esa-sceva/satcom-chunk-collection huggingface dataset:
# Difficulties and Challenges of Anomaly Detection in Smart Cities: A Laboratory Analysis
## 5 Anomaly Detection
### Attack Detection with Machine Learning
#### 5.2.1 Dataset Description  
Before training the machine learning models, the data gathered from the WSNs have been aggregated by network in 30 min intervals. Other time intervals are also possible, but we have considered 30 min is a reasonable trade-off between setting shorter intervals, which overly reduce the variability of the resulting data, and longer time intervals that reduce the sampling set. Table 2 shows a summary of the new variables extracted from each network. As shown in the table, the values of the sensor readings have been ignored, since the implemented attacks have a denial-of-service effect and, therefore, the values recorded by the sensors have not been altered.  
Afterwards, the data obtained from the previous aggregation process have been divided into several dataset partitions, as Figure 8 shows. These partitions have been used to analyse our data using two different approaches: (1) all variables from all the networks together are used to generate a single model; (2) the variables from the different networks are used separately to generate different models.  
For the first approach, we have created five datasets: a training dataset (_tr_all_), a validation dataset (_va_all_) and a test dataset for each type of attack (_te_all_jam_, _te_all_sf50_, _te_all_sf75_). On the other hand, for the second approach, we have created 20 datasets. A training dataset for each of the four networks (_tr_z1_, _tr_wp_, _tr_rpi_, _tr_sck_), a validation dataset for each of the four networks too (_va_z1_, _va_wp_, _va_rpi_, _va_sck_) and a test dataset for each of the three attacks and each network (_te_z1_jam_, _te_wp_jam_, _te_sck_jam_, _te_z1_sf50_, _te_wp_sf50_, _te_rpi_sf50_, _te_sck_sf50_, _te_z1_sf75_, _te_wp_sf75_, _te_  
\begin{table}
\begin{tabular}{l} \hline \hline
**Zolertia Network** \\ \hline  • Amount of lost packets \\ • Amount of received packets \\ • Average time between consecutive packets \\ • Number of times that the channel was occupied before transmitting \\ \hline
**Libelium network** \\ \hline  • Amount of lost packets \\ • Amount of received packets \\ • Average time between consecutive packets \\ • Average percentage of battery consumption \\ \hline
**RPI network** \\ \hline  • Amount of lost packets \\ • Amount of received packets \\ • Average time between consecutive packets \\ • Signal level \\ \hline
**Smart citizen kit (SCK)** \\ \hline  • Amount of lost packets \\ • Amount of received packets \\ • Average time between consecutive packets \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the variables resulting from our aggregation. These variables are the result of aggregating data from all nodes in each network in 30 min intervals.
# Biomass and Cellulose Dissolution--The Important Issue in Renewable Materials Treatment
## 3 Dissolution of Biomass and Its Components
### Ionic Liquids
#### 3.6.2 Cellulose Dissolution in Ionic Liquids  
The first patents on cellulose dissolution in melted ammonium and pyridinium salts were published in 1933-1934, but the application of 1-butyl-3-methylimidazolium chloride ([bmim]Cl) in 2002 began the intensive development in the field of ionic liquid applications.  
The ability of ionic liquids to dissolve cellulose depends not only on the polymer characteristics, e.g., the degree of polymerization, crystallinity, type of plant, and process conditions (temperature, time, and presence of additional solvents), but also on ionic liquids properties [95, 96, 97, 98]. Table 3 presents examples of cellulose solubility in ionic liquids.  
Figure 10: The mechanism of cellulose dissolution in ionic liquid.
# Enhancing Robustness of Per-Packet Load-Balancing for Fat-Tree
## 2 Background and Motivation
### Per-Packet Load Balancing and Packet Reordering  
Packet reordering can affect performance of TCP. Since fast retransmit of TCP degrades performance severely in multipath routing environments where out-of-order packets are produced persistently, packet-level scheduling schemes usually entail measures to handle out-of-order packets. DeTail [8], a cross-layer network stack, constructs a lossless fabric in the link layer. When the queue fills up beyond a threshold, flow control messages are propagated back to quench the source. As a consequence, packets are seldom lost due to congestion, so DeTail simply disables fast retransmit and fast recovery in TCP. Out-of-order packets are reordered at the end-hosts. In Fastpass [10], a centralized arbiter assigns a timeslot and a path to each packet with the aim of achieving nearly zero queues. The arbiter requires complete knowledge of every host's traffic demand in the network and communicates with end-hosts for each request for packet scheduling. Presto [11] mitigates packet reordering by balancing traffic load in units of flowcell whose size is set to the maximum TCP Segment Offload size (64 KB). When out-of-order packets are to be reordered at the end-hosts, the resequencing delay must be short.
## 5 Conclusions  
This paper aims at the numerical analysis of the influence of arch loading angle (which varies from 5-45\({}^{\circ}\)) on the success of the Brazilian Test for the concrete and mortar specimens tested by [PERSON] et al. [8]. The position of initial cracks of the Brazilian disk has been thoroughly investigated since different numerical results have been acquired.  
The main findings from the numerical analysis conducted by the FLAC\({}^{\text{2D}}\) code are the following:  
* It has been confirmed that the Splitting Tension Test (or Brazilian test) makes it possible to impose a practically homogeneous tensile stress field in the central zone of the disk in 0.8H and a compression in 0.1H on both sides.
* The numerical ratio \(\sigma_{c}^{G}/\sigma_{t}^{G}\) between the tensile and compressive stress at the specimen center for all simulated concretes and mortars respects the Griffith failure criterion if the test is successful.
* The initiation of cracks starts from the center of the disks of concrete if the loading angle is between 20-45\({}^{\circ}\), thus allowing the validity of the test. Nevertheless, the tensile strength values for 2\(\alpha\), which varies from 5-15\({}^{\circ}\), were insignificant (unsuccessful test).
* For the mortar disks, the crack initiation starts from the loading points (tension failure) but spreads rapidly towards the center, thus giving significant tensile strength value in the case of 2\(\alpha\) = 10\({}^{\circ}\) loading angle. The validity of the test is guaranteed if the angle 10\({}^{\circ}\leq 2\alpha\leq 45^{\circ}\).
* It is confirmed that the possibility of compressive failure near the specimen's loaded points is reduced considerably if the loading angle is increased. Consequently, the possibility of a central crack initiating will increase, thus giving a validated and a successful test [31].  
Conceptualization, M.F.B.; methodology, M.F.B and M.B.; software, M.F.B.; validation, M.F.B.; formal analysis, M.F.B.; investigation, M.F.B. and M.B.; resources, M.F.B.; data curation, M.F.B.; writing-original draft, M.F.B.; preparation, M.F.B.; writing-review and editing, M.F.B. and M.B.; visualization, M.F.B.; supervision, M.F.B.; project administration, M.F.B.; funding acquisition, M.F.B. All authors have read and agreed to the published version of the manuscript.  
Figure 8: Distribution of horizontal and vertical stress through the loading diameter in the disk tested by [8] in the case of 2\(\alpha\) = 15–40\({}^{\circ}\) (compression\(-\), tension +).
# Simple Compact UWB Vivaldi Antenna Arrays for Breast Cancer Detection
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{**cr**} & \multirow{2}{*}{**S\({}_{12}\) (dB) at Freq Band** (GHz), BW (GHz)**} & \multirow{2}{*}{**Gain (dBi)**} & \multirow{2}{*}{**Antenna, Feeding**} & \multirow{2}{*}{**Volume**} \\  & & & & & **mm \(\times\) mm, mm** \\ \hline \multirow{2}{*}{Array 1} & \multirow{2}{*}{3.55} & \(<\)–10.28, & \multirow{2}{*}{3.36–8.61} & \multirow{2}{*}{1 \(\times\) 2 VTSA array, NTL WPD} & \multirow{2}{*}{135 \(\times\) 56 \(\times\) 0.813} \\  & & 2.42–11.52, 9.1 & & & \\ \hline \multirow{2}{*}{Array 2} & \multirow{2}{*}{3.55} & \(<\)–10.2, 3.24–13, 9.76 & \multirow{2}{*}{2.51–8.14} & \multirow{2}{*}{1 \(\times\) 2 VNSA array, NTL WPD} & \multirow{2}{*}{88.08 \(\times\) 44.755 \(\times\) 0.813} \\  & & & & & 0.813 \\ \hline \multirow{2}{*}{[7]} & \multirow{2}{*}{2.65} & \(<\)–11.55, & \multirow{2}{*}{10.1–14.81} & \multirow{2}{*}{(2 layers) DSVA with diagonal rectangular-shaped corrugations} & \multirow{2}{*}{150 \(\times\) 100 \(\times\) 0.5} \\  & & & & & \\ \hline \multirow{2}{*}{[9]} & \multirow{2}{*}{4.3} & \(<\)–12.7, 4\(>\)10, 6 & \multirow{2}{*}{\
\begin{tabular}{c} Directivity (3.96–12.54) \\ \end{tabular} } & \multirow{2}{*}{DSVA with loaded ZIM unit cells} & \multirow{2}{*}{90 \(\times\) 85 \(\times\) 1} \\  & & & & \\ \hline \multirow{2}{*}{[26]} & \multirow{2}{*}{2.55} & \(<\)–10, 1.28–11.51, & \multirow{2}{*}{0.5–4} & \multirow{2}{*}{
\begin{tabular}{c} \(1\times\) 16 VTSA array circularly connected, \\ T-junction PD \\ \end{tabular} } & \multirow{2}{*}{\(\pi\times\) 88\({}^{2}\times\) 1} \\  & & & & \\ \hline \multirow{2}{*}{[11]} & \multirow{2}{*}{2.65} & \(<\)–10, 2.4–12, 9.6 & \multirow{2}{*}{0.7–14.2} & \multirow{2}{*}{DSVA with ZIM unit cells and pair of DS} & \multirow{2}{*}{130 \(\times\) 80 \(\times\) 1} \\  & & & & \\ \hline \multirow{2}{*}{[20]} & \multirow{2}{*}{10.2} & \(<\)–10, 3.4–8.3, 4.9 & \multirow{2}{*}{6.25–12.3} & \multirow{2}{*}{\(\mathrm{H}\)-plane 1 \(\times\) 4 VTSA, MSM PD} & \multirow{2}{*}{NA} \\  & & & & \\ \hline \multirow{2}{*}{[12]} & \multirow{2}{*}{3.55} & \(<\)–9.84, 4.2–11, 6.8 & \multirow{2}{*}{8–13.3 (Sim)} & \multirow{2}{*}{DSVA with director and corrugated slots} & \multirow{2}{*}{130 \(\times\) 80 \(\times\) 0.813} \\  & & & & \\ \hline \multirow{2}{*}{[18]} & \multirow{2}{*}{4.3} & \(<\)–10, 2.5–6.8, 4.3 & \multirow{2}{*}{6.5–14.12} & \multirow{2}{*}{1 \(\times\) 6 VTSA array, T-junction PD} & \multirow{2}{*}{167.48 \(\times\) 158.25 \(\times\) 0.6} \\  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Other related VTSA arrays and DSVA at different wide and ultra-wide frequency bands in the literature. Figure 14: (a) Measurement setup and (b) simulated and measured group delay of the proposed arrays.
## 2 Methodology
### Refraction Event  
The head wave is generated when the velocity of the refraction medium (\(V_{2}\)) is larger than that of the incidence medium and the incidence angle is equal to the critical angle \(\theta_{c}\). Here, the transmitted wave travels just below and parallel to the interface. To find the velocity of the refraction medium, the following time-distance equation is used:  
\[T_{h}=\frac{X}{V_{2}}+\frac{2H\cos\theta_{c}}{V_{1}}, \tag{2}\]  
where \(T_{h}\) is the arrival traveltime of the head wave at offset \(X\) and \(H\) is the thickness of the uppermost layer. Equation (2) describes a straight line relating \(T_{h}\) and \(X\) with an intercept of \(2H\cos\theta_{c}/V_{1}\) and a slope of \(1/V_{2}\). The refraction velocity in this case can be calculated by finding the inverse of the slope of the best-fit line to the \(T_{h}-X\) plot of the head wave. In general, when a wave encounters an interface between two media, [PERSON]'s law is used to relate incident and transmitted angles [32]:  
\[\frac{\sin\theta_{1}}{V_{1}}=\frac{\sin\theta_{2}}{V_{2}}, \tag{3}\]where \(\theta_{1}\) and \(\theta_{2}\) are the ray incident and transmitted angles in the upper (\(V_{1}\)) and lower (\(V_{2}\)) media, respectively. The critical angle \(\theta_{c}\) takes place when \(\theta_{2}=90^{\text{o}}\), which can be calculated as:  
\[\theta_{c}=\sin^{-1}\left(\frac{V_{1}}{V_{2}}\right), \tag{4}\]  
In the case of a dipping refractor, the critical angle \(\theta_{c}\) can be calculated from the apparent refractor velocities in the up-dip (\(V_{2u}\)) and down-dip (\(V_{2d}\)) directions using Equation (5) [32]:  
\[\theta_{c}=\frac{1}{2}\left[\sin^{-1}\left(\frac{V_{1}}{V_{2d}}\right)+\sin^{ -1}\left(\frac{V_{1}}{V_{2u}}\right)\right], \tag{5}\]  
The apparent refractor velocities can be determined by picking arrival travel times from shots in the up-dip and down-dip sides. Once \(\theta_{c}\) is calculated, the refractor medium velocity \(V_{2}\) can be determined as [32]:  
\[V_{2}=\frac{V_{1}}{\sin\theta_{c}}, \tag{6}\]
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 6 Evaluation
### Results and Discussion
#### 6.2.2 Performance Results of PQC Algorithm Encryption and Decryption
BIKE-L5 showed a slight increase in decryption time as the file size increased, with average decryption times of 23.17 ms (1 KB), 27.70 ms (10 KB), and 27.85 ms (100 KB), as shown in Figure 5a. Figure 5b shows that HQC followed a similar pattern to BIKE, ranging from an average decryption time of 2.82 ms to 26.18 ms for 100 KB. HQC-128 required an average decryption time of 2.82 ms for 100 KB files, whereas HQC-192 required 9.38 ms, which increased sharply to 26.18 ms for HQC-256 when decrypting the same file size. In contrast, Figure 5c shows that the CRYSTALS-Kyber algorithms maintained consistently low decryption times for the same file size (i.e., 100 KB), with K512 requiring an average decryption time of 0.12 ms and K1024 requiring 0.16 ms. Notably, K768's decryption time decreased slightly to 0.11 ms compared to K512 and K1024, deviating from the upward trend observed in BIKE and HQC. Smaller file sizes (1 KB) yielded minimal average  
Figure 4: Average encryption times: (a) BIKE. (b) HQC. (c) CRYSTALS-Kyber. decryption times for all algorithms, with K512 and K768 requiring 0.09 ms. These results reaffirm the computational efficiency of lattice-based algorithms like CRYSTALS-Kyber for both encryption and decryption, particularly when handling larger file sizes, compared to code-based (BIKE) and hash-based (HQC) alternatives.
## 2 Materials and Methods
### Statistics  
All data are presented as means \(\pm\) standard deviations, absolute and relative frequencies. According to the tests on normal distribution (Kolmogorov-Smirnov), univariate differences among metric data (age, body height and weight, ski length relativized to body height, waist width, as well as sidecut radius and standing heights of the front and rear components of the ski binding as well as standing height ratio) between skiers without and with falls were evaluated either by independent t-tests or Mann-Whitney-U tests. In addition, differences in frequency (sex, fitness level, skiing skill level, risk taking behavior, preferred skiing speed, snow condition, weather condition, preferred slope difficulty) were evaluated by Chi-square tests. According to types of data, Cohen's d (small: 0.2, medium: 0.5, large: 0.8) and Phi coefficient (small: 0.1, medium: 0.3, large: 0.5) were calculated as effect sizes [20].  
All significant factors (\(p<0.05\)) of the univariate comparisons were additionally entered in a logistic regression analysis to estimate adjusted odds ratios (OR) and their 95% confidence intervals (CI) for risk of falls. We provided for an easier understanding percentage of increasing or decreasing risk of falling which are the respective difference between the result of the OR and 1, e.g., an OR of 0.40 corresponds to a decrease in the risk of falling by 60%. In addition, significant factors of the simple analysis with more than two categories (fitness level, preferred skiing speed, snow condition, weather condition, preferred slope difficulty) were dummy coded (no = 1 and yes = 2) before entering themultiple logistic regression, e.g., easy ski slope (yes/no) vs. moderate ski slope (yes/no) vs. hard ski slope (yes/no), etc. SPSS 26.0 (IBM Corporation, Armonk, NY, USA) was used for the statistical analysis. All \(p\)-values were two-tailed and statistical differences were considered significant at \(p<0.05\).
## 2 Materials and Methods
### The Architecture of the System and Ball Detection
The ball detection algorithm is based on the YOLOv5 model and TensorRT 7 [23; 24]. To achieve the recognition and tracking of table tennis movements, our project utilizes a deep learning approach based on the YOLOv5 model. The overall process is presented in Figure 2. This method extracts features and performs classification on real-time table tennis images captured by stereo cameras, enabling object recognition and tracking in a 3D environment [25]. Compared to traditional image processing algorithms and motion-based models, deep learning methods demonstrate superior generalization and anti-interference capabilities [26]. Compared to conventional object detection algorithms, YOLOv5 employs a lightweight neural network structure that allows for rapid object detection on resource-constrained edge devices while maintaining high accuracy [26]. Therefore, as the YOLOv5s model is lightweight, easy to deploy, and efficient in recognition, our project employs it for table tennis object recognition and detection. The importance of capturing frame  
Figure 2: Technological workflow for real-time estimation and prediction of table tennis trajectories. rates for reconstructing the trajectories of fast-moving objects is self-evident. Due to the significant displacement and changes exhibited by fast-moving objects within short time intervals, a low frame rate fails to accurately capture the motion trajectory, resulting in inaccurate trajectory reconstruction. On platforms with limited computational resources, the inference speed of the YOLOv5s model used for table tennis object detection and recognition is insufficient to meet the requirements of capturing the fast movements of the table tennis ball. Hence, optimization of the original model is necessary to further improve the inference speed. In the optimization of the YOLOv5 network, we employ a pre-trained YOLOv5 model that is transformed and compressed using TensorRT. This process converts the model structure into a more suitable format for GPU computing, thereby enhancing the inference speed. Similar works have also evaluated the efficiency on other objects [27; 28]. In the overall workflow of GPU utilization and engine execution, the process begins with reading the input image and performing preprocessing on it. The preprocessed image is then passed to the TensorRT inference engine, initiating the inference process. The inference engine performs forward computation on the input image using the model, generating object detection results. Subsequently, post-processing is applied to the detection results, yielding the final detection outcomes.
###### Abstract  
This study examined the impact of three pre-season fitness training protocols with different training frequencies (heavy, moderate and light) on the physical abilities and the biochemical indicators of muscle damage and oxidative stress in female basketball athletes as expressed through physiological and biochemical assessments. Also, a comparison and a search for correlations were made between the physiological and biochemical changes that were caused by each protocol. The sample consisted of 28 professional female basketball athletes, who competed in three different teams, and each training protocol was assigned to each of the three teams. The somatometric and performance markers measured were as follows: weight, fat percentage, aerobic capacity, anaerobic capacity, vertical jumping ability, speed, acceleration, explosiveness and maximum lower body part strength. The biochemical measurements consisted of exercise-induced muscle damage (creatine kinase--CK, lactate dehydrogenase--LDH) and oxidative stress markers (protein carbonyls--PCs, glutathione peroxidase--GPx). The three fitness training protocols that were used improved the fitness status of high-level female basketball athletes after 6 weeks during the pre-season period, but they were also accompanied by some significant differences between them (fat percentage, anaerobic capacity, speed and acceleration). The results were also accompanied by an increase in CK and LDH for the three teams and by an increase in GPx for Teams 1 and 3. Also, we concluded that even 4 more or 4 less fitness training units (TUs) during a 6-week basketball preparation period can be considered important to differentiate the outcomes of physiological and biochemical markers. The purpose of this study was to provide more theoretical and practical knowledge to basketball coaches and trainers, so they can optimize the training process during the preparation period and thus maximize the performance of their athletes. Nevertheless, the present study was created with certain limitations, such as the small sample size for each team, the absence of a long-term follow-up or the lack of a control group. Future studies may need to turn their focus on fitness protocols with longer durations, try to differentiate the types of training protocols and attempt to achieve a better balance between maximizing performance and mitigating the muscle damage and oxidative stress levels.  
basketball; pre-season training; physiological assessment; biochemical evaluation; muscle damage; oxidative stress; functional training; psychometric training +
Footnote †: journal: Applied Sciences  
Article
## 3 Tests on the Ferry in Real Maritime Conditions  
The tests were carried out in the winter season in February. They took place on a real facility--the \"Wolin\" ferry traveling from Swinoujscic (Poland) to Trelleborg (Sweden) according to its daily schedule. During the cruise, despite the fact that it lasted about 7 h, the convenient window for the test was very narrow due to, e.g., leaving and arriving at port, passing wind farms or forbidden zones, bad weather, and dusk (when flight tests should not be carried out). During the window of possible tests, additional calibrations or parameter changes often had to be performed, which took up valuable time. Finally, on a given day, six flights of different durations and altitudes were conducted. The hexarotor in flight above the ferry deck in real sea conditions is shown in Figure 10. The ambient conditions during the aerial mission were as follows:  
* Average temperature: 9 \({}^{\circ}\)C;
* Relative wind speed: 11-18 m/s;
* Gusts of wind: up to 22 m/s;
* Cloudiness: 80%;
* Humidity: 74%.  
In Figure 10, the ship's helipad deck (Figure 10A), the multirotor in follow-me flight mode above the ship's deck (Figure 10B), the UAV power cord transmitting electricity from the vessel's deck (Figure 10C), the unwinder of the power cord (Figure 10D), the navigation station (Figure 10E), and one of the masts of the local UWB navigation system (Figure 10F) are presented.  
Figure 10: Tethered hexacopter during flight tests.
## 4 Numerical Experiments  
To evaluate the performance of the proposed method, we built a benchmark problem for the mBSSRP. We distributed \(n=10,\ 30,\ 50\) ports uniformly in a \(10\) km\({}^{2}\) region. The number of bicycles at port \(i\), \(b_{i}\) was set to a random number integer between \(-5\) and \(+5\) at intervals of 1. Note that \(\Sigma_{i=1}^{n}b_{i}=0\). The number of vehicles was set to a maximum of three. In applying the CROS-exchange and the Or-opt, if the constraints are satisfied and the number of tours can be decreased, the number of vehicles is decreased such that the relocation of bicycles is performed by one or two vehicles. The capacity of the vehicle was \(q=5\). The moving speed of the vehicle was \(30\) [km/h]. The loading or unloading time of a bicycle was \(\tau=2\) [min]. The time limit \(T\) was \(120\) [min] for \(n=10\) and \(30\) and \(210\) [min] for \(n=50\).