esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
In such a model, the change of natural conditions can be reproduced on demand. It means that the unpredictability of environmental conditions can be emulated and recreated when needed. The impact of weather changes and the human factor led to the invention of the feed-forward method, which changes its state based on prediction disturbances [25]. In [26], an IoT system was adopted to monitor an HVAC system for a Smart Factory. The system was based on temperature and humidity sensors and the energy meter of the air conditioning. However, Wi-Fi communication ruled out the use of battery power supply for sensors and therefore significantly limited the ability to collect data from multiple rooms with a single access point. In [27], the architecture of a real-time control system for heating energy management is described, which combines a field-programmable gate array (FPGA) controller and a cloud backend. In [28], a central heating system with feedback from temperature measurements is described. In this paper, we propose a similar solution to one of the aforementioned examples that takes into account data from several sensors and is suitable for larger buildings, such as multi-family buildings, hotels, schools, offices, etc. To simplify the evaluation of an HVAC system, some authors [6] limit measurements to a single room. This minimizes the number of controlled HVAC devices and allows for easily repeatable conditions. Some researchers assert that indoor changes are cyclical and that only user experience feedback should be used to determine the performance of the tested control strategy. If the satisfaction level is unchanged, the energy consumed by the HVAC equipment is used as a metric for evaluation [29]. When people feel discomfort, they override automatic settings and the energy consumption of the HVAC system increases. The satisfaction level could be related to the level of thermal discomfort. It is equal to zero if the temperature is within the limits, but out of the limits its value is equal to the quadratic function of temperature deviations. In the same article, the authors emphasize the importance of considering the relation to the outdoor temperature. The heating degree days (HDD) metric can be used for that purpose [30], optionally in combination with metered energy use [31]. Using only HDD is not sufficient to estimate user comfort [32]. However, it is very useful in combination with information of the energy consumed by an HVAC system and information about indoor temperature. Other performance criteria might be used as optimization goals in different HVAC control strategies, including energy consumption, thermal comfort, HVAC runtime, fossil fuel consumption, peak, cost, and CO\({}_{2}\) emission [20].
## 3 Results and Discussion
### Effect of Different Working Conditions on the Optimum Filling Rate  
#### 3.2.1 The Influence of Different Temperature on the Best Filling Rate  
Since the driving force of the operation of the backplane heat pipe air conditioning system depends on the temperature difference between the air in the evaporation section of the heat pipe and the outdoor condensation side, as well as the height difference between the evaporation section and the condensation section, the temperature condition in the machine room is generally maintained between 20 \({}^{\circ}\)C and 25 \({}^{\circ}\)C; but when the server in the room goes down, power is off or the outdoor weather conditions change dramatically, the temperature may change suddenly. Therefore, it is necessary to study the influence of different temperature conditions on the optimal refrigerant filling rate and heat exchange performance of the system, which is conducive to ensure that the backplane heat pipe air conditioning system is in the optimal operation state during the actual operation process, and the air outlet temperature meets the air supply temperature requirements of the data room. Table 3 shows the five different operating conditions set in the experiment. Under the five operating conditions, the experiments with different filling rates were carried out respectively.  
Figures 4 and 5 show the influence of the cooling capacity of the system filled with different refrigerant filling rates and the air outlet temperature at the evaporator side under different working conditions. It can be seen from the figures below that under different working conditions, the trend of the system changes but the refrigerant filling rate is the same, the range of the optimal refrigerant filling rate is about 65-79%, and the air outlet temperature of the evaporator backplane reaches the lowest, meeting the air supply requirements for the data room server. The experimental results show  
Figure 3: Change of heat exchange with refrigerant filling rate.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Temperature \({}^{\circ}\)C** & **Condition 1** & **Condition 2** & **Condition 3** & **Condition 4** & **Condition 5** \\ \hline Air inlet temperature of backplane & 35 & 30 & 40 & 35 & 35 \\ CDU inlet water temperature & 12 & 12 & 12 & 10 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different temperature conditions.  
that the inlet air temperature of the backplane and the inlet water temperature of the condenser have little effect on the range of the optimal refrigerant filling rate.
# Deep Neural Networks for Spatial-Temporal Cyber-Physical Systems: A Survey
## 4 CPS Application Domains
### Transportation-Based CPS
#### 4.1.1 Traffic Forecasting
To this end, they proposed a _\"graph convolution gated RNN (GCGRNN)\"_ to analyze multistep traffic volume by automatically determining the spatial-temporal dependencies in historical traffic data, where GCGRNN is based on encoder-decoder RNN and a data-driven graph filter. One benefit of their approach is that graph convolution is not dependent on a predefined adjacency matrix. In the case of flight networks, there are some related studies. For example, [PERSON] et al. [62] proposed an approach to carrying out the flight delay forecast. Their designed approach leveraged graph convolutional neural networks (GCN), which capture the insightful information of the airport network. In their study, an adaptive graph convolutional block was embedded in the proposed scheme so that the hidden spatial interactions in an airport network could be exposed. As another example, [PERSON] et al. [63] observed that CNNs, GCNs, and RNNs were the most frequently utilized for extracting spatial-temporal features from traffic networks. They added that dynamic graphs could be more effective at reflecting the spatial-temporal features of the traffic network, but generating graph structures from data can be difficult. Thus, they proposed a long-term traffic flow prediction scheme that relies on GCN-LSTM to extract the spatial-temporal features for carrying out prediction. Furthermore, they developed a network of graph convolutional policies using the principle of reinforcement learning to create dynamic graphs when static ones are lacking because of data sparsity problems. These efforts can be mapped to the cube \(<\)X\({}_{1}\), \(Y_{1}\), \(Z_{1}/Z_{2}/Z_{3}\)\(>\) in Figure 3 and Table 1. It means that, in those efforts, all the representative DNNs (CNNs, RNNs and GNNs) \(Z_{1},Z_{2}\), and \(Z_{3}\) are utilized for traffic prediction (\(Y_{1}\)) in transportation CPS (\(X_{1}\)).
## 2 Background
### Sparse Storage Schemes
#### 2.1.3 Hybrid ELL/COO (HYB)  
The hybrid format was proposed by Bell and Garland to eliminate the issues with ELL [58]. The major disadvantage of ELL was the performance degradation on varying number of non-zeros per row of the matrix. The HYB storage scheme divides the matrix into two parts, a dense part and a sparse  
Figure 1: The dense representation of matrix \(A\).  
Figure 3: The compressed sparse row (CSR) representation of matrix \(A\).  
Figure 2: The coordinate storage (COO) representation of matrix \(A\).  
part. In the dense part, we store the typical number of non-zeros per row in the ELL storage format and the rest of entries belonging to rows with higher non-zeros in the COO format. The size of the ELL matrix needs to be determined normally from the input matrix. We can add a \(k\)th column to the ELL part if at least one third part of the matrix rows contains \(k\) non-zeroes. The remaining non-zeros in the rows are stored using the COO format. Another way to compute \(k\) is to use the frequency of the non-zeros per row as seen in CUSP implementation. The remaining non-zeros in the rows are stored using the COO format. An example of HYB storage for a sparse matrix \(A\) is shown in Figure 4. Hence, this storage technique requires two 2D arrays of size \(m\times k\) for storing the ELLAPCK part, where \(k\) is the number of selected columns, and three separate arrays to store the COO part. If the actual matrix is of size \(m\times n\), then the value array of the COO part would have a dimension of \((n-k)\).
## 3 Antimicrobial Resistance--Mechanisms and Mitigation Strategies
### Target Modification  
Another important mechanism of bacterial resistance prevents the antimicrobial from binding to its target and this is dependent on where in the cell or when during the cell life cycle this target is positioned. For example, for cell-wall-acting agents, Gram-positive germs change their penicillin-binding proteins and, therefore, betalactams no longer recognize the target where they were supposed to bind.  
For drugs that act by inhibiting protein synthesis, i.e., aminoglycos, mutations can occur in the 305 ribosomal binding site used by the drug [11], while for macrolides, streptomycin and liposomes, which normally bind to the 50S ribosomal site, methylation of the 23S ribosomal RNA subunit by methyltransferases leads to resistance [12].  
Antimicrobials that act by inhibiting nucleic acid synthesis can also be affected by target modification, e.g., alteration in the alpha subunit of DNA gyrase (\(gyrA\) gene), mediating quinolone resistance [11]. For drugs such as trimethoprim, which has antimetabolite activity and acts by inhibiting dihydrofolate reductase, thus preventing synthesis of folic acid, resistance can occur through decreased affinity of dihydrofolate reductase [13].
## 3 Preliminaries  
### Notation  
In this paper, vectors and matrices are represented by bold letters, while scalars are represented by normal letters. For a vector \(\mathbf{x}\in\mathbb{R}^{n}\), \(\|\mathbf{x}\|\) is usual Euclidean norm of \(\mathbf{x}\), i.e., \(\|\mathbf{x}\|=\left(\sum_{i=1}^{n}|x_{i}|^{2}\right)^{\frac{1}{2}}\). Accordingly, the matrix norm is induced by Euclidean norm i.e., for a matrix \(\mathbf{A}\in\mathbb{R}^{m\times n}\), \(\|\mathbf{A}\|=\sqrt{\max\{\lambda_{i}(\mathbf{A}^{H}\mathbf{A})\}}\), where \(\mathbf{A}^{H}\) is the conjugate transpose of \(\mathbf{A}\) and \(\lambda_{i}(\mathbf{A})\) is the \(i\)-th eigenvalue of \(\mathbf{A}\). For a square matrix \(\mathbf{B}\in\mathbb{R}^{n\times n}\), the expression \(\mathbf{B}\prec\mathbf{0}\) means \(\mathbf{B}\) is a negative definite matrix, while \(\mathbf{B}\succ\mathbf{0}\) means \(\mathbf{B}\) is a positive definite matrix. The symbol \(\otimes\) denotes the Kronecker product, e.g., for matrix \(\mathbf{C}\in\mathbb{R}^{m\times n},\mathbf{D}\in\mathbb{R}^{p\times q}\),  
\[\mathbf{C}\otimes\mathbf{D}=\left[\begin{array}{ccc}c_{11}\mathbf{D}&\cdots&c_{1n}\mathbf{ D}\\ \vdots&\ddots&\vdots\\ c_{m1}\mathbf{D}&\cdots&c_{mm}\mathbf{D}\end{array}\right]\in\mathbb{R}^{mp\times nq},\]  
where \(c_{ij}\) is the \(ij\)-th element of \(\mathbf{C}\).
## 5 Simulation
### Transmission Delay Evaluation  
Although we can enhance the security of data collection in a WSN by using methods like SPDC, the performance of the WSN is affected because of additional communication and storage overhead. The SPDC algorithm affects the performance of data collection in WSNs. A secure routing mechanism will affect the performance of a WSN in different aspects like transmission delay, energy consumption, load balance, _etc._ In this manuscript, we focus on the impact of the SPDC algorithm on the transmission delay of WSN. We evaluate the performance of the algorithm by several groups of parameters. Table 2 shows the performance of SPDC under different groups of parameters. It can be seen that the overall delay of data transmission is higher when the number of the nodes in the WSN is larger. It is mainly because that the hop count increases when the network scale becomes large. A large hop count results in high transmission delay. We also note that the overall delay decreases as the times of transmission decrease under a fixed group of parameters. It is mainly because that the delay caused by routing path discovery and secure path construction is fixed and independent of the times of transmission. Therefore, the average delay decreases when the times of transmission increase.
# Evaluating the Perceived Quality of Mobile Banking Applications in Croatia: An Empirical Study
## 4 Results
### Pros and Cons of Mobile Banking Applications  
The analysis of responses to two open-ended questionnaire items uncovered that efficiency, ease of use, and usefulness are the most relevant advantages of the most commonly used mobile banking applications in Croatia since aspects of these quality constructs were reported by 46, 26, and 18 respondents, respectively. Only two study participants have singled out the security of personal data as the strength of mobile banking applications. On the other hand, lack of relevant functionalities (15), security concerns (8), issues with responsiveness (7), difficulties in use (4), and various technical problems, including common updates (3), were recognized by clients of mobile banking applications as their biggest shortcomings.
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 4 Experiments and Results
### Measurement Index  
Detection performance quantification adopted the orthogonal metric system:  
\[\mathcal{M}=\{P,R,\text{mAP},\text{FPS}\} \tag{3}\]  
where precision (\(P\)) and recall (\(R\)) follow the definitions in Equations (4) and (5). The frames-per-second (FPS) metric incorporated full pipeline latency measurements, including  
* Tensor preprocessing (normalization + resizing);
* Inference computation;
* Non-maximum suppression (NMS) postprocessing. \[P=\frac{TP}{TP+FP}\times 100\%\] (4) \[R=\frac{TP}{TP+FN}\times 100\%\] (5) The equation for Average Precision (AP) is shown in Equation (6): \[AP=\int_{0}^{1}P(R)\,dR\] (6) The equation for mean Average Precision (mAP) [4] is given in Equation (7): \[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i}\] (7)  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Augmentation Technique** & **Parameters/Probability** \\ \hline Random Horizontal Flip & Probability = 0.5 \\ Mosaic (applied for first 150 epochs) & Probability = 1.0 \\ MixUp [64] (applied after Mosaic disabled) & Probability = 0.15 \\ Random Affine: & \\ - Rotation & Range = \(\pm\)10.0 degrees \\ - Scale & Range = 0.5–1.5 \\ - Translation & Range = \(\pm\)0.1 \(\times\) image size \\ - Shear & Range = \(\pm\)2.0 degrees \\ HSV Color Space Jitter & H = 0.015, S = 0.7, V = 0.4 \\ Copy-Paste [63] & Probability = 0.1 \\ Random Erasing [65] & Probability = 0.2, Area = 0.02–0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data augmentation techniques and example parameters used during training.  
The detection results were divided into True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN). The equations for Average Precision (AP) and mAP are given in Equations (6) and (7), respectively. AP represents the mean accuracy for a specific object class, \(N\) denotes the total number of classes, mAP@50 represents the average AP across all classes at an Intersection over Union (IoU) threshold of 0.5, and mAP@50:95 spans IoU values from 0.5 to 0.95 with a step size of 0.05. Detection Evaluation Metrics:  
Detection results were categorized as TP (correctly identified objects), FP (spurious detections), TN (accurately rejected backgrounds), and FN (missed targets).  
Core metrics are defined as  
\[AP=\int_{0}^{1}p(r)dr\quad\text{(area under precision--recall curve per class)} \tag{8}\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP_{i}\quad\text{(mean over $N$ classes)} \tag{9}\]  
Standard evaluation protocols include  
* mAP@50: IoU threshold fixed at 0.5.
* mAP@50:95: Average over IoU thresholds [0.5, 0.95] with 0.05 increments [4].
## 2 Literature Survey
### Routing Protocols  
Routing protocols are categorized into table driven and on-demand, based on route calculation. Researches indicate that a Mobile _Ad hoc_ NETwork (MANET) has several characteristics: (1) dynamic topologies; (2) bandwidth-constrained links; (3) energy constrained operation; and (4) limited physical security. Therefore the routing protocols for wired networks cannot be directly used for wireless networks [14, 17, 18]. The characteristics (1), (2) and (3) are similar to WVSNs, which is the driving force to design a protocol to apply to this case.  
Proactive (table-driven) protocols are based on periodic exchange of control messages and maintaining routing tables. These protocols maintain complete information about the network topology locally. On the other hand, reactive (on-demand) protocols try to discover a route only on-demand, when it is necessary. These protocols usually take more time to find a route compared to proactive protocols. The following sections briefly introduce three well known protocols applied to MANET: Optimized Link State Routing (OLSR), _Ad hoc_ On-demand Distance Vector (AODV), and Destination Sequenced Distance Vector (DSDV). The delay, throughput, control overhead and packet delivery ratio are the four common measures used for the comparison of the performance of the above protocols [18].
## 4 Results and Discussion
### Effect of Inclination Angle on the Thermal Resistance of the Heat Pipe  
Figure 10 presents the effect of inclination angle on the thermal resistance of the MF-AHPAs. It is obvious that the inclination angle plays a significant role in determining the thermal resistance for all of the heat pipes with different working fluids. For example, the thermal resistance of acetone heat pipes is strongly affected by inclination angles for a given heat load. For example, at a heat load of 50 W, the thermal resistance increased from 0.063 K/W to 0.083 K/W as the inclination angle rose from 10\({}^{\circ}\) to 75\({}^{\circ}\). The thermal resistance could have a reduction of 24% by working on the optimal inclination angle in this case.  
As shown in Figure 10b, U-shaped thermal resistance curves were observed, indicating an existence of a critical heat load to realise the best performance. The critical heat load increased as the inclination angle increased. The optimal inclination angle also depends on the heat load. The optimal inclination angle of the cyclopentane MF-AHPAs is 25\({}^{\circ}\) for heat loads from 0 W to 50 W, while it  
Figure 9: Effect of working fluid on (**a**) thermal resistance, and (**b**) temperature difference of the MF-AHPA at a 45\({}^{\circ}\) inclination angle.  
increased to 75\({}^{\circ}\) with heat loads ranging from 50 W to 160 W. As shown in Figure 10c, the optimal inclination angle of the n-hexane MF-AHPA is 75\({}^{\circ}\) with heat loads larger than 60 W. However, with the heat load between 20-40 W, it was difficult to determine an optimal inclination angle. It appeared to be between 45-75\({}^{\circ}\) due to overlapped error bars. The mechanisms of orientation effect will be explained in the following section.
## 2 Materials and Methods  
### Study Design and Participants  
A total of 45 (15 non-injured and 30 injured) long distance recreational runners participated in the study. Fifteen non-injured, average age of 28 (SD 1.9; range 26-32) and body mass index (BMI) of 24.7 (SD 1.4), and 30 injured, average age of 30 (SD 1.7; range 28-33), and a BMI of 23.6 (SD 1.5), were randomly selected, retrospectively, from a gait and running analysis laboratory database between the years 2015-2019. Inclusion criteria included a diagnosis of at least one of the following running-related injuries: PFPS, ITBS, or MTSS (Table 1). Their referring orthopedic clinician and physical therapists determined diagnoses. The subjects underwent a clinical evaluation by the main investigator (SK).  
Lower limb measures of ROM and muscle-tendon length were performed by a universal standard plastic goniometer, with muscle strength measured by manual muscle testing [28]. No significant abnormal clinical findings were noted on their physical evaluation, such as limited ROM, short muscle tendon, or muscle weakness. Excluded from the study were runners unfamiliar with treadmill running and if pain was provoked during the running evaluation. The study was approved by the Tel Aviv Sourasky Medical Center, Tel Aviv, Israel's Ethics Committee (0055-20-TLV).  
Data were collected from a running analysis gait laboratory equipped with eight infrared motion capturing cameras (Bonita, Vicon Motion Systems, Ltd., Oxford, UK) and a conventional treadmill used for running. The data were recorded according to the Plug-In Gait Model (PGM) [29], using Cardan angles, measuring running deviations of the pelvis and lower extremities with a sampling rate of 200 Hz. Fifteen reflective passive skin markers were placed on the subjects' pelvis and lower limbs according to the PGM protocol. On the pelvis, markers were placed on the anterior superior iliac spine and sacrum, lateral thigh, lateral knee, lateral shank, lateral ankle, heel, and forefoot bilaterally (Figure 1). Participants ran barefoot to rule out any effect of their running shoes and at their own self-selected speed. Six running cycles were chosen for further analysis. Subjects were given up to 7 min to acclimate to running barefoot and to establish a comfortable running speed prior to the data capture for an additional 5 min. They were instructed to report pain and to cease running if pain developed during the running session.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **N = 30** & **Bilateral** & **Left** & **Right** & **Sex (F/M)\_** \\ \hline PFPS & 12 & 4 & 4 & 4 & F = 4; M = 8 \\ ITBS & 10 & - & 5 & 5 & F = 5; M = 5 \\ MTSS & 8 & 8 & - & - & F = 3; M = 5 \\ \hline \hline \end{tabular}  
* Patella femoral pain syndrome (PFPS), liithibial pain syndrome (ITBS), medial tibial stress syndrome (MTSS).  
\end{table}
Table 1: Runners’ injuries.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ABER & average bit error rate \\ AoA & angle of arrival \\ AWGN & additive white Gaussian noise \\ CDF & cumulative distribution function \\ DF & decode-and-forward \\ FoV & field of view \\ FSO & free-space optical \\ OOK & on-off keying \\ OP & outage probability \\ PDF & probability density function \\ RF & radio frequency \\ SC & selection combining \\ SNR & signal-to-noise ratio \\ UAV & unmanned aerial vehicle \\ \end{tabular}