esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Qualitative Evaluation  
We evaluated the output of the trained network, in the first instance, by visual inspection. This allowed us to interpret the results and judge them in the light of the pathologist's experience and diagnostic reasoning in histopathology.  
In most cases, we observed that the areas reported as false positives (blue pixels in Figure 8d) actually corresponded to small nests of stromal infiltration, located in correspondence with the tumor invasion front.  
Vice versa, we observed that a portion of the pixels reported as false negatives actually referred to small areas of stroma within the tumor area.For example, by observing the colored mask in Figure 8d, it is visible that the stromal area within the tumor is considered as a set of false-negative pixels (coloured in red) even if they should be considered as true positives.  
Given the discussion above, from a qualitative point of view, the algorithm has often reported pixels relating to areas of intense inflammatory infiltrate as false positives. However, single interspersed lymphocytes in the peritumor stroma were not per se a problem in the interpretation of the image.  
Figure 8: An example of error analysis for a test image. (**a**) The original image included in the test set. (**b**) The corresponding annotation mask, where carcinoma pixels are coloured in white. (**c**) The predicted mask generated from U-Net with ResNet50 encoder. (**d**) The coloured error mask, where green pixels are true-positive (TP) carcinoma pixels, blue are false-positives (FPs), and red are false-negatives (FNs).  
Tumor grading was another determining factor in the efficiency of the algorithm. In fact, the trained network recognized with difficulty the highly differentiated tumor areas, with a prevalence of keratin pearls. This last factor could be attributable to the fact that the dataset used for the training was mainly composed of a series of high grade advanced squamous carcinomas.
## 3 Numerical Simulation
### Thermomechanical Testing and Validation
So, the temperature difference between the outer and inner surface would be larger than that in the case of the assumption of a perfectly insulated condition at the inner surface. The permanent deflection was about \(-0.8\) mm at the end of the test. It was small compared with the diagonal length of 240 mm (less than 1%). Figure 11: Experimental setup for thermomechanical testing of sandwich structures. Figure 12: Thermomechanical testing results of the sandwich structure. (**a**) Temperature difference and temperature measurements of the outer and inner face sheets, (**b**) total deflection of the inner face sheet with time, (**c**) an out-of-plane deformation field at 533 s measured by the digital image correlation (DIC) method, (**d**) an out-of-plane deformation field at 530 s simulated by the numerical simulation, unit: mm. The comparison in total deflection along the diagonal length between the experiment and the numerical simulation is shown in Figure 12b. The total deflection curve of the numerical simulation result followed the general tendency of the total deflection curve of the experimental result very well. There was a difference between the simulation and experiment during the time that the temperature maintained at a constant value at the outer face sheet. The reason could be explained by the experimental boundary conditions of the tested sample and the imperfection in the fabricated sample. It is also obvious that the deformation field of the tested structure agreed well with that of the simulated model, as shown in Figure 12c,d. Therefore, the current model was of acceptable accuracy and could be used to investigate the thermomechanical performance of the corrugated core sandwich structure. A picture of the sandwich structure after the thermomechanical testing is shown in Figure 13a and the von-Mises stress field is shown in Figure 13b. The sandwich structure was in good condition without any failure. There were several permanent deformations in local areas near the corner edges of the structure. The localized permanent deformation was the result of the excessive stress (391 MPa) over the yield strength of the material (210 MPa). However, this excessive stress was very localized and much lower than the ultimate strength of the material (505 MPa); therefore, there was no failure in the tested sandwich structure after a thermal experiment. The outer surface of the tested sandwich structure was oxidized, since the untreated surface was used.
# Robotics Applications in the Hospital Domain:
## 4 Robotic Systems in Hospitals Settings
### Social and Assistive Robots  
In the healthcare context, for social assistive robotics (SAR)-enabled treatment to be effective, it is essential that the patient trusts both the operator and the robotic system [4]. A good example of trust in an SAR system is the PARO robot, which is used in affective therapy sessions, specifically for patients diagnosed with dementia [52]. Although some research suggests a greater preference for the PARO system over other systems in the same category (NAO and Bandit), there is still no scientific evidence that this PARO system is used in more effective therapy sessions than the other systems (see [PERSON] et al., 2019) [53; 54].  
This trust is a significant predictor of treatment adherence, satisfaction with treatment, and continuity of care. Trust in the patient-therapist relationship is positively correlated with rehabilitation and treatment outcomes, including pain reduction, improvement in disability, physical and mental health, and satisfaction with treatment. It is therefore crucial to prioritize building trust in the therapeutic relationship, particularly as new technologies such as SAR are integrated into standard care [4].  
Table 6 provides a comprehensive overview of the assistive robots used in hospital settings, comparing them in terms of characteristics, advantages and disadvantages, and stage of development. This comparison includes various robots, like PARO, NAO, and Bandit, highlighting their unique features and their impact on patient care [52; 54].  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Robot System** & **PARO—Advanced Interactive Robot [52; 54]** & **NAO—Bipedal Robot [53]** & **Bandit + Humanoid Robot [54]** \\ \hline \multirow{4}{*}{**Description**} & Used in dementia care units to calm patients and providing companionship, leading to reduced use of psychotropic medications and improved patient well-being (cost \(\approx\) EUR 55 kJ [52; 54]. & Used in pediatric wards to entertain and distract children during medical procedures, reducing anxiety and stress (cost \(\approx\) EUR 12 kJ [53]. & Used in physical rehabilitation to assist with exercises, providing encouragement and feedback to enhance effectiveness (cost \(\approx\) EUR 13 kJ [54]. & Used in physical rehabilitation to assist with exercises, providing encouragement and feedback to enhance effectiveness (cost \(\approx\) EUR 12 kJ [53]. & \multirow{2}{*}{Research stage [54].} \\ \cline{1-1} \cline{3-
# Explainable AI-Enhanced Human Activity Recognition for Human-Robot Collaboration in Agriculture
## 4 Discussion  
### Interpretation of the Results from a Broader Perspective  
The present study provides a thorough evaluation of the proposed XGBoost-based HAR model, with a focus on understanding the underlying dynamics of feature contributions through XAI. In summary, the proposed model achieved a relatively high accuracy (93%) in classifying human activities, highlighting also its robustness in distinguishing between similar tasks. However, certain misclassifications were observed between similar activities involving subtle differences, such as bending or lifting an empty crate compared to a full crate. These misclassifications can be attributed to overlapping biomechanical patterns during these activities, emphasizing the complexity of accurately recognizing slight variations.  
Beyond the commonly reported metrics for assessing the accuracy of the ML model, the approach adopted in this study offers significant value through its explainability, a feature notably absent in prior studies related to agriculture. The use of SHAP allowed for an in-depth understanding of the contributions of individual features. The global feature importance analysis highlighted the dominant role of magnetometer-based features, particularly those located in the lumbar region, cervix, and chest, which demonstrated their relevance in capturing core movements, as also pointed out in [26, 27]. Magnetometers play a central role because they are effective in accurately determining orientation, rotational movements, and subtle changes in body posture [68, 69]. These characteristics are essential for distinguishing between various human activities, especially those involving complex movements, such as bending, lifting, or carrying loads. Unlike accelerometers, which primarily measure linear acceleration, and gyroscopes, which detect angular velocity, magnetometers provide a reliable sense of directionality and spatial orientation. This makes them highly valuable for tasks requiring precise posture analysis and movement tracking, such as those performed in this study. However, the present study expanded on previous work [27] by identifying specific wrist sensor features, particularly all the magnetometric components of both wrists as well as the \"ax\" component. This divergence is likely due to the broader range of activities considered in the present analysis, which includes differentiation based on crate weight.
# Energy Management Integrated Circuit for Multi-Source Energy Harvesters in WBAN Applications
## 4 Circuit Block Designs
### Analog Blocks: Path Switches, Comparators, and Oscillator
Furthermore, in this system, there is no need for comparator hysteresis. The path switch is controlled by the digital control logic, and the comparators only inform about the amount of stored energy. The digital logic determines the path to be connected at the next clock time. As checking time for the status of the stored energy is not synchronized with the turn on-time for a path switch, the changes in the state of the stored energy after checking the state does not affect the switch operation. When the digital logic sends a switch control signal, the comparator is disabled. Therefore, the transition of the switch between on and off has no effect on the operation of the comparator. Without the need for hysteresis, the comparator becomes simple, and easy to implement. The oscillator provides clock signals to the digital control logic. The schematic of the oscillator in Figure 6c shows a fully differential relaxation type. Each side of the oscillator is composed of three stage inverters in series. When the voltage of the charging capacitor with a constant current reaches Vref, the comparator shifts the output of the differential amplifier to ground. The grounded output of the differential amplifier discharges the charging capacitor. The repeated charging and discharging in the charging capacitor induces oscillation. The adopted symmetric differential structure increases the overall oscillator loop, reduces the sensitivity to power supply variations and allows seamless oscillation.
## 3 System Implementation
### Receive and Storage  
Whenever the server receives a packet, it decompresses the packet to the appointed path of storage to get the original data. As shown in Table 2, the triggered time (Time), the location of the node (Coords), the identification code of the node device (IMEI), and the noise level (Decibel) are stored in MySQL for efficient management. Especially, the GPS devices submit data in the form of sentences, which are defined by the NMEA-0183 protocol. There are lots of sentences in the NMEA protocol and it is infeasible to save the whole emitted information in a database, therefore we are going to deal with only the $GPRMC sentence, which contains the necessary minimum information for location purposes, as can be seen in Appendix A. After obtaining the coordinates of latitude and longitude which can be extracted from the $GPRMC sentence, we can convert the coordinates to the corresponding map coordinates by calling the API interface of Baidu Map. Finally, we save the map coordinates in MySQL for a direct call in the monitoring platform. The monitoring platform automatically accesses these data to update the information displayed on the platform.
## 2 Literature Review
### Public Charging Infrastructure Spatial Allocation Methods  
The requirements and needs for charging infrastructure are mostly addressed by scholars in the research on and management of electric passenger vehicles and light-duty ones. At the same time, the literature on the methodologies addressing the needs of infrastructure planning for heavy-duty electric buses focuses mostly on the analysis at the urban scale. In comparison to location methods for private passenger charging stations, the ones for buses are mostly quantitative ones that can be classified into optimization models based on various factors and the analysis of geospatial characteristics (Table 2). The authors of [32] used a mathematical cost-minimization method as pure economic optimization is not suitable due to several non-economic factors involved in charging infrastructure planning. They considered such variables as onboard bus batteries and buses, together with charging infrastructure, when deciding on the suitability of a location.  
References [33; 34] proposed using a mixed integer linear model for the analysis of multiple bus routes at the urban level in Salt Lake City (USA) and Berlin (Germany), respectively. Reference [35] used a similar a mixed integer linear cost optimization model to analyze the potential for the substitution of traditional public transportation modes by barouches with electric buses, using a function of the least energy-consuming routes. The authors of [14] analyzed public transport electrification potential at urban level in Stockholm with the identification of potential charging locations using a mathematical cost- and energy-optimization and a geospatial analysis using ArcGIS. Lately, a mixed type of analysis has been introduced that also considers expert opinion integrated with spatial and economic analysis [13]. These scholars also note that more mixed qualitative-quantitative types of decision-making systems for the localization of charging stations for electric buses is needed, considering the complexity of such installations.  
To the authors' knowledge, the only study addressing the location of charging infrastructure at the regional level was performed by [12]. They developed a model that considers distinctive spatio-temporal characteristics and difficulties associated with longer-distance operation of battery-electric buses. The model suggests a potential for the optimization of investment based on a variety of strategies that can be adopted by public transportation operators operating at the regional level for charging infrastructure based on operation characteristics specific to electric buses.
## Solution
### Discussion  
In order to evaluate the security of security protocol implementations, based on assumption (3), we proposed MCSPI scheme to construct client valid request message. In MCSPI, firstly, we intercept net-traces and parse them. Next, we use API tracing technology to trace cryptographic functions to obtain API traces and dependencies of cryptographic functions contained in client-side implementations. Then, we locate message tokens. Apart from that, we reconstruct the output of traced cryptographic functions with out-of-the-box re-execution. Finally, we replace located tokens with reconstructed outputs to generate constructed client request messages.  
In summary, MCSPI has the following three characteristics:  
1. The analysis objects are the message tokens that are the minimum compositions of protocol messages.
2. The internal structures (dependencies) of cryptographic primitive implementations in client-side implementations are used to locate and reconstruct the output of cryptographic primitive implementations, which is powerful for constructed valid request messages.
3. MCSPI can deal with both cryptographic and noncryptographic protocols.  
In some sense, MCSPI is related to protocol reverse engineering, especially to net-trace technology. In general, net-trace technology is used for the purpose of inferring protocol format [22; 23]. For example, PIP [22] inferred protocol format based on the Needleman-Wunsch algorithm [36], and Discover [23] used tokenization clustering and merging technology to infer both text and binary protocol formats based on net-traces. However, on the basis of net-trace technology, the objective in MCSPI is not to infer protocol formats but to generate message tokens to construct client request message of security protocol implementations.  
In this sense, MCSPI is similar to AUTFORGE [7]. However, there is a distinct difference in identifying message tokens between AUTFORGE and MCSPI. AUTFORGE uses the Needleman-Wunsch algorithm to identify message tokens, but MCSPI identifies message tokens through matching the output of traced cryptographic functions with message tokens generated by parsing net-traces, which is easier to implement.  
Currently, MCSPI is not powerful enough in automatically extracting client-side implementations and analyzing encrypted or obfuscated implementations, which will be enhanced in our future work.
## 2 E-Waste Management System  
In the overall e-waste management system, there are several mandatory steps [11,12]:  
1. Collection--carried out at the place of origin.
2. Sorting--involves sorting according to the categories of waste from electrical and electronic devices, and can be performed at the household level, at the local community level, at the landfill, or at recycling centers.
3. Separation--includes shredding, separation of recyclable from non-recyclable parts, and separation of useful components by one of the usual separation methods. The final quality of the recyclate depends on the efficiency of this step.
4. Final processing--involves the processing of previously separated recyclable materials by hydrometallurgical or pyrometallurgical process.
5. Disposal of non-recyclable parts of e-waste.  
In order to encourage the development of the circular economy and recycling, it is most expedient to influence the key step, which is the separation of e-waste. Waste separation has numerous positive effects, with the most important one standing out [13].  
* Reducing the amount of waste;
* Extending the exploitation life of the landfill;
* Controlling the management of hazardous waste, which is separated from nonhazardous waste in a timely manner by proper sorting;* Increasing the efficiency of recycling;
* Overall environmental protection.  
The separation of waste can be performed manually or automated, whereby automation can be direct or indirect [14]. Manual separation of waste is based on macroscopically observable differences between recyclable waste components. Direct automated separation techniques are based on concentration methods that primarily originate from mining and use the preparation of raw mineral materials, methods such as gravity, magnetic, electric, and flotation concentration [15; 16]. Indirectly automated separation is based on the use of artificial intelligence and sensors to recognize certain types of waste [17]. \"Macro sorting\" refers to the separation of components, items, and packaging from the waste without any special pretreatment. \"Micro sorting\" occurs when the waste undergoes prior shredding to enhance its surface area relative to its volume [18].  
Of all the listed techniques, manual separation is considered the most precise, but not the most efficient, given the high risk of injury and high labor costs [19]. Manual separation is twice as cheap as automated separation, but automated separation can pay for itself three times faster than manual separation [20]. With the rapid advancement of technology and the emergence of innovative technological solutions, an automated technique for efficient waste separation has emerged, involving the use of robots. The integration of robots into the e-waste management system is a significant step towards achieving the goals of the circular economy, and the benefits of switching from a manual to a robotic waste separation system are numerous, including the reduction in illnesses and injuries at work, the acceleration of the separation process, the reduction in costs, the increase in recycling efficiency, the long working life of robots, and so on [21].
# Learning-Based Branching Acceleration for Unit Commitment with Few Training Samples
## 4 Branching Acceleration for Unit Commitment
### Feature Design
#### 4.2.2 UC Domain-Specific Features  
For the MILP problem, branching tree-related features are universal; however, relying solely on these features is insufficient for learning an effective policy [33]. Therefore, incorporating UC domain-specific features is critical for enhancing the performance of the policy model.  
In power system dispatch, the UC problem is typically formulated as an optimization task aimed at minimizing total generation cost--ensuring economic operation by accurately determining each unit's operational status and output levels to meet load demand at  
Figure 1: Example graph of optimal nodes for the B&B algorithm.  
minimal cost. Drawing on UC domain knowledge and the need for generalization across systems with varying unit counts, we selected the following key features:  
1. Daily load features: Maximum and minimum daily loads capture crucial load information to establish overall power output limits, thereby affecting unit operation.
2. Unit cost features: Operating costs directly influence generation expenses. Within the B&B tree, the generation cost and startup/shutdown costs associated with the current branching variable determine a unit's potential operating state.  
These features are essential for the design and optimization of the policy model, significantly enhancing its performance. Moreover, normalizing these features further improves the model's generalization capability.
## 3 Results
### Dynamic Analysis of Gaze  
The dynamic analysis of the drivers' gaze used a transition matrix that showed a transition of their gaze from one AOI to the next one, as indicated in Figure 8. The Figure shows that as more transitions were made, the color turned darker. In Figure 8a, which suggests a transition matrix of the novice group, gaze transitions were observed in diagonal direction not only between the traffic-related AOIs (traffic lights, pedestrians, and passing vehicles) and the ones inside the vehicle (dashboard and side mirrors), but also between the two consecutive AOIs. In addition, the experienced group were engaged in more gaze transitions between the right- and left-side mirrors and the rear-view mirror to examine the surrounding road conditions, and Figure 8b suggests that the transitions were evenly made across the entire AOIs. In general, the novice group conducted a visual search in the physically close AOIs, whereas the experienced group carried out a visual search frequently to assess the traffic situations. For estimation of GTE that represents the amount of information indicating gaze transitions, Equation (2) was used, and the results showed that the novice group had a lower GTE (2.21 \(\pm\) 0.19 bits) than the experienced group (3.04 \(\pm\) 0.16 bits). In addition, independent sample \(t\)-tests on the novice and experienced  
Figure 7: Number of fixations by AOI at intersection. (**a**) division and size of each AOI; (**b**) novice driver; (**c**) experienced driver.  
driver groups revealed statistically significant differences (t((22) = 4.24, \(p\) = 0.02), and small effect size (Cohen's d = 0.51). This signified that the novice drivers were not as various as the experienced group in observing the surrounding objects.