylsiloxane) and the other one with PDMS/DVB (poly-dimethylsiloxane/divinylbenzene). Both fibres were conditioned prior to the extraction according to the manufacturer and were purchased from Supelco Co. (Bellefonte, PA, USA). Prepared samples, with the mass of 1 g, were placed into HS-20 mL glass vials sealed with a polytetrafluoroethylene (PTFE)/silicon septa stainless-steel cap. Equilibration of the sample was performed at 60 \({}^{\circ}\)C for 15 min and then it was extracted for 45 min. The temperature of the injector was set to 250 \({}^{\circ}\)C. Thermal desorption of the sample from the fibre was conducted directly to the GC column for 6 min. HS-SPME was performed in triplicate.
## 1 Introduction
### Related Work  
#### 1.1.1 Task Allocation  
The challenge of _task allocation_ between crowdsourcers and crowdworkers, or, conversely, the autonomous selection of a declared task by crowdworkers from crowdsourcers, has garnered attention from both academia and industry [7]. A set of novel algorithmsfor influence-aware task assignment in Spatial Crowdsourcing are introduced in [8], utilizing workers' historical patterns, a historical acceptance approach, and a propagation optimization algorithm to maximize both task assignments and worker-task influence in the era of widespread smartphone use. A methodology for optimizing resource allocation in crowd-based cooperative computing is discussed in [9], focusing on evolutionary heuristics to balance matching rate and collaborative quality, supported by suitable metrics and multicriteria decision-making, demonstrating effectiveness through experiments on various scales of crowd-based cooperative task allocation problems. Focusing on the challenge of online task assignment with specific time windows for data collection, the authors in [10] formulated a profit maximization problem for the crowdsourcing system, proposing two heuristic algorithms whose effectivenesses were validated through simulation results. A mobile crowdsourcing mechanism for urban-scale monitoring is proposed in [11], addressing the cost-fair task allocation problem to balance sensing costs among mobile users, offering offline and online algorithms for efficient and fair task distribution. The concept of opportunistic mobile crowdsourcing is analyzed in [12] by addressing challenges related to uncertain worker trajectories, incorporating worker and task requester preferences and capacity constraints, and proposing novel task assignment algorithms that are proven to be optimal in terms of preference awareness. A game-theoretic approach for task allocation in crowdsourcing is presented in [13], addressing the challenge of optimizing crowdsourcing task assignment by incorporating trust evaluation, virtual currency incentives, and a bargaining game model.