esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time, Model-Agnostic and User-Driven Counterfactual Explanations Using Autoencoders
## 1 Introduction
* **Data manifold closeness:** In addition to satisfying the property of data closeness, a counterfactual instance has to be close to the distribution of the data (\(p_{data}\)), i.e., the changes have to be realistic, i.e., \[\mathbf{x}_{cf}\sim p_{data}.\] (3)
* **Agnosticity:** The generation of counterfactuals can be applied to any machine learning model without relying on any prior knowledge or assumptions about the model. * **Black-box access**: The generation of counterfactuals can be achieved with access to only the predict function of the black-box model. In order to fulfill all these properties, it is necessary to design an algorithm that optimizes various aspects in the loss function. This loss function should penalize large changes, out-of-distribution counterfactuals, and counterfactual explanations that do not match the counterfactual label defined by the user. Thus, in this paper, we propose Real-Time Guided Counterfactual Explanations (RTGCEx), a model-agnostic algorithm that is capable of creating counterfactual explanations in real time through the use of autoencoders, by optimizing a loss function that takes into account all these properties. The paper is organized as follows: In Section 2, we review the related works in the field. In Section 3, we present the proposed algorithm in detail. Section 4 describes the experimental framework used to evaluate RTGCEx. In Section 5, we present and analyze the results obtained from the experiments. Finally, in Section 6, we summarize the main contributions of this work and discuss directions for future research.
# Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics
## 2 Flow Field Signal Acquisition
### Phase Space Reconstruction  
A virtual lateral line was set in the flow field, with 21 monitoring points. A weighting fusion algorithm was used to couple the 21-series signal, as described in our previous work [47] and shown in Equation (4).  
\[\left\{\begin{array}{l}K_{i}=S_{i}+A_{i}\\ S_{i}=\frac{\varepsilon\sin{a_{i}}}{\sum\limits_{i=0}^{N}{\alpha_{i}}}\\ A_{i}=\frac{(1-\epsilon)A_{m}(s_{i})}{\sum\limits_{i=0}^{N}{A_{m}(s_{i})}}\\ c_{p}(t)=\sum\limits_{i=0}^{N}{K_{i}S_{i}(t)}\end{array}\right. \tag{4}\]  
where \(\varepsilon\) is the weight of position coefficient \(S_{i}\). \(A_{m}(s_{i})\) is the amplitude of signal collected by sensor \(i\). \(c_{p}(t)\) is the coupling signal series at the time of \(t\).  
In phase space reconstruction of the signal time series, the selection of delay time and embedding dimension was carried out by the C-C method, as shown in Equation (5). The first local minimum value times of \(\Delta S(m,t)\) was considered the optimal delay time \(\tau\). The minimum value of \(S_{cor}(t)\) can be seen as the delay time window \(\Gamma\), which has a connection with the embedding dimension \(m\), as shown in Equation (5).  
\[\left\{\begin{array}{l}\overline{S}(t)=\frac{1}{16}\sum\limits_{m=2}^{5}{ \sum\limits_{j=1}^{4}{S(m,r_{j},t)}};\\ \Delta\overline{S}(t)=\frac{1}{4}\sum\limits_{m=2}^{5}{\Delta S(m,t)};\\ S_{cor}(t)=\Delta\overline{S}(t)+\left|\Delta\overline{S}(t)\right|;\\ S(m,r_{j},t)=\frac{1}{\Gamma}\sum\limits_{s=1}^{t}{\left[{C_{s}(m,r_{j},t)-C_ {s}^{m}(1,r_{j},t)}\right]};\\ \Delta S(m,t)=\max\bigl{\{}S(m,r_{j},t)\bigr{\}}-\min\bigl{\{}S(m,r_{j},t) \bigr{\}};\\ \Gamma=(m-1)\cdot\tau\end{array}\right. \tag{5}\]  
Figure 5: Comparison of calculation and PIV experiment results.  
The final signal time series at high phase space \(X_{n}(t_{i})\) and the correlation integral \(C(m,N,r,t)\) are shown in Equations (6) and (7). The pressure signal time series around a square target was defined as the original signal, as shown in Figure 6.  
\[X_{n}(t_{i})=[x_{n}(t_{i}),x_{n}(t_{i}+\tau),x_{n}(t_{i}+2\tau),\ldots,x_{n}(t_{ i}+(m-1)\tau],i=1,2,\ldots,N-(m-1)\tau \tag{6}\]  
\[\left\{\begin{array}{l}C(m,N,r,t)=\frac{2}{M(M-1)}\sum\limits_{1\leq i\leq j \leq M}\theta(r-d_{ij}),r>0\\ d_{ij}=\left|X_{n}(t_{i})-X_{n}(t_{j})\right|\end{array}\right. \tag{7}\]  
where \(N\) is the length of the signal series, \(r\) is the radius of the time series, \(\sigma\) is the time-series standard deviation, \(\theta(x)\) is shown in Equation (8).  
\[\theta(x)=\left\{\begin{array}{l}0,x<0\\ 1,x>0\end{array}\right. \tag{8}\]
###### Abstract
The four-element-based mushroom-shaped design provides up to \(-40\) dB of isolation over a frequency range of 2.3-2.4 GHz. The complex design and vast size are partly due to the mushroom's multi-layered structure. The broadband four-elements-based MIMO antenna is presented in [18; 19]. The inverted L-monopole structure is used to attain the broad bandwidth. Nevertheless, only \(-11\) dB of isolation can be accomplished throughout the operational bandwidth. The partial diffracted ground and diagonally trancing patch help to attain sub-6 GHz communication applications [20]. The insertion of stub in the resonators helps with improving isolation [21]. The transparent MIMO antenna using transparent conductive oxide and plexiglass substrate is presented in article [22]. The machine-learning-enhanced MIMO antenna allows for more precise aiming across a wider variety of use cases [23]. The current trend uses graphene-based THz MIMO antennas; however, fabrication technology needs to be explored further. There is a need for high-gain, low-cost, multiband antenna to be designed, which can operate over a wide range with multiple bands. This manuscript presents the 1 \(\times\) 2 MIMO antenna, which is analyzed for the L, S, C, and X frequency bands. The main objective of the proposed work is to achieve the multiband response with healthy gain and sufficient wideband response to target multiple applications for S, C, X, and Ka bands. The presented manuscript represents the novel concept of the addition of complementary split rings and slots in the patch area and the ground region. The modification in the patch structure and ground structure helps to achieve the targeted goals. The presented work shows the path to achieving a broad bandwidth, lower E.C.C., and good gain using the low-profile substrate material, which helps to achieve satellite communication applications.
## 3 Results  
The male group was composed of 19 players (age = 19.2 \(\pm\) 0.5 years, height = 179.7 \(\pm\) 5.3 cm, weight = 71.0 \(\pm\) 5.9 kg), and the female group had 8 players (age = 27.3 \(\pm\) 6.5 years, height = 161.0 \(\pm\) 0.6 cm, weight = 56.7 \(\pm\) 4.9 kg). Differences were found between groups in age (t\({}_{25}\) = 5.55; \(p\)! 0.001; d = 2.33), weight (t\({}_{25}\) = 6.02; \(p\)! 0.001; d = 2.54), and height (t\({}_{25}\) = 9.84; \(p\)! 0.001; d = 4.15). The statistical analysis of the H:Q ratio showed no significant differences (Table 1) between the female and male soccer players in both the lunge and Bulgarian squat exercises (\(p\)! 0.05). Significant differences were found in the VM:VL ratio (Table 2) for both the lunge, exercise (t\({}_{20}\) = 3.35; \(p\) = 0.001; d = 1.42) and the Bulgarian squat (t\({}_{23}\) = 4.15; \(p\)! 0.001; d = 1.76). For the intragroup muscular pattern in the lunge exercise (Figure 2), the female group showed higher activation for the VL muscle (t\({}_{23}\) = 4.1; \(p\)! 0.001; d = 1.75) than the male group and lower muscle activation in the VM (Table 3) (t\({}_{23}\) = \(-\)3.8; \(p\) = 0.001; d = 1.62) compared to the male group. No significant differences were found in the RF, BF, and ST muscles (\(p\)! 0.05). Similarly, in the Bulgarian squat exercise (Table 3 and Figure 3), the females also showed higher VL activation (t\({}_{23}\) = 3; \(p\) = 0.006; d = 1.29) and lower muscle activation in the VM (t\({}_{23}\) = \(-\)3.9; \(p\) = 0.001; d = 1.67). In the same way, no significant differences were found in the RF, BF, and ST muscles (\(p\)! 0.05). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Female Players**} & \multicolumn{2}{c}{**Male Players**} & & \\ \cline{2-6}  & **Mean** & **SD** & **Mean** & **SD** & **Significance** & **Effect Size (d)** \\ \hline H:Q ratio in the lunge & & & & & & \\ exercise & 0.25 & 0.18 & 0.18 & 0.10 & \(p\)! 0.05 & - \\ H:Q ratio in the Bulgarian & & & & & & \\ squat exercise & 0.24 & 0.16 & 0.18 & 0.06 & \(p\)! 0.05 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hamstring: quadriceps (H:Q) ratio (mean \(\pm\) SD). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Female Players**} & \multicolumn{2}{c}{**Male Players**} & & \\ \cline{2-6}  & **Mean** & **SD** & **Mean** & **SD** & **Significance** & **Effect Size (d)** \\ \hline VM:VL ratio in the lunge & & & & & & \\ exercise & 1.12 & 0.36 & 2.64 & 1.88 & \(p\) = 0.001 & 1.42 \\ VM:VL ratio in the Bulgarian squat exercise & 1.10 & 1.88 & 2.04 & 0.72 & \(p\)! 0.001 & 1.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: VM:VL ratio (mean \(\pm\) SD). Figure 3: Intragroup muscular pattern (mean \(\pm\) SD) in percentage. The intragroup muscular pattern expresses the activation of each muscle belly in relation to the total muscle (quadriceps and hamstrings). The asterisk (*) shows that there is a significant difference.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 4 Results
### Selecting a Time-Frame of Sentinel-1 Scenes  
One goal of the study was to examine the minimum number of the scenes and the shortest time after the damage to achieve a feasible uncertainty level in windstorm detection. For this purpose: (a) only images until 20 August were used (19 images instead of the original 27 image layers); and (b) only images until 14 August were used, further limiting the number of images to 17. We also studied the importance of the scenes before the damage, that is, the achieved accuracies when some of the images acquired before the damage were left out of analysis. It turned out that 10 scenes gave almost as large accuracies as the entire set of the scenes from 4 January to 20 August 2017. The accuracies were thus calculated using the following numbers of the images: 27 (all scenes), 19 (all scenes until 20 August), 17 (all scenes until 14 August), the most important scenes until 20 August (10 scenes) and the most important scenes until 14 August (8 scenes) (see Section 4.4). Recall that the damage occurred on 12 August.  
The importance of the Sentinel-1 scenes, acquired before the damage, was studied with all three methods. The date of these 10 scenes were: 16 January, 28 January, 9 February, 2 July, 14 July, 21 July, 26 July, 2 August, 19 August and 20 August. The dates of all available scenes in the period 4 January-20 August, in total 19 scenes, were 4 January, 16 January, 28 January, 9 February, 21 February, 2 July, 3 July, 8 July, 9 July, 14 July, 15 July, 21 July, 26 July, 2 August, 7 August, 8 August, 14 August, 19 August and 20 August (Tables 4 and 7).  
When using all Sentinel-1 scenes from the period 4 January-14 August, as well as adding scenes cumulatively after 14 August, it was noticed that the overall accuracy (OA) increased only slightly. The maximum accuracy was obtained with the training and validation data with the scenes until either 12, 13 or 18 September, depending on the method. The OA in the validation data was 0.79 with SVM, 0.73 with ik-NN and 0.71 with MLR (Figure 8 and Table 7).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Intensity** & \multicolumn{2}{c}{**OA**} & \multicolumn{3}{c}{**UA**} & \multicolumn{3}{c}{**PA**} \\ \hline method & Training & Test & 1 1  & 2 2  & 3  & 1  & 2  \\ \hline Average & 1.0 & 0.771 & 0.805 & 0.619 & 0.750 & 0.913 & 0.531 & 0.250 \\ Median & 1.0 & 0.747 & 0.840 & 0.511 & 0.412 & 0.913 & 0.531 & 0.250 \\ Quantiles & 1.0 & 0.763 & 0.788 & 0.647 & 0.625 & 0.930 & 0.449 & 0.208 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The overall accuracies (OA) in training and validation data and user’s accuracies (UA) and producer’s accuracies (PA) by damage by categories in the validation data when using 19 Sentinel-1 cover (1 January 2017–20 August 2017) and the SVM classifier. A volume threshold of 75 m\({}^{3}\)/ha is used in the data.
## 4 Methodology
### Participants
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **Mean** & **SD** & **Mean** & **SD** & **Difference** \\  & **F.** & **F.** & **L.** & **L.** & **F-L** & & **F.** & **F.** & **L.** & **L.** & **F-L** \\ \hline B1 & 1.31 & 0.60 & 1.61 & 0.66 & \(-\)0.30 & D1 & 1.37 & 0.71 & 1.56 & 0.72 & \(-\)0.19 \\ B2 & 1.31 & 0.79 & 1.76 & 0.93 & \(-\)0.48 & D2 & 1.18 & 0.40 & 1.48 & 0.68 & \(-\)030 \\ B3 & 2.25 & 0.68 & 1.68 & 0.83 & 0.57 & D3 & 1.18 & 0.40 & 1.60 & 0.71 & \(-\)0.42 \\ B4 & 1.50 & 0.51 & 1.71 & 0.74 & \(-\)0.21 & D4 & 2.12 & 0.71 & 1.60 & 0.69 & 0.52 \\ B5 & 1.50 & 0.81 & 1.77 & 0.76 & \(-\)0.26 & D5 & 2.25 & 1.06 & 1.67 & 0.79 & 0.58 \\ B6 & 1.37 & 0.61 & 1.74 & 0.71 & \(-\)0.37 & D6 & 1.62 & 0.80 & 1.60 & 0.70 & 0.02 \\ B7 & 1.81 & 0.91 & 1.67 & 0.70 & 0.14 & D7 & 2.43 & 0.96 & 1.72 & 0.73 & 0.71 \\ B8 & 1.93 & 0.44 & 1.78 & 0.82 & 0.15 & D8 & 1.12 & 0.34 & 1.56 & 0.63 & \(-\)0.44 \\ B9 & 2.06 & 0.44 & 1.75 & 0.79 & 0.31 & D9 & 2.12 & 0.95 & 1.61 & 0.69 & 0.51 \\ B10 & 1.43 & 0.62 & 1.74 & 0.74 & \(-\)0.31 & E1 & 2.00 & 0.63 & 1.56 & 0.71 & 0.44 \\ C1 & 1.43 & 0.51 & 1.52 & 0.65 & \(-\)0.09 & E2 & 1.37 & 0.50 & 1.55 & 0.69 & \(-\)0.18 \\ C2 & 1.12 & 0.34 & 1.57 & 0.68 & \(-\)0.45 & E3 & 1.44 & 0.72 & 1.81 & 0.71 & \(-\)0.37 \\ C3 & 2.18 & 0.75 & 1.71 & 0.73 & 0.47 & E4 & 1.81 & 0.54 & 1.75 & 0.75 & 0.06 \\ C4 & 1.75 & 0.85 & 1.68 & 0.68 & 0.07 & E5 & 1.88 & 0.80 & 1.64 & 0.79 & 0.24 \\ C5 & 2.00 & 1.09 & 1.65 & 0.67 & 0.35 & & & & & & \\ C6 & 1.81 & 0.65 & 1.62 & 0.75 & 0.19 & & & & & & \\ C7 & 1.75 & 0.57 & 1.60 & 0.61 & 0.15 & & & & & & \\ C8 & 2.18 & 0.75 & 1.77 & 0.84 & 0.41 & & & & & & \\ C9 & 2.37 & 0.95 & 1.66 & 0.69 & 0.71 & & & & & & \\ \hline \hline \end{tabular} \(N=151\).  
\end{table} 
Table 4: Faculty (F) and learners (L.) Means and standard deviation. online work), with faculty mean rank higher (97.41) than students (73.46), \(p=0.026\); and C9 (progress in relation to the learning goals), where faculty's mean rank was higher (104.47) compared to students (72.63), \(p=0.003\). For Construct 3, findings showed significant differences in items: D3 (improving the organization of ideas), with faculty's mean rank (54.34) being lower than students (78.57), \(p=0.019\); D5 (improving grammar and vocabulary), where faculty had a higher mean rank (97.41) than students (73.46), \(p=0.023\); D7 (review rubrics to further improve writing), with a higher faculty mean rank (104.84) compared to students (72.58), \(p=0.003\); D8 (use instructor feedback to improve work), where faculty had a lower mean rank (50.81) than students (78.99), \(p=0.006\); and D9 (progress in relation to learning goals), where faculty's mean rank was higher (96.59) than students (73.56), \(p=0.008\). For Construct 4, significant differences were observed for E2 (self-assessment before submission), where faculty had a lower mean rank (68.56) compared to students (76.88), \(p=0.030\), and E3 (using feedback to resubmit improved work), where faculty had a lower mean rank (55.59) than students (78.42), \(p=0.008\).
## 5 In Vitro Antimicrobial Susceptibility Pattern of the _B. burgdorferii_ Complex
### Fluorquinolones  
The use of fluoroquinolones is, in part, well-established for a variety of soft tissue infections [92]. Due to the limited in vitro activity of the initial quinolones against borreliae, in particular malidixic acid and perfloxacin, fluoroquinolones are not recommended as drugs of choice for the treatment of LB. Some authors report that borreliae shows general resistance to these drugs [93; 94]. Other investigators, however, report some in vitro activity [40; 58; 95; 96; 97]. Molecular studies on borreliae clearly indicate the presence of a target structure for the quinolones, i.e., a functional DNA gyrase consisting of full-length GyrA and GyrB subunits, which is required for borreliae to grow [98]. Investigations on the activity of 15 fluoroquinolones against human pathogenic isolates of the _B. burgdorferi_ s.l. complex and against other previously designated genospecies demonstrated enhanced in vitro activity against borreliae with some of the recently developed antimicrobial agents [58]. Interestingly, the range of MIC values is clearly class dependent, as class I and II compounds, like nonfloxacin and ciprofloxacin, generally had higher MIC\({}_{50}\) and MIC\({}_{90}\) values than did class III and IV compounds, such as sparfloxacin and gemifloxacin (Table 2). The rank order of potency on a \(\upmu\)g/mL basis for the quinolones with enhanced in vitro activity against _B. burgdorferi_ s.l. is gemifloxacin > sitafloxacin > grepafloxacin > gatifloxacin, clinafloxacin, trovafloxacin, and sparfloxacin [58].  
Figure 4: **(A–D)**: Thin-section electron micrographs of _B. garitiii_ isolate P$th. **(A)**: Control sample at lower magnification without the addition of antibiotics revealing the cell shape and the normal morphology of untreated cells. **(B)**: Cross-section of untreated cell at higher magnification showing ultrastructural details of this organism: CM, cytoplasmic membrane; F, flagella; OM, outer-membrane; P, protoplasmic cylinder; SL, surface layer **(C)**: Spirochetes exposed to 0.03 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro \SIUnitSymbol  
The higher susceptibilities of _B. burgdorferi_ s.l. to class III and IV fluoroquinolones, such as sparfloxacin and gemifloxacin, (Table 2) which are derivatives exhibiting enhanced activity against Gram-positives and anaerobes, indicate that the in vitro susceptibility of borreliae probably does not resemble that of common Gram-negative bacteria [42; 52; 58; 81]. The naturally occurring DNA-gyrase of _B. burgdorferi_ s.l. and the homologous C-terminal domain of _Escherichia coli_ GyrA are biochemically distinct, sharing only 24% identity at the amino acid level [98]. The GyrA C-terminal domain of _E. coli_ is acidic with a predicted isoelectric point of 4.0, whereas the naturally occurring 34 kDa protein in _B. burgdorferi_ s.l. is basic, with a predicted isoelectric point of 9.1 [98]. These differences may explain the lower activity of class I and II quinolones against borreliae in comparison to common Gram-negative bacteria, such as _E. coli_.
## 1 Introduction
### Main Contributions  
Due to the sensitivity of the renewable-energy data and the negatives consequences of the energy optimization utility being accessed by a malicious user, this sector must not depend on only one authority for approving access to cloud-hosted resources. In other words, it is better to distribute the access decision among multiple authorities. This paper generates an effective token to validate the access request for a specific user usingmultiple authorities distributed in various geographical locations. The proposed token was implemented in a complete token-revocation access-control system. The main contributions are as follows:  
* The proposed scheme has a pool of dynamically changing revocation thresholds. Then, If the malicious user was able to pass through some stages of the access-control scheme, it will definitely be revoked in one of the later stages.
* The proposed system was proven to be secure against any attacker, based on the Diffie-Hellman assumption. In addition, the time elapsed in evaluating each user until either granting or denying access is very short in comparison with previously introduced access-control systems.
* The encryption and decryption times for the proposed system were compared with LW [10], LCHWY [11], MAACS [12], and MD-AC [13].
## 4 Experimental Results
### Visualization of Model Decisions
Future work can explore refining its detection sensitivity for more advanced manipulations, such as those generated by diffusion-based models. * ResNet-50: The high FRA and lower MDR suggest that ResNet-50 requires additional architectural adjustments or more focused training to enhance attention on critical regions. * VGG16: Its balanced attention coverage and intensity confirm its reliability for forensic analysis. However, optimizing its computational efficiency would expand its applicability to resource-constrained scenarios. By quantifying Grad-CAM activations, this analysis provides a comprehensive view of the models' strengths and weaknesses. The metrics reflect their capacity to localize manipulation artifacts and offer actionable insights for improving deepfake detection frameworks.
## 2 Materials and Methods
### Chemical Analysis of Plant Material
#### 2.2.3 HPLC Conditions  
HPLC (high-performance liquid chromatography) analysis was performed using an Agilent 1100 Series HPLC system (Agilent Technologies, Santa Clara, CA, USA). A C\({}_{18}\) reversed-phase packing column (YMC Triart C\({}_{18}\), 150 mm \(\times\) 4.6 mm, 5 \(\upmu\)m; Agilent Technologies, Santa Clara, CA, USA) was used for separation at a temperature of 25 \({}^{\circ}\)C. Gradient elution chromatographic systems were used. The mobile phase for gradient elution consisted of solvent A (acetonitrile) and solvent B (5 mM CH\({}_{3}\)COOH in water), which were applied according to the following program: start with 25% A and 75% B for 10 min, followed by a gradient from 25% A to 100% A over 20 min, then to 250% A over 10 min. In the next 5 min, the initial conditions were set up. The injection volume of standards and plant samples was 20 \(\upmu\)L. The flow rate was 1.0 mL/min. Detection was performed by a diode array detector at wavelengths of 280 nm for naringin, catechin, and hydroxyl coumarin; 320 nm for caffeic acid, ferulic acid, and coumaric acid; 370 nm for quercetin and rutin; and 254 nm for quercetin.  
Identification of particular compounds was achieved by comparing the retention times of the standards and unknown peaks in the samples. To avoid misinterpretation of results, the method of standard addition was applied. Quantification was conducted using external standards.