esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Regression Prediction Experiments
\begin{table}
\begin{tabular}{l l c c c} \hline \hline
**Algorithm** & **Course Name** & **Intercept** & **MSE** & **RMSE** & **RE** \\ \hline \multirow{4}{*}{\begin{tabular}{l} Linear \\ Regression \\ \end{tabular} } & Compiler Principles & \(-\)0.34 & 72.32 & 8.50 & 9.27\% \\  & Operating Systems & 11.43 & 84.27 & 9.18 & 9.28\% \\  & Data Structures & 9.21 & 74.85 & 8.65 & 9.77\% \\  & Algorithm Analysis & 31.38 & 53.60 & 7.32 & 7.3\% \\  & and Design & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{l} Ridge \\ Regression \\ \end{tabular} } & Compiler Principles & \(-\)0.33 & 72.32 & 8.50 & 9.27\% \\  & Operating Systems & 11.43 & 84.27 & 9.18 & 9.28\% \\  & Data Structures & 9.21 & 74.85 & 8.65 & 9.77\% \\  & Algorithm Analysis & 31.38 & 53.60 & 7.32 & 7.3\% \\  & and Design & & & & \\ \hline \multirow{4}{*}{\
\begin{tabular}{l} Lasso \\ Regression \\ \end{tabular} } & Compiler Principles & 1.00 & 71.05 & 8.43 & 9.19\% \\  & Operating Systems & 12.34 & 84.34 & 9.18 & 9.29\% \\  & Data Structures & 10.37 & 75.35 & 8.68 & 9.82\% \\  & Algorithm Analysis & 32.08 & 52.42 & 7.24 & 7.23\% \\ \hline \multirow{4}{*}{
\begin{tabular}{l} Decision Tree \\ Regression \\ \end{tabular} } & Compiler Principles & None & 110.08 & 10.49 & 11.28\% \\  & Operating Systems & None & 189.89 & 13.78 & 12.48\% \\ \cline{1-1}  & Data Structures & None & 151.93 & 12.33 & 13.83\% \\ \cline{1-1}  & Algorithm Analysis & None & 119.07 & 10.91 & 10.41\% \\ \cline{1-1}  & and Design & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison table of evaluation indicators for four types of regression algorithms. outperformed the others with the highest accuracy. On the other hand, decision tree regression had the lowest accuracy in this case. In conclusion, the results suggest that lasso regression performs the best overall in accurately predicting student performance in these courses, while decision tree regression tends to be the least accurate among the regression algorithms.
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
## 2 Materials and Methods
### Source Implementation  
Since experimental data from the ELIMAIA-ELIMED source are not available yet, a realistic experimental source term (based on data from the J-KAREN-P PW-class laser facility in Japan [29]) was implemented in the simulation. The energy and angular distributions were based on the data from [PERSON] et al. [30], where they used a laser beam with an irradiance of 5 \(\times 10^{21}\) W\(\cdot\)cm\({}^{-2}\) and a stainless steel target with a thickness of 5 \(\upmu\)m placed at 45\({}^{0}\) with respect to the laser direction. On the other hand, the spatial distribution was assumed to be Gaussian with a standard deviation equal to 10 \(\upmu\)m. All the distributions are presented in Figure 1.
# Carbon Dynamics of _Pinus palustris_ Ecosystems Following Drought
## 3 Results
### Annual Carbon  
During years of severe drought, we observed declines in all three components of the carbon cycle (NEE, GEE and R\({}_{\text{eco}}\)) with the largest declines in the 2010-2011 year, which corresponded to the lowest  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Year** & **Total Precipitation (mm)** & **NEE** & **GEE** & **R\({}_{\text{eco}}\)** & **GEE/R\({}_{\text{eco}}\)** \\ \hline \hline Mesic & 2008–2009 & 1474.5 & \(-133.4\) & \(-1970.9\) & 1837.3 & 1.07 \\  & 2009–2010 & 1181.1 & \(-106.6\) & \(-1764.2\) & 1657.6 & 1.06 \\  & 2010–2011 & 765.8 & \(-8.3\) & \(-1359.3\) & 1351.0 & 1.01 \\  & 2011–2012 & 1091.0 & \(-331.4\) & \(-2177.5\) & 1846.1 & 1.18 \\  & 2012–2013 & 1504.2 & \(-251.3\) & \(-2060.3\) & 1809.0 & 1.14 \\  & 2013–2014 & 1313.9 & \(-287.4\) & \(-1990.1\) & 1702.7 & 1.17 \\  & 2014–2015 & 974.3 & \(-3391.1\) & \(-2115.8\) & 1776.6 & 1.19 \\  & Average & 1186.4 & \(-208.2\) & \(-1919.7\) & 1711.5 & 1.12 \\ Xeric & 2008–2009 & 1360.9 & 124.7 & \(-1601.0\) & 1725.8 & 0.93 \\  & 2009–2010 & 1017.8 & 15.0 & \(-1528.6\) & 1543.6 & 0.99 \\  & 2010–2011 & 755.1 & 6.5 & \(-1366.6\) & 1373.1 & 1.00 \\  & 2011–2012 & 893.3 & \(-81.4\) & \(-1956.1\) & 1874.7 & 1.04 \\  & 2012–2013 & 1507.8 & \(-229.4\) & \(-1971.5\) & 1742.1 & 1.13 \\  & 2013–2014 & 1217.9 & \(-148.4\) & \(-1817.2\) & 1668.8 & 1.09 \\  & 2014–2015 & 1069.3 & \(-202.9\) & \(-2087.7\) & 1884.8 & 1.11 \\  & Average & 1117.4 & \(-73.7\) & \(-1761.2\) & 1687.6 & 1.04 \\ \hline \hline \end{tabular} Years are delineated as measurements taken from 22 October at 10:00 a.m. to 22 October at 09:30 a.m. the following year.  
\end{table}
Table 1: Annual precipitation (mm), Net Ecosystem Exchange of Carbon (NEE, g C m\({}^{2}\)-year\({}^{-1}\)), Gross Ecosystem Exchange of Carbon (GEE), Ecosystem Respiration (R\({}_{\text{eco}}\)), Ratio of GEE to R\({}_{\text{eco}}\).  
annual precipitation rates and PDSI values of \(<\)\(-\)5.0 (Figure 2). Interestingly, both sites seemed to have similar variance for their fluxes over the 7 years of the study; however, mean carbon uptake from the mesic site was large and the site remained a sink over the entire study period (Table 1). During periods of drought the xeric site carbon balance was driven by both a reduction in gross photosynthetic rates and by a dominance of respiration rates over NEEday (Table 1). These results are further validated when examining the changes in averaged diurnal patterns of NEE, GEE and Reeco during recovery from drought when physiological activity increased at both sites (Figure 1).
## 2 Materials and Methods
### QRNG Implementation
where \(R\) and \(\delta\) are, respectively, the acquisition range and bin width of the previously considered ADC module, \(\sigma_{Q}\) is the experimentally measured standard deviation of the quantum noise distribution, and \(e_{\text{max}}\) is the maximum excursion considered for the classical noise signal. Here, this parameter was taken as \(5\sigma_{E}\) so that, for any given classical noise sample, only a low probability, approximately \(5.73\times 10^{-7}\), of it falling outside the considered interval \([-e_{\text{max}},e_{\text{max}}]\) was observed.
## 5 Evaluation and Results
### Visualization-Based Insights  
To provide a clearer understanding of access behavior, we analyzed simulation outcomes using three visual representations.  
Figure 8 shows the overall access outcome distribution. Out of 1500 total access requests, 356 were granted while 1144 were denied, reflecting the strict enforcement of attribute-based policies in our framework.  
Figure 9 depicts the percentage of successful access requests per user. The results indicate that users affiliated with regulatory roles and possessing higher clearance levels had significantly higher grant rates, whereas general users lacking sufficient attributes consistently experienced denials.  
Finally, Figure 10 presents an access matrix mapping users to resources. Granted and denied requests are visually clustered, illustrating how various attribute profiles either satisfied or failed to meet the ABAC policy conditions. This visualization reinforces the observed selectivity of the access control logic.  
Figure 8: Access Outcome Distribution.  
Figure 9: User-wise Grant Rate (%).
## 2 Dark Current Noise Correction Method Based on Dark Pixels
### LWIR QWIP Detection System Design  
The main components of the LWIR QWIP detection system are shown in Figure 4. The blackbody model HFY300 for small surfaces was manufactured by the Shanghai Fuyuan Photoelectric Company (Shanghai, China). The blackbody's surface diameter is 100 mm, its operational range is 20-70 \({}^{\circ}\)C and its temperature controlling accuracy is 0.01 \({}^{\circ}\)C. The LWIR QWIP detection system operates as follows. Radiation emitted from the blackbody is transmitted through the detector window to the image pixel; photons interact with electrons to produce the current signal, which is collected and processed by the readout circuit and output as an analog signal. Next, the information acquisition system proportionally amplifies and uses a low-pass filter for the analog signal output from the detector, performs an analog-to-digital conversion with 12 quantization bits, generates the digital signal, and transmits it to the Field Programmable Gate Array (FPGA). Finally, the FPGA transmits the processed digital signals to the data acquisition system via TLK2711 and stores them as image data. The quantized data stored in the system are called Grey value or Response value and their units are DN.
## 8 Challenges in Biofilm-Associated HAIs
### Biofilm Resistance to Disinfectants  
A primary problem in controlling HAIs is biofilms' inherent resistance to disinfectants. Biofilms provide a protective environment for bacteria, safeguarding them from the impacts of several disinfectants and antimicrobial therapies, thereby enabling pathogens to survive under rigorous infection control protocols [365]. This resistance is especially significant in DSBs, which develop on healthcare surfaces and have enhanced tolerance to disinfection and physical removal methods relative to wet surface biofilms and planktonic bacteria [366; 367].  
One such instance is _C. auris_, a multidrug-resistant fungus that has shown resilience to disinfectant doses often used in clinical environments, as documented by [PERSON] et al. (2018). This capability complicates surface sterilizing and heightens the risk of HAIs linked to this fungus [191]. Likewise, biofilm-forming bacteria, including _A. huumannii_, exhibit resistance to commonly used disinfectants such as benzalkonium chloride and chlorhexidine, as emphasized in the study by [PERSON] et al. [368]. These results highlight the inadequacies of commonly used disinfectants in eliminating biofilms and controlling HAI-related bacteria.  
Healthcare institutions have used many cleaning and disinfection methods to tackle these difficulties. Strategies include bleach, QACs detergents, ultraviolet irradiation, hydrogen peroxide vapor (HPV), and copper-treated surfaces or textiles [356; 367]. HPV systems, in particular, have emerged as a powerful tool, capable of penetrating biofilm structures and eradicating embedded pathogens through the generation of ROS. These approaches have demonstrated efficacy in decreasing the occurrence of HAIs and MDROs [369; 370]. Despite these measures, the enduring presence of biofilm-associated organisms underscores the need for ongoing innovation and enhancement in disinfection protocols.  
Improving cleaning practices and investigating innovative disinfection technologies are essential to reduce biofilm-related hazards. Future research must concentrate on creating chemicals and techniques that may breach the protective biofilm matrix, thereby enhancing disinfection efficacy and decreasing the incidence of HAIs.
# Testbed for LoRaWAN Security: Design and Validation through Man-in-the-Middle Attacks Study
## 2 Background  
### LoRa and LoRaWAN Basics  
The LoRaWAN technology consists of the two main elements: the physical (PHY) layer solution based on the proprietary LoRa modulation, and the link (LL) and network layer (NWK) specification. The latter is described in the open standard governed and developed by the LoRa Alliance [6].  
The LoRa modulation is a variant of a frequency-chirp-spread-spectrum M-ary digital modulation, in which the instantaneous frequency is linearly increased and then wrapped to the minimum frequency when it reaches the maximum frequency of the occupied band [7]. One of the critical parameters of the LoRa modulation is the spreading factor (SF), which corresponds to the number of chips per symbol and is inversely proportional to the modulation rate of the chirp [8]. By increasing the SF a transmitter increases the time on-air, thus increasing the energy consumption and reducing the data rate, but boosts the maximum possible communication range. Notably, the signals with different SFs are quasi-orthogonal, allowing transmissions with different SFs to be correctly received simultaneously.  
Following the LoRaWAN specification, a network is composed of a single network server (NS), one or multiple gateways (GWs) and end devices (EDs). In addition to these,  
Figure 1: Results of the keyword search for the terms “LoRaWAN” and “LoRaWAN” + “Security” in Google Scholar for selected years 2014–2020 (predicted values are dotted). The correlation trend is R = 0.893 with a square root of R\({}^{2}\) = 79.84%.  
a network may include a dedicated join server (JS) and provide interfaces to application servers (ASs). An ED is typically represented by a sensor equipped with a radio transceiver, allowing it to communicate with a GW. Depending on the implemented class (denoted A, B or C in LoRaWAN), the media access procedure and the capability of an ED obtaining the data in downlink somewhat differs. The most basic LoRaWAN class, i.e., class A, implies Aloha channel access for uplink with a random selection of a frequency channel among all supported by the network. The two obligatory receive windows are opened by the ED after such an uplink transmission at scheduled times and using pre-specified frequency channel and SF configuration. A GW routes all the received packets to the NS through an Internet protocol (IP) backbone network connection (Ethernet, LTE, etc.). Notably, unlike the traditional cellular systems, in LoRaWAN an ED is not associated with a specific GW. Instead, all GWs forward all correctly decoded packets to the NS. The NS also features an interface for the AS, which serves as a user interface for management and data presentation and acquisition purposes. From the point of view of cyber security, the most susceptible in this architecture is the wireless connection between an ED and a GW, where a number of attacks, including, e.g., a man-in-the-middle (MitM) attack, can be carried out. The on-air LoRaWAN security is based on encryption and authentication procedures, which we detail in the following subsections.
# MLPs Are All You Need for Human Activity Recognition
## 5 Experimental Setup
### Ablation Study  
The Mixer is ablated to compare the importance of different design choices of the MLP-Mixer in HAR. The different design choices involve the architecture of the Mixer (token-mixing MLP, channel-mixing MLP) and the RGB embedding layer. The macro \(F_{1}\) score is used in the ablation study to assess the model. This prevents high evaluation scores by simply choosing the majority class in imbalanced datasets and provides accurate insight into the model's learning capabilities across class activities.  
**The MLP-Mixer without RGB Embedding:** The Mixer saw a slight decrease in performance, which meant that this layer made some contribution to the Mixer's learning capabilities. This allows the sensor data to simulate the RGB channels in images. This  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Opportunity** & **PAMAP2** & **Daphnet Gait** \\ \hline
**Specifications** & & & \\ \hline
**Number of Layers** & 10 & 10 & 10 \\
**Patch Resolution** & 11 & 4 & 9 \\
**Input Sequence Length** & 49 & 210 & 14 \\
**Patch-Embedding Size** & 512 & 512 & 512 \\
**Token Dimension** & 256 & 256 & 256 \\
**Channel Dimension** & 2048 & 2048 & 512 \\
**Learnable Parameters (M)** & 21 & 21 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of the Mixer architecture for each dataset.  
produces three sets of features for the Mixer to project into its embedding space instead of a single set of features from the single sensor channel. The results are tabulated in Table 3.  
**The MLP-Mixer without the Token-Mixing MLPs:** The model had a significant decrease in performance in all the datasets without the token-mixing MLPs. The Mixer uses token-mixing to learn global context from the input and communicate information between patches; without this layer, the Mixer cannot effectively capture the spatial and temporal information of the activities in the datasets. The results tabulated in Table 3 indicate the Mixer loses its capabilities to learn relevant features of the dataset; hence, it can be concluded that the token-mixing MLP is necessary for the Mixer to perform well in HAR benchmark datasets.  
**The MLP-Mixer without the Channel-Mixing MLPs:** The channel-mixing MLPs allow the model to communicate between channels, essentially acting as a \(1\times 1\) convolution. This enables the Mixer to detect features between channels, and without it, only spatial information between the various patches will be learned. The results tabulated in Table 3 showcase substantial performance loss, which indicates that the channel-mixing MLP is important for HAR. However, the performance loss is lower than the performance loss in the absence of the token-mixing MLPs. This indicates that the channel-mixing MLP is a supplement to the token-mixing MLP, communicating the information learned from the token-mixing layer across channels rather than capturing core features needed for accurate prediction in HAR.
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 7 Correlation Analysis
### Influence of Dominant Features on Fatigue Behavior
#### 7.3.2 Material Grade: Direct Influence on Fatigue Resistance  
Material grade was ranked as the second most important factor, affecting fatigue resistance through mechanical properties such as yield strength, hardness, and micro structural stability. This is explained by the fact that the higher-grade materials exhibit enhanced endurance limits, resisting cyclic strain accumulation and delaying fatigue crack initiation. The models reinforced that material strength correlates with fatigue life, supporting empirical observations from strain-life and stress-life fatigue models.
## 3 Test Results and Analysis
### Test Phenomenon and Failure Modes
#### 3.1.4 Strengthened Beam B1  
The reinforced beam B1 was in the elastic stage during the initial loading. When the load was increased to 24.26 kN (24% \(P_{\rm m}\)), the first vertical crack appeared in the concrete layer in the tensile area of the pure bend section. With the increase in the load, the number of the B1 beam concrete cracks gradually increased and the crack distribution was fine and dense. When loaded to 90.28 kN (89% \(P_{\rm m}\)), the longitudinal reinforcement reached the yield load, the concrete cracks in the tensile area bifurcated at the bottom, and longitudinal cracks appeared on the concrete side within the scope of the protective layer. When the loading continued, the deflection increased rapidly. When the loading reached 101.61 kN, the peak load was reached. The thin layer of epoxy mortar reinforcement broke near the middle of the span, and the load dropped to 82.44 kN (81% \(P_{\rm m}\)). As the load first slowly increased and then gradually decreased, the deflection continued to increase rapidly. The load dropped to 80.51 kN (79% \(P_{\rm m}\)), and the concrete in the pressure area was crushed. The load dropped sharply to 69.29 kN (68% \(P_{\rm m}\)), and the test was stopped. The distribution of the cracks is shown in Figure 10d.
## 3 System Models and Problem Formulation
### Problem Formulation  
Assuming the FL server can access the state information of all clients, including their channel gains, CPU frequencies, and transmission power, and that all clients are time-synchronized and cooperative, adhering to the clustering, workload, and bandwidth allocations provided by the FL server. We formulate the problem to jointly optimize client clustering, bandwidth allocation, and workload management in semi-synchronous FL as follows.  
\[\mathbf{P0}: \operatorname*{arg\,max}_{\mathbf{d}\mathbf{x},\mathbf{b}}\sum_{i\in\mathbf{ \mathcal{I}}}\left(d_{i}\sum_{j\in\mathbf{\mathcal{J}}}w_{j}x_{ij}\right),\] (4) s.t. \[\forall i\in\mathbf{\mathcal{I}},\Bigl{(}t_{i}^{comp}+t_{ij}^{wait}+t _{ij}^{uplond}\Bigr{)}x_{ij}\leq j\tau, \tag{5}\] \[\forall i\in\mathbf{\mathcal{I}},D^{min}\leq d_{i},\] (6) \[\forall i\in\mathbf{\mathcal{I}},\sum_{i\in\mathbf{\mathcal{J}}}x_{ij}=1,\] (7) \[\forall i\in\mathbf{\mathcal{I}},\forall i\in\mathbf{\mathcal{J}},x_{ij}= \{0,1\},\] (8) \[\sum_{j\in\mathbf{\mathcal{J}}}b_{j}\leq B. \tag{9}\]  
where \(x_{ij}\) is a binary variable to indicate if client \(i\) is clustered in tier \(j\) (i.e., \(x_{ij}=1\)) or not (i.e., \(x_{ij}=0\)), \(\mathbf{x}=\bigl{\{}x_{ij}|\forall i\in\mathbf{\mathcal{I}},\forall j\in\mathbf{\mathcal{ J}}\,\bigr{\}}\), \(\mathbf{d}=\bigl{\{}d_{i}|\forall i\in\mathbf{\mathcal{I}}\,\bigr{\}}\) is the workload allocation in terms of the number of data samples being used to train local models for all the clients, \(\mathbf{b}=\bigl{\{}b_{j}|\forall j\in\mathbf{\mathcal{J}}\,\bigr{\}}\) implies the amount of bandwidth allocated to different tiers, and \(w_{j}\) indicates the frequency of the clients in tier \(j\) uploading their local models. i.e., \(w_{j}=\frac{|\mathbf{\mathcal{I}}|-j+1}{|\mathbf{\mathcal{J}}|}\). Here, \(|\mathbf{\mathcal{J}}|\) indicates the last tier. Hence, a larger \(w_{j}\) means tier \(j\) is a lower tier, and clients in this tier upload their local models more frequently. Training a model over more data samples leads to higher model accuracy. Based on this intuition, we set up the objective of \(\mathbf{P0}\) so that the number of training data samples per unit time is maximized, where \(\sum\limits_{i\in\mathbf{\mathcal{J}}}w_{j}x_{ij}\) is the frequency of client \(i\) in uploading its local model, and \(d_{i}\) is the number of data samples used to train its local model. Constraint (5) indicates the clients in different tiers should meet their tier deadlines. Constraint (6) defines the minimum number of the samples, denoted as \(D^{min}\), to be trained locally by each client. Constraint (7) specifies each client should only be clustered into exactly one tier. Constraint (8) refers to \(x_{ij}\) being a binary variable, and Constraint (9) means the amount of bandwidth allocated to all the tiers no larger than \(B\), where \(B\) is the available bandwidth at the BS.