esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mantel Tests Analysis  
Mantel tests showed that there was no significant correlation between the bacterial communities of the larval gut and brood provision, larval gut and nest soil, brood provision, and nest soil in each plot. (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Plot** & **Sample 1** & **Sample 2** & **Mantel Statistics R * & \(p\)-Value** \\ \hline \multirow{4}{*}{SD} & Gut & Brood provision & \(-0.01818\) & \(0.49167\) \\  & Gut & Soil & \(-0.5273\) & \(0.96667\) \\  & Brood provision & Soil & \(0.5515\) & \(0.14167\) \\  & Gut & Brood provision & \(0.3939\) & \(0.1\) \\ \multirow{4}{*}{CQ} & Gut & Soil & \(0.2606\) & \(0.21667\) \\  & Brood provision & Soil & \(0.6485\) & \(0.05\) \\ \multirow{4}{*}{LN} & Gut & Brood provision & \(0.1879\) & \(0.31667\) \\  & Gut & Soil & \(-0.4545\) & \(0.96667\) \\ \multirow{4}{*}{XJ} & Brood provision & Soil & \(-0.05455\) & \(0.55833\) \\  & Gut & Brood provision & \(-0.2606\) & \(0.79167\) \\ \multirow{4}{*}{XJ} & Gut & Soil & \(0.0303\) & \(0.5\) \\  & Brood provision & Soil & \(-0.2242\) & \(0.70833\) \\ \hline \end{tabular}  
* The Mantel statistic R closes to 1 indicates that the positive correlation between the two matrices was stronger. It closes to \(-1\) indicates the negatively correlation was stronger. Number of permutations: 999; Correlation coefficient: Spearman.  
\end{table}
Table 3: Mantel correlations among Bray–Curtis distance matrices based on bacterial OTUs in gut, brood provision, and nest soil.  
Figure 5: Nonmetric multidimensional scaling (NMDS) analysis of gut (**A**); brood provision (**B**); nest soil (**C**); and the above three groups in total (**D**).
# Experimental Investigation of Column Separation Using Rapid Closure of an Upstream Valve
## 3 Observations and Results  
In this work, the column separation phenomenon that occurs in the downstream side of a closing valve was investigated. The influence of initial flow velocity, reservoir static pressure head, and pipeline length on column separation events in an experimental apparatus (Figure 1) was studied. Experimental runs were performed for the rapid closure of ball valves, BV1# or BV2#, with three constant downstream static heads in Tank 2 (\(H_{T2}\) = 1.55, 2.55, 3.55 m). The flow direction was from Tank 1 to Tank 2. The initial static head in Tank 1#, \(H_{T1}\), varied with an interval of 0.01 m in each experimental case, which created different initial flow velocity conditions. The experiments thus contained 28 individual cases, as listed in Table 2.  
An initial steady flow condition was established by controlling the water levels in both the upstream and downstream tanks. All valves were fully open. Then, a valve was closed rapidly to initiate a water hammer event. Each experimental case was repeated at least three times until the magnitude and timing of the main pressure pulse were almost the same. In order to better establish the consistency of the results, each repetition was carefully conducted.
## 3 Compact Model Validation
### Validation of DC Characteristics  
The validation of the compact model against the dark current characteristics was performed following the extracting of relevant model parameters from the forward and reverse current measurements. These measurements were performed in the bias range of (\(-\)3, 1) V, and (\(-\)5, 1) V for InGaAs/InGaAsP and GaInAsSb/InP UTC-PDs, respectively. All measurements were performed at room temperature. Figure 13a,b compare the measured DC characteristics and the compact model simulation, demonstrating good accuracy of the model over the entire forward and reverse bias ranges. All UTC-PDs under test show very low dark current levels (below 25 nA at \(-\)2 V). The low values of the dark current implies a higher quality of the passivation of the junction in the UTC-PDs. We note here that a study of the dark currents at different temperatures along with compact model validation has been presented in a previous study where the model also showed excellent results [3].
## 3 Proposed SQRD Algorithm with a Novel Modified RVD
### Proposed Modified RVD
The complex elements of the \(4\times 4\) channel matrix \(\mathbf{H}\) are delivered column by column as the input of norm calculator. First, the squares of the real part and imaginary part of the four elements in one column are calculated with SQ modules and then these squares are added up in pairs to obtain the norm value of every complex element; ultimately, all the norm values are added together with the SUM module to get the norm value of the current input column. In sorting#1, the norm update module is bypassed and the CS module compares these successive norm values until it finds the minimum one. Finally, the column with the smallest norm is swapped with the first column of the four stored in the column buffer. In a similar way, sorting#2 and sorting#3 perform the rest of the sorting procedures. However, what is different from sorting#1 is that, before the CS module, they will first update the norm values from the former sorting module according to line 13 in Algorithm 1. Figure 2:0 Architecture of micro-rotations of adopted CORDIC module: (**a**) micro-rotation for normal CORDIC module; (**b**) micro-rotation for time-sharing CORDIC module. Figure 2:1 Architecture of scaling of adopted CORDIC module. ## 5 Implementation Results and Comparisons

We designed the RTL models of the proposed SQRD processor with Verilog HDL and synthesized it by Synopsys Design Compiler with SMIC 55-nm COMS technology. The bit-width of the data pass of our design was set to 16 bits, including 5 bits for the integer part and 11 bits for the fractional part. The fixed-point design of the proposed SQRD was simulated in the same platform introduced in Section 2.1. The result shows that the BER performance has a negligible degradation compared to the floating-point one. Table 2 presents a summary of the implementation results and performance comparisons with related studies. The gate count of the proposed SQRD processor is 176.5 K which is greatly reduced compared to the other designs. This is due to the adopted low-complexity SQRD algorithm and time-sharing Givens rotation structure. Our design achieves a high throughput of 62.5 M SQRD/s and 250 M \(\mathbf{Q}^{H}\mathbf{y}\)/s, with an operating frequency of 250 MHz, which is better than most of the other studies. The design in reference [3] has the highest SQRD throughput with the best \(f_{\text{max}}\), albeit at the expense of exorbitant hardware cost and long processing latency. Therefore, we take hardware complexity and implementation technology into consideration and introduce normalized hardware efficiency (NHE) for a fair comparison of throughput. Table 2 clearly shows that our design is superior to all the other SQRD processors in terms of normalized hardware efficiency. In addition, we evaluate these designs in a 4 \(\times\) 4 64 QAM MIMO system and our design achieves the highest data throughput of 6 Gbps. For IEEE 802.11 ax [1], the maximum uncoded data throughput in the same scenarios with a bandwidth of 160 MHz is 2.88 Gbps. Therefore, the proposed SQRD design is able to support the IEEE 802.11 ax.
## 3 Results and Discussion  
The neurons of the hidden layer in the structure of the artificial neural networks were determined by trial and error method. Since the ultimate goal was to classify hazelnuts into three classes, the outer layer included three neurons. Network performance was determined using the rate of correct identification and the mean squared error (MSE) for network training and validation. The least mean square error for network validation was used as a base to select the number of neurons in hidden layers. Also, equation (9) was used to calculate MSE:  
\[MSE=\frac{\sum_{j=0}^{P}\sum_{i=0}^{N}\left(D_{ij}-Y_{ij}\right)^{2}}{NP} \tag{9}\]  
where  
\(Y_{ij}\) is the network output (real output) for the sample of \(i\) in neuron \(j\);  
\(D_{ij}\) is the optimal output for the sample of \(i\) in neuron \(j\);  
\(P\) is the number of neurons in the outer layer, and \(N\) is the number of samples in the training set.  
To determine the number of neurons in the middle layer for different iterations, the minimum mean squared error (MSE) was considered for the network validation. It was more stable and was selected as the optimal network for hazelnut classification.  
The number of neurons in hidden layer was changed from 10 to 30 neurons, and the optimal number of neurons was obtained through trial and error. The least error was obtained for 21 neurons in the hidden layer. The output layer consisted of three neurons to reach the desired target. To determine the number of neurons in the hidden layer, as shown in Figures 17 and 18. For different epochs and neurons, the minimum of MSE was used to validate the network. According to the results of Figures 17 and 18, the least-squares error values (MSE) were obtained for neurons 10 to 30, which resulted in better performance for the network with 21 neurons in the hidden layer.  
After network training and validation (Figure 18), the correct sorting for big, small, hollow, or damaged hazelnuts was determined as 96.1%, 89.3%, and 93.1%, respectively. Also, the overall accuracy for these hazelnuts with the 4-21-3 model was 92.8%.  
Figure 17: Mean square error curve with the increased neurons in the hidden layer.
# Unexpected-Behavior Detection Using TopK Rankings for Cybersecurity
## 2 Intrusion Detection Systems
### Signature-Based Detection  
This detection technique is the first one employed on an IDS and was introduced in [PERSON] reports [4]. The intrusion is detected by matching the behavior recorded in either log registries, network packets, or system status against well-known suspicious patterns. This methodology is very effective in detecting known attacks, but is useless for new forms of attacks [8].  
Examples of malicious behaviors and related attacks that this methodology is able to detect are described in Table 1, where we can observe that the expected behaviors are well defined. Figure 2 depicts this detection methodology in a diagram.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Attack** & **Expected Behavior** \\ \hline Unauthorized access & SSH try of a root login \\ \hline Unauthorized web access & Multiple wrong password web login forms submitted from the same IP address in a short period of time \\ \hline Malware execution & High CPU load into a server and multiple outgoing TCP connections \\ \hline Social Engineering and malware & An email with subject “Urgent Document” and an attachment filename “authorization.exe” \\ \hline \hline \end{tabular}
\end{table}
Table 1: Known attacks and their expected behavior.  
Figure 2: Diagram of a generic signature-based IDS.
## 3 Numerical Algorithm
### The Jacobian-Free Newton-Krylov Method
With regard to the second point, we also note that, despite the fact they are time implicit, our integration techniques do not use arbitrarily large time steps for accuracy reasons. Furthermore, this prevents us drifting far away from the next time level solutions. As we pointed out earlier, we utilize preconditioning techniques to accelerate the convergence of the JFNK's Krylov step. Usually, the original linear system (9) is modified when a preconditioner is in use (the Jacobian matrix is multiplied by a preconditioning matrix from the left or right). When the linear system is multiplied by a preconditioning matrix from the left, the resulting method is called the left preconditioner (or the right preconditioner if the multiplication is performed from the right). We note that the left preconditioner also modifies the right-hand side of the linear system (9), meaning that the modified nonlinear residuals must be recovered. This brings additional coding complexity and computational costs [20; 21; 42]. On the other hand, the right preconditioner does not affect the right-hand side term and does not possess the aforementioned difficulties. Therefore, we prefer the right preconditioning (RP) strategy in this paper. The RP procedure modifies and improves the linear system (9) as follows  
\[\mathbf{J}^{P-1}(P\delta c)\approx-\mathbf{F}(c), \tag{15}\]  
where \(P\) is the preconditioning matrix. The action of the right preconditioning can be defined similarly to (14), e.g.,  
\[\mathbf{J}^{P-1}\mathbf{v}\approx\frac{\mathbf{F}(c+\epsilon P^{-1}\mathbf{v} )-\mathbf{F}(c)}{\epsilon}, \tag{16}\]  
where \(\mathbf{v}\) is the Krylov vector. The execution of this procedure involves the following steps: \begin{tabular}{l l}
**Step-1:** & Given \(\mathbf{v}\), solve \(Pz=\mathbf{v}\) for \(z\) (e.g., \(z=P^{-1}\mathbf{v}\)) \\
**Step-2:** & Perform \(\mathbf{Jz}\approx(\mathbf{F}(c+\epsilon z)-\mathbf{F}(c))/\epsilon\), \\ \end{tabular}   
where \(z\) is the preconditioned Krylov vector. Below, we describe two different ways of obtaining \(z^{\prime}\)s.
## 1 Introduction
As a result, it becomes impossible to obtain precise measurements, quantify the extent of anomalies or accurately position these anomalies within a 3D reference system. For this reason, in recent years, several studies have focused on establishing a spatial connection between thermal information and 3D models derived from LiDAR techniques or digital photogrammetry, with the aim of making thermal images metrically measurable and providing the possibility of deriving 3D information from them as well. Some of the most commonly used methods include homographic transformations and single image rectification [17], the generation of thermal textures based on the projection of the TIR images onto a 3D surface model [18] or the use of digital photogrammetry with SfM (Structure from Motion) algorithms [19]. These strategies allow for the spatialization of thermal information, enhancing the effectiveness of the investigations: for this reason, based on the 3D data previously acquired, in the case of the present research, an additional survey was integrated using a thermal camera capable of capturing thermal-infrared images, thus providing non-visible information useful for analysing the presence of thermal anomalies, which can support historical research and analyses and the investigation and interpretation of the building's construction evolution. The thermal images were integrated into the previously conducted 3D metric survey through 3D co-registration strategies from a data fusion perspective, allowing these anomalies to be assessed not only qualitatively but also quantitatively for 3D metric purposes. The preliminary results of this experimentation were published in [PERSON] et al. (2022) [20], where an in-depth comparison of different 3D co-registration strategies for thermal data was proposed, as well as the potential of this type of information managed in an HBIM environment to support historical analyses.
###### Abstract  
The assembly process is sometimes blocked due to excessive dimension deviations during large-scale assembly. It is inefficient to improve the assembly quality by trial assembly, inspection, and accuracy compensation in the case of excessive deviations. Therefore, assemblability prediction by analyzing the measurement data, assembly accuracy requirements, and the pose of parts is an effective way to discover the assembly deviations in advance for measurement-assisted assembly. In this paper, a coordination space model is constructed based on a small displacement torsor and assembly accuracy requirements. An assemblability analysis method is proposed to check whether the assembly can be executed directly. Aiming at the incoordination problem, an assemblability optimization method based on the union coordination space is proposed. Finally, taking the space manipulator assembly as an example, the result shows that the proposed method can improve assemblability with a better assembly quality and less workload compared to the least-squares method.  
measurement-assisted assembly; coordination space; assemblability; small displacement torsor +
Footnote †: journal: Applied Sciences
## Materials and Methods
### Nvidia Jetson TX2 and Nvidia Digits  
We chose NVIDIA Jetson TX2 as our hardware platform because one of our primary goals was to process the data locally and in real time so that it can be used to classify retina images without the need for an Internet connection [20]. The features provided by the NVIDIA Jetson enables producing classification results in a reasonable timeframe for a practical application and allows for future on-device retraining and improvement of the model. This is important, as our aim is to enable use of the device at remote locations where an Internet connection may not be available or reliable. However, it must be noted that it is possible to implement the presented model on a microcontroller and CPU-based hardware platform. The performance and efficiency of Jetson are equivalent to an Intel Xeon E5. In a direct comparison of Jetson with Intel Xeon Server in running a deep learning inference model based on a GoogleNet deep learning image recognition network, Jetson was able to process 290 images per second compared to 231 by the Intel Xeon Server with a 128 batch size [35]. With a few modifications, and following the instruction provided by NVIDIA, we installed, set up, and ran an NVIDIA deep learning GPU training system (DIGITS), including installing Nvidia drivers, installing Docker, and setting up and starting a DIGITS Container [36]. NVIDIA DIGITS is an open-source software provided to design, train, and visualize deep neural networks for image classification, segmentation, and object detection using Caffe, Torch, and TensorFlow. Docker provides an easy tool to create, deploy, and run applications using containers [37]. Caffe is a deep learning framework that provides processing speed and expressive architecture for developing machine learning models.  
Once the setup was complete, the training data were imported to DIGITS, classification was selected based on desired output labels of healthy or unhealthy. Lightning memory-mapped database (LMDB), a memory-mapped file, was chosen as a database backend for its faster input/output performance over a large dataset. Next, an inference model was created using the solver options shown in Table 3 below. Once the model is trained, it outputs epochs and accuracy. The trained model can be tested with testing images.
## 2 Materials and Methods
### Measurement Equipment and Data Analysis  
Height (Radiometer Seca 202, Seca Ltd., Hamburg, Germany) and body mass (Seca Scale, Seca Ltd., Hamburg, Germany) were evaluated upon arrival at the testing facility.  
Figure 1: Multidirectional landing setup for the dominant leg (i.e., right leg (RL)). The grey box represents the force plate. RLLI, right leg lateral inside; RLDI, right leg diagonal inside; RLSJ, right leg straight jump; RLDO, right leg diagonal outside; RILO, right leg lateral outside. All directions were repeated on the non-dominant leg.  
Cycling-based warmups were completed on a stationary cycling ergometer (WattbikePro, Technogym, Fairfield, NJ, USA). All concentric and eccentric knee torques were evaluated on an isokinetic dynamometer (Cybex II, HUMAC(r)/Norm(tm); Computer Sports Medicine, Inc., Stoughton, MA, USA), and the raw data were exported to MATLAB for further analysis (R2022a, Mathworks(tm), Natick, MA, USA). Custom MATLAB code separated extension and flexion torques, normalised the torques to the relative movement time as well as body mass, calculated the mean torques across all repetitions, and extracted the peak torque values relative to the normalised duration.  
The landing forces were recorded on an AMTI force plate (AMTI, Watertown, MA, USA) at 2000 Hz with all force data files exported to MATLAB for further analysis. Custom MATLAB code was written to (i) normalise the landing duration from the instant of landing (defined as the point at which vertical ground reaction force (GRF) first exceeds 30 N) until the end of the recording (i.e., 8 s), (ii) normalise the landing force relative to body mass, (iii) extract the peak normalised landing force, (iv) ascertain the time to stabilisation (TTS) upon landing after which the force trace remained within a an average range of variation of body weight (mean + 5 SD), and (v) derive the centre of mass (CoM) displacement using inverse kinetics to contextualise the eccentric and concentric landing phases [14; 19; 31; 32; 33].
## 3 Methodology
### Evaluation Models and Metrics  
We compared the proposed Wet-ConViT model's performance with multiple state-of-the-art deep learning models, including convolutional, transformer, and hybrid architectures, in order to evaluate its potential for classifying wetland areas. These models include ResNet50 [55] and EfficientNet-V2 [56] from the CNN category, ViT [42] and Swin-v2 [44] from the transformers, and CvT [57], CoAtNet [58], and WetMapFormer (WMF) [19] from the hybrid category. To ensure a more effective comparison with the proposed model, we deliberately opted for the smallest version of each model among their various existing variants. This approach allows for a closer match in terms of model parameters and processing time when compared to the proposed model. As such, we considered the small version of EfficientNet-V2, the base version of ViT, the tiny version of Swin-V2, CvT-13, and CoAtNet-0.  
Next, the efficiency of the deep learning models is examined in terms of both accuracy metrics and computational complexity. Model complexity is a crucial consideration, particularly in large-scale applications. Various factors serve as indicators of model complexity, with key metrics being the number of parameters (Params) and training time. Params represent the number of trainable parameters involved in the training process.  
The per-class accuracy of the models is assessed using the F1-Score (F1), along with three overall metrics: overall accuracy (OA), average accuracy (AA), and the kappa coefficient (k), to evaluate the performance of the models in wetland classification. The per-class F1 can be calculated based on precision (P), recall (R), and confusion matrix elements, as follows:  
\[\text{P}=\text{TP}/\text{TP}+\text{FP} \tag{13}\]  
\[\text{R}=\text{TP}/\text{TP}+\text{FN} \tag{14}\]  
\[\text{F1-score}=2\times(\text{P}\times\text{R})/(\text{P}+\text{R}) \tag{15}\]  
True positive (TP) is the number of correctly classified samples for a specific land cover class, whereas false positive (FP) and false negative (FN) signify the number of samples incorrectly predicted as a particular category (positive) and as different categories (negative), respectively. These metrics can be defined using the following equations:  
\[\text{OA}=\text{N}_{\text{Correct}}/\text{N}_{\text{Total}} \tag{16}\]  
\[\text{AA}=(\sum_{i=1}^{\text{C}}\text{R}_{i})/\text{C} \tag{17}\]  
\[\text{k}=(\text{OA}-\text{P}_{\text{e}})/(1-\text{P}_{\text{e}});\text{ where }\text{P}_{\text{e}}=(\sum_{i=1}^{\text{C}}(\text{N}^{i}_{\text{True}}+\text{N}^{i} _{\text{Predict}}))/\text{N}_{\text{Total}^{2}} \tag{18}\]  
where C represents the total number of classes, and R\({}_{i}\) donates the recall (R) of the \(\text{i}^{\text{th}}\) class. \(\text{N}^{i}_{\text{True}}\) and \(\text{N}^{i}_{\text{Predict}}\) stand for the number of true samples (TP + FN) and correctly predicted samples (TP + TN) within class i, respectively. \(\text{N}_{\text{Correct}}\) and \(\text{N}_{\text{Total}}\) represent the number of correct predictions and total number of samples.