esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Cutaneous squamous cell carcinoma (cSCC) is the second most common form of non-melanoma skin cancer, accounting for approximately 20% of all skin malignancies [1]. While most cases are diagnosed early and managed effectively with surgery or radiotherapy, about 2-5% progress to locally advanced or metastatic disease, which carry significant morbidity and a poor prognosis (median overall survival \(\leq\) 20 months) [2]. These advanced forms of cSCC are associated with significant morbidity, limited therapeutic options, and a poor prognosis, with median overall survival rarely exceeding 15-20 months in the absence of effective systemic treatment [3].  
Cancer immunotherapies--including immune checkpoint inhibitors, CAR-T cells, oncolytic viruses, and vaccines--work by reactivating antitumor immune surveillance, enhancing T-cell activation, or reshaping the tumor microenvironment to eliminate malignant cells [4; 5; 6]. However, these approaches face critical limitations: low overall response rates and primary or acquired resistance driven by an immunosuppressive tumor milieu; risks of immune-related adverse events (e.g., cytokine release syndrome); and challenges in drug delivery, manufacturing complexity, and high costs [6]. Ongoing research aims to overcome these hurdles via combination regimens, next-generation engineering (e.g., Fc-optimized antibodies and bispecifies), and novel small-molecule and cell-based strategies to improve efficacy and safety.  
Immunotherapy with cemiplimab, a fully human monoclonal antibody targeting programmed cell death protein 1 (PD-1), has shown 40-50% response rates in advanced cSCC [7; 8; 9], but nearly half of patients either fail to respond or develop resistance.  
Currently, few validated tools are available to anticipate the response to immunotherapy in cSCC [10; 11]. Conventional metrics, such as the maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), calculated with positron emission computed tomography (PET/CT), provide only a macroscopic tumor burden estimate, missing spatial heterogeneity [12].  
Emerging evidence suggests that radiomics, the high-throughput extraction and analysis of quantitative imaging features from medical images, may offer a deeper characterization of tumor phenotypes [13; 14]. Radiomic features capture subtle variations in voxel intensity, texture, shape, and spatial distribution that are often imperceptible to the human eye. Applied to [18]FFDG PET/CT imaging, radiomics could enable the identification of imaging biomarkers predictive of immunotherapy response by capturing the biological heterogeneity associated with tumor aggressiveness and immune evasion [15].  
In this study, leveraging our prior handcrafted PET/CT radiomics experience [16; 17] --a supervised, controlled feature-engineering approach--we aim to develop a baseline [18]FFDG PET/CT model to predict immunotherapy response in advanced cSCC and noninvasively determine tumor grade, thereby enhancing early patient stratification.
## 7 Evaluation and Discussion
### Discussion
#### 7.4.2 Effectiveness against Malicious Hosts  
Anti-Debug Instruction is designed to make sure that the execution environment of the program is unharmed. Currently, anti-debug techniques can be divided into two categories. One is to check hardware or software debug structure for the presence of debugger state such as breakpoint set. The other is to detect human behavior such as the undesired pause during program execution. An unambiguous rule for distinguishing the behavior of debugger is recognized as being highly significant for the effectiveness and accuracy of the anti-debug technique. However, one drawback of the current anti-debug mechanisms is that most of them fail to conceal themselves from other processes. If malware or debuggers perceive that they are monitored by anti-debug schemes, they would try their best to bypass such monitoring or even strike back using anti-anti-debug techniques. Anyhow, applying anti-debug technique into PVM-based software protection still can thwart the malicious behavior of an adversary as long as the host machine is harmless.  
Figure 14: Statistics results from 100 times of executions, M is for Monitor Handler, and P is for Process Handler.
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Design of State Space
#### 3.3.1 Probability Distribution
In order to evaluate the network QoS performance parameters reasonably, it is necessary to remove the dimensional difference between the related parameters first, and then obtain the cost value by normalizing these parameters and different weights. The vector \(\Omega=[\mu_{o}\quad\mu_{d}\quad\mu_{l}]\) is used to represent the indicator result value. Secondly, the weight vector of the QoS performance evaluation index is defined as \(B\), which can be expressed as \(B=[\alpha_{o}\quad\alpha_{d}\quad\alpha_{l}]\quad(\sum\limits_{i=o,\,d,\,l} \alpha_{i}=1)\). Under the premise of satisfying the basic criteria of the service index, the closer to the basic criteria, the smaller the weight value. The standard value here can be set empirically. Finally, whether the network is able to meet the service requirements of the business is the key to measuring the network status. According to this, the congestion factor is set and the congestion sensitivity factor \(\lambda_{ODL}\) for evaluating the network performance is defined, which is expressed by Equation (14). \[\lambda_{ODL}=\Omega\times B^{T}=[\mu_{o}\quad\mu_{d}\quad\mu_{l}]\times[\alpha _{o}\quad\alpha_{d}\quad\alpha_{l}]^{T}=\sum\limits_{i=o,d,\,l}\mu_{i}\alpha_{i} \tag{14}\]  
Through the above steps, the sensitive factors for evaluating network performance based on QoS on-demand drive are obtained. The QoS parameter values are stored in a matrix and updated periodically, through which the congestion factor is calculated from the matrix to achieve congestion control.
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 3 Results
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Authors** & **Year** & **Evaluation Method** & **Algorithm** & **Accuracy (\%)** \\ \hline [PERSON] et al. [3] & 2017 & \begin{tabular}{c} 5-Fold \\ Leave-One-Subject-Out \\ \end{tabular} & ANN & 98.57 \\ \hline [PERSON] et al. [9] & 2017 & 5-Fold & DNN & 96.73 \\  & 2017 & 5-Fold & RF & 94.00 \\  & & SVM & 92.81 \\ \hline [PERSON] [12] & 2019 & \begin{tabular}{c} Train/test by subjects \\ 20\% train as validation \\ \end{tabular} & LSTM-AE + SVM & 98.17 \\ \hline [PERSON] et al. [14] & 2021 & Random sub-sampling for cross-validation & 1D CNN & 99.82 \\ \hline [PERSON] et al. [18] & 2023 & \begin{tabular}{c} 5-Fold \\ Leave-One-Subject-Out \\ \end{tabular} & Optimized ResNet & 99.87 \\ \hline [PERSON] and [PERSON] [19] & 2023 & 80/20 train/test split & Patch-Transformer & 99.14 \\ \hline [PERSON] et al. [20] & 2024 & \begin{tabular}{c} 70/30 train/test split \\ 10-Fold \\ \end{tabular} & PCNN-Transformer & 98.30 \\ \hline Our approach & 2025 & \
\begin{tabular}{c} 5-Fold \\ \end{tabular} &
\begin{tabular}{c} BRNN (Optimized) \\ BRNN \\ RNN \\ RNN \\ RNN \\ RNN \\ \end{tabular} & 99.70 \\  & & Leave-One-Subject-Out & RNN & 98.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy of fall detection models reported in related studies and in our approach. Figure 3: Fall classification accuracy scores for Leave-One-Subject-Out Cross-Validation [3; 18].
## 2 Materials and Methods
### Chemistry
1H NMR (DMSO-d6, 400 MHz) \(\delta\): 2.42 (s, 3H, C1-CH3), 7.20-7.22 (d, 2H, J = 8.8 Hz, H2\({}^{\prime}\), H6\({}^{\prime}\)), 7.47-7.49 (d, 2H, J = 8.0 Hz, H2, H6), 7.72-7.74 (t, 1H, J = 7.6 Hz, H5\({}^{\prime\prime}\)), 7.76-7.78 (d, 2H, J = 8.4 Hz, H3, H5), 7.91-7.93 (d, 2H, J = 8.4 Hz, H2, H6), 7.91-7.93 (d, 2H, J = 8.4 Hz, H6\({}^{\prime}\)), 8.24-8.26 (d, 1H, J = 7.6 Hz, H4\({}^{\prime\prime}\)), 8.51-8.53 (d, 2H, H2\({}^{\prime\prime}\), N=CH), 12.15 (s, 1H, CONH). 13C NMR (DMSO-d6, 100 MHz) \(\delta\): 21.16 (C1-CH3), 120.95 (C4\({}^{\prime\prime}\)), 122.16 (C2\({}^{\prime}\), C6\({}^{\prime}\)), 124.31 (C2\({}^{\prime\prime}\)), 128.23 (C4\({}^{\prime}\)), 129.76 (C3, C5), 130.30 (C2, C6), 130.43 (C5\({}^{\prime\prime}\)), 131.20 (C4), 132.13 (C3\({}^{\prime}\), C5\({}^{\prime}\)), 133.38 (C6\({}^{\prime\prime}\)), 136.05 (C1\({}^{\prime\prime}\)), 145.63 (C3\({}^{\prime}\)), 146.03 (C1), 148.20 (N=CH), 151.34 (C1\({}^{\prime}\)), 162.26 (C=O). MS (ESI) \(m/z\) calcd. for C21H17N3O6S [M]+ 439.08, found 438.97. _N\({}^{\prime}\)-[(4-Nitrophenyl)methylidene)]-4-[(4-methylphenyl)sulfonyl]benzoylhydrazazide_ (**3**). Yield 98.8%. mp 227.3-228.0 \({}^{\circ}\)C. HPLC tR: 6.564 min, purity: 99.20%. 1H NMR (DMSO-d6, 400 MHz) \(\delta\): 2.43 (s, 3H, C1-CH3), 7.20-7.22 (d, 2H, J = 8.4 Hz, H2\({}^{\prime}\), H6\({}^{\prime}\)), 7.48-7.50 (d, 2H, J = 8.4 Hz, H3, H5), 2H, J = 8.4 Hz, H2, H6), 7.77-7.79 (d, 2H, J = 8.4 Hz, H3\({}^{\prime}\), H5\({}^sodium phosphate buffer (pH 7.5), respectively, and 0.5 mM 5,5\({}^{\prime}\)-dithiobis(2-nitrobenzoic acid) (DTNB) was used as a color developing agent, as described previously [25; 26]. Inhibitory activities were measured after preincubating enzymes and inhibitors for 15 min. BACE-1 activity was measured using a BACE-1 activity detection kit, measuring at an excitation wavelength of 320 nm and emission wavelength of 405 nm using a fluorescence spectrometer (FS-2, Scinco, Seoul, Korea) [27]. Antioxidant activity was measured using 0.2 mL of ethanol solution at 517 nm after incubating 50 uM compound and 0.1 mM 2,2-diphenyl-1-picrylhydrazyl (DPPH) for 15 min in the dark, as described previously [27]. Enzymes and chemicals were purchased from Sigma-Aldrich (St. Louis, MO, USA). The experiments were carried out for duplicate or triplicate.
# Real-Time Vehicle Energy Management System Based on Optimized Distribution of Electrical Load Power
## 5 Experimental Results and Analysis
### Experiment with Changing Load Power  
Under the NEDC, an experiment with changing load power is conducted. Figure 15 shows the power curves of the battery, alternator, electrical load, and output voltage curve of the alternator. The experiment results are similar to those of the experiment with unchanged load power. For the change of vehicle electrical appliances, the output voltage of the alternator is maintained between 13 V and 14.8 V; the battery SoC can fluctuate within a certain range; and the final SoC is almost equal to the initial SoC. However, the total output power from the battery and alternator is almost equal to the load power. This condition denotes that the energy management controller with the OPD strategy can  
\begin{table}
\begin{tabular}{c c c c} \hline
**Control Strategy** & **Distance (km)** & **Fuel Consumption (mL)** & **Fuel Consumption (L/100 km)** \\ \hline OPD strategy & 4.055 & 319.12 & 7.87 \\ OLC strategy & 4.049 & 324.73 & 8.02 \\ \hline \end{tabular}
\end{table}
Table 1: Fuel consumption of constant electrical load power.  
Figure 14: Results under the OLC strategy for the ECE15: (**a**) battery SoC; (**b**) output voltage of alternator; and (**c**) the power change comparison.  
distribute the demand of load power in real-time to the battery and the alternator, thereby decreasing the fuel consumption from the alternator.  
Table 2 presents a comparison of the fuel consumption of OPD and OLC under the NEDC. For 100 km, OPD consumes 0.14 L fuel less than OLC, and the fuel saving rate is about 1.7%. All of the results demonstrate that the OPD strategy can effectively reduce vehicle fuel consumption and increase vehicle fuel economy.  
Furthermore, Table 3 lists the improved effects of fuel economy from the A-PMP (Adaptive PMP) strategy in [9] and the OPD strategy in this paper. The A-PMP strategy in [9] can increase fuel economy by almost 1.4% while the OPD strategy in this paper can increase fuel economy by almost 1.7%. The improvement of fuel economy from the A-PMP and the OPD is very close. However, the implementation of A-PMP relies on modifying the structure of the alternator controller. It is much more complicated than the implementation of OPD based on an intelligent alternator. Therefore, OPD is superior to A-PMP on the implementation and engineering application.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Control Strategy** & **Distance (km)** & **Fuel Consumption (mL)** & **Fuel Consumption (L/100 km)** \\ \hline OPD strategy & 10.958 & 867.87 & 7.92 \\ OLC strategy & 10.961 & 883.46 & 8.06 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuel consumption of changing load power.  
Figure 15: Results under the OLC strategy for the NEDC: (**a**) output voltage of alternator; (**b**) battery SoC; and (**c**) the power change comparison.
## 3 Results
### Final Assessment
Subsequently, the data from each measurement, both from the initial and the validation measurements, should be processed and compared in accordance with the criteria outlined in Table 5.  
Test subject C exhibited a balanced right half of the saddle without pressure peaks during the initial pressure measurement, which was maintained during the validation measurement. The total pressure change of \(-14\%\) between the initial and validation measurements also had a beneficial impact on the overall result of the validation measurement. In essence, the outcome for subject C can be characterized as satisfactory. The pressure peak of the left saddle half was reduced by approximately 50%, while the already optimal right half was nevertheless slightly improved by \(-3\%\). It can thus be concluded that the considerable reduction in peak pressure for test subject C is attributable to the personalized saddle pad. The findings indicated that the implementation of functionally graded lattice structures based on pressure measurement data can markedly enhance the pressure distribution on the bike saddle. This is particularly evident in the data collected from test subjects A and C. An increase in pressure values was observed in test subject B during the validation measurement, indicating that the initial pressure measurement of this test subject yielded incomplete or faulty data. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Subject** & **A** & **B** & **C** \\ \hline Body size [cm] & 184 & 180 & 185 \\ Body weight [kg] & 81 & 82 & 80 \\ Accumulated pressure- & 142.440 & 129.730 & 159.450 \\ Initial measurement [mbar] & 135.170 & 147.940 & 136.220 \\ Accumulated pressure- & \(-5.1\) & +14.01 & \(-14.56\) \\ Peak pressure changes left [\%] & \(-14.25\) & +27.6 & \(-46.25\) \\ Peak pressure changes right [\%] & \(-16.51\) & +37.15 & \(-3.1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Alteration in peak pressure. Long-term testing is a crucial component of evaluating the long-term efficacy and comfort of personalized saddles. The feedback from the test subjects was predominantly positive. Test subject A performed an alpine crossing, testing the saddle pad over a distance of 500 km and 3600 m of elevation gain. Test subject B completed approx. 280 km and 1700 m of elevation gain without any issues or failures of the lattice struts. In particular, test subject C completed over 1500 km with his customized saddle, with only one issue concerning the separation of glue in one isolated area.
## Abbreviations  
ADC Analog-to-digital converter AC Alternating current CPWM Centered pulse width modulation DC Direct current DPWM Digital pulse width modulation DSP Digital signal processor FPGA Field-programmable gate array FPIC Fixed point induction control LC Inductive-capacitive MOSFET Metal-oxide-semiconductor field-effect transistor OS Overshoot PID Proportional integral derivative PWM Pulse-width modulation SISO Single-input and single-output SMC Sliding mode control SSEror Steady-state error ST Settling time SVPWM Space vector pulse width modulation ZAD Zero average dynamics ZAD-FPIC Zero average dynamics and fixed point induction control
# Mitigating Electrochemical Effects in Resistive Frost-Detection Sensors
## 2 Materials and Methods
### Sensor Design and Configuration  
Each sensor was enclosed within a sealed glass flask filled with distilled water and consisted of two parallel copper electrodes, each measuring 35 mm in length and 2.25 mm in diameter, with a 5 mm separation distance between them. The electrodes were fully submerged in distilled water, which was chosen as a medium to represent the condensed water formed inside the HX during operation. The sensor configuration is illustrated in Figure 6.  
The two flask-encased sensor electrodes were mounted onto a nonconductive 3D-printed jig consisting of a lid and a spacer. Figure 7 shows the two sensors. The sensor on the right, connected via blue cables, was operated under the continuous-power approach and remained constantly powered throughout the experiment. Conversely, the sensor on the left, connected via green cables, utilized the pulsed DC excitation method, in which \(V_{in}\) was pulsed to assess the potential benefits of intermittent excitation in reducing electrochemical effects.  
The sensors were intentionally designed to be larger than the application size to enhance visualization of electrochemical reactions during the experimental analysis. While previous studies have focused on miniaturization by using smaller electrodes [16; 17], the larger electrode dimensions in this study allow for better observation of potential electrochemical effects.  
Copper was chosen as the electrode material as it was the material used in previous studies [16; 18]. It was chosen because it has good electrical conductivity, is widely available and cost-effective, and can be easily shaped and soldered. While copper is not as resistant to electrochemical reactions as noble metals, its behavior under an electric field depends on  
Figure 6: Rendered front view of the individual sensor configuration.  
environmental conditions. In distilled water, significant reactions are not expected in the absence of voltage. However, when an electric field is applied, electrochemical migration and oxidation can occur [23], degrading the electrode material.  
If the sensor were to be implemented in a long-term application where electrode degradation became a concern, plating or the use of noble metals such as platinum or gold could be considered to mitigate these effects, as their high electrochemical stability and resistance to oxidation help prevent degradation and improve performance over time. This would mitigate electrochemical reactions involving the electrodes themselves, limiting the system's behavior to water electrolysis only (Equations (3)-(7)), without electrode oxidation or material loss.
## 1 Introduction
[16] proposed a multi-scale perceptual CNN that adaptively fuses variable density logging (VDL) features, achieving over 90% accuracy in bond quality identification. [PERSON] et al. [17] proposed RheologyNet, a physics-informed neural network (PINN) framework that embeds rheological PDEs to capture macro-micro thixotropic behaviors. [PERSON] et al. [18] proposed a transfer learning strategy for cement isolation classification by converting VDL data into continuous wavelet transform (CWT) images and fine-tuning pretrained deep models, achieving accuracies of 97.4% to 99.6%. These studies demonstrate that artificial intelligence (AI)-based methods offer significant advantages in prediction performance compared with traditional approaches for cement bond quality evaluation. However, existing models still face several critical chal lenges in practical applications. First, most current studies primarily rely on well-logging data, often neglecting crucial cementing operational parameters such as displacement efficiency, flow rates, and fluid properties, all of which play a decisive role in the formation of cement bond quality. Some studies utilize CBL-VDL data, which are acquired after the cementing operation. While these data are useful for post-job evaluation, they lack timeliness and are insufficient for proactive optimization or real-time control during the cementing process. Second, certain studies focus on predicting cement sheath characteristics under laboratory conditions without validating the models in complex field environments. This raises concerns about the adaptability and accuracy of such models in real-world applications. Third, most existing research assesses cement bond quality at a holistic level, without distinguishing between the casing-cement sheath interface and the cement sheath-formation interface. However, these two interfaces differ fundamentally in terms of mechanical behavior and failure mechanisms. The absence of interface-level modeling limits the interpretability of the results and reduces their engineering relevance. Therefore, developing high-precision models that integrate multi-source parameters, enable interface-specific prediction, and are adaptable to complex field conditions has become an urgent demand in this field. To address the aforementioned challenges, this study proposes a novel Wide and Deep neural network architecture capable of simultaneously predicting the cement bond quality at both the casing-cement sheath and cement sheath-formation interfaces. The model is trained and validated on a field dataset comprising 30,000 samples from eight wells in an oilfield located in western China. The consistency coefficient and flow behavior index are embedded into the network structure, and a temperature correction mechanism is incorporated to further enhance prediction accuracy. The objective of this study is to improve the adaptability, interpretability, and accuracy of cement bond quality prediction models, thereby providing reliable data-driven support for optimizing cementing design and reducing operational risks.